Despite its volatile history, the Islamic Republic of Iran has performed well on social indicators, especially in providing basic services such as health care and education. Iran's fertility decline may have proceeded in two stages, the first beginning in the late 1960s. The Iranian government introduced a family planning program during the 1960s with explicit health and demographic objectives. Between 1967 and 1977, fertility declined-mainly in urban areas-to an average of 4 children per woman. Although the family planning program continued after the 1979 Islamic revolution, it was suspended after war broke out with Iraq in 1980. During the war, the government pursued a pronatalist population policy, including incentives for childbearing. The fertility decline coincided with improvements in primary and secondary education, possibly affecting the rapid decline in adolescent fertility during 1997-2006, especially when compared to other Middle East and North Africa region countries. Today regional disparities in fertility exist with higher fertility in less developed districts. Yet Iran's example shows how good public policy interventions in health (including family planning) and education can reduce fertility and contribute to human development.
The Thai economy runs on a single engine: external demand. The economic roller coaster since the onset of the global financial crisis can be overwhelmingly attributed to fluctuations in the output of three sectors most sensitive to external demand: manufacturing, logistics (transportation and storage), and tourism (hotels and restaurants). As global trade contracted between the fourth quarter of 2008 and first quarter of 2009, Thailand's real gross domestic product (GDP) fell 6.3 percent, before rebounding 6.9 percent through the end of 2009 on a revival in actual and expected external demand. At the end of 2009, real GDP was back to pre-crisis levels, as measured in seasonally adjusted terms. For 2009 as a whole, however, real GDP fell 2.2 percent. The dominance of sectors linked to external demand over Thailand's growth dynamics is not new. Both sets of sectors grew at about the same pace prior to the 1997 financial crisis. However, a structural break took place in the aftermath of the crisis, when sectors linked to external demand grew an average of 6.1 percent between 2001 and 2007 compared to a 4.3 percent growth rate of other sectors. While the sectors linked to external demand are expected to grow below the historical average in the near term due to lower growth in demand from advanced economies, a reversal of the structural change observed since 1998 is unlikely. This will require an acceleration of the growth of the sectors linked to domestic demand. But the constraints that limited the growth of these sectors in the past not only remain but have been compounded in the near term by the escalation of the political conflict. This will ensure that growth rates in sectors linked to domestic demand will also remain below their (already low) historical averages and the dominance of external demand on the economy will continue to increase.
India's economic performance in FY2009/10 shows that the recovery from the slowdown during the global financial crisis is well underway. India's Gross domestic Product (GDP) growth in FY2009/10 has beaten expectations by reaching 7.4 percent compared with 6.7 percent in the previous year. In particular, agricultural sector growth was better than feared with a slightly positive growth rate despite the worst monsoon shortfall in three decades. Strong growth in the fourth quarter pushed annual GDP growth to 7.4 percent in 2009-10. Fourth quarter growth reached 8.6 percent (y-o-y), the highest quarterly growth rate since the end of FY2007/08. The industrial sector's robust recovery beat expectations. Growth in the last quarter of fiscal year FY2009/10 was an unexpectedly high 13.3 percent resulting in over 12 percent growth in the second half of year, nearly double the 6 percent growth witnessed in the first half. Higher inflation mars the bright picture, but there are clear indications of moderation. Inflation as measured by the wholesale price index (WPI) averaged 10 percent during February-May 2010. India's recovery after the slowdown seems well underway. Growth is projected to climb to 8-9 percent in the next two years. These growth rates are achievable without a renewed build-up of inflationary pressure as long as agricultural growth returns to trend, infrastructure constraints are alleviated, and international prices remain stable. Over the next year, sources of growth will shift from fiscal stimulus to manufacturing and, possibly a recovering agriculture.
In the post-global economic crisis environment, Vietnam's economy continues to grow at a reasonably rapid and stable rate. While the speed of global economic recovery has been uneven across the world, Asia as a region has done particularly well. And within Asia, Vietnam's growth performance continues to be impressive. As shown in left panel of, Vietnam was one of the fastest growing economies in the East Asia and Pacific (EAP) region prior to the global economic crisis and has remained so in the post-crisis period as well1. After registering a real gross domestic product (GDP) growth of 5.3 percent in 2009, Vietnam's economy is expected to grow between 6.5-6.7 percent in 2010. Vietnam, like China, stands out not only for achieving a higher average growth rate but also a more stable growth path. This however has meant that the speed with which the Vietnam's economy is bouncing back from the lows of 2009 appears to be less impressive than countries that experienced negative growth last year. This edition of 'Taking Stock' a semi-annual publication from the World Bank attempts to understand the recent macroeconomic changes in Vietnam. It documents changes to the macroeconomic outcomes and policies with a view to inform policy discussions in the country. The analysis is mostly retrospective in nature, though discussions on prospective challenges and outlook are also briefly mentioned. Developments in the global economy in general and in the EAP region in particular are juxtaposed against Vietnam's own economic outcomes and policies to provide a more complete and nuanced picture of the issues.
Real Gross Domestic product (GDP) in Thailand is projected to grow at 5.0 percent in 2008, driven by recovery in domestic demand. The key reason for the strengthened growth this year is the higher confidence of both consumers and investors with the return of democracy and the election of a new government late in 2007. Last year's better than-expected growth of 4.8 percent was due to buoyant export performance throughout the year even as domestic consumption and investment declined amidst the uncertain political environment and sudden shifts in policy. But this year, the opposite is likely. The external current account may weaken slightly in 2008, as the global downturn slows exports and robust domestic demand stimulates imports. Private investment should recover after its slump last year. Recovery in private consumption and investment could be fragile as there remain large down side risks to their growth, but could be mitigated by additional fiscal stimulus. In addition to the short-term measures have been introduced by the government to mitigate risks this year and next, longer term measures are needed to sustain Thailand's growth and poverty alleviation.
In this paper, authors first review the literature on the relation between finance and growth. Theory provides ambiguous predictions concerning the question of whether financial development exerts a positive, causative impact on long-run economic growth. The second part of this paper reviews the literature on the historical and policy determinants of financial development. Governments play a central role in shaping the operation of financial systems and the degree to which large segments of the financial system have access to financial services. The authors discuss the relationship between financial sector policies and economic development. The remainder of the paper proceeds as follows. Sections one and two review theory and evidence on the relation between finance and growth. Section three turns to an examination of financial sector policies, and section four concludes.
Economists seeking explanations for the global financial crisis of 1997-99 are reaching consensus that a major factor was weak financial institutions, which resulted in part from inadequate government regulations. At the same time many developing countries are struggling with an overregulated financial system-one that stifles innovation and the flow of credit to new entrepreneurs and that can stunt the growth of well-established firms. In particular, too many countries are relying excessively on capital adequacy standards, which are inefficient and sometimes counterproductive. The author argues that financial systems can be reformed successfully using a 'dynamic portfolio approach' aimed at managing the incentives and constraints that affect not only financial institutions exposure to risk but also their ability to cope with it. The article sets out general principles of financial regulation and shows how the dynamic portfolio approach can help countries deal with the special problems that arise during the transition to a more liberalized economy as well as those that arise in dealing with a financial crisis similar to the 1997 crisis in East Asia.
Breast cancer (BC) risk for BRCA1 and BRCA2 mutation carriers varies by genetic and familial factors. About 50 common variants have been shown to modify BC risk for mutation carriers. All but three, were identified in general population studies. Other mutation carrier-specific susceptibility variants may exist but studies of mutation carriers have so far been underpowered. We conduct a novel case-only genome-wide association study comparing genotype frequencies between 60,212 general population BC cases and 13,007 cases with BRCA1 or BRCA2 mutations. We identify robust novel associations for 2 variants with BC for BRCA1 and 3 for BRCA2 mutation carriers, P < 10-8, at 5 loci, which are not associated with risk in the general population. They include rs60882887 at 11p11.2 where MADD, SP11 and EIF1, genes previously implicated in BC biology, are predicted as potential targets. These findings will contribute towards customising BC polygenic risk scores for BRCA1 and BRCA2 mutation carriers. ; BCAC acknowledgements. We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. ABCFS thank Maggie Angelakos, Judi Maskiell, Gillian Dite. ABCS thanks the Blood bank Sanquin, The Netherlands. ABCTB Investigators: Christine Clarke, Deborah Marsh, Rodney Scott, Robert Baxter, Desmond Yip, Jane Carpenter, Alison Davis, Nirmala Pathmanathan, Peter Simpson, J. Dinny Graham, Mythily Sachchithananthan. Samples are made available to researchers on a non-exclusive basis. BBCS thanks Eileen Williams, Elaine Ryder-Mills, Kara Sargus. BCEES thanks Allyson Thomson, Christobel Saunders, Terry Slevin, BreastScreen Western Australia, Elizabeth Wylie, Rachel Lloyd. The BCINIS study would not have been possible without the contributions of Dr. K. Landsman, Dr. N. Gronich, Dr. A. Flugelman, Dr. W. Saliba, Dr. E. Liani, Dr. I. Cohen, Dr. S. Kalet, Dr. V. Friedman, Dr. O. Barnet of the NICCC in Haifa, and all the contributing family medicine, surgery, pathology and oncology teams in all medical institutes in Northern Israel. The BREOGAN study would not have been possible without the contributions of the following: Manuela Gago-Dominguez, Jose Esteban Castelao, Angel Carracedo, Victor Munoz Garzon, Alejandro Novo Dominguez, Maria Elena Martinez, Sara Miranda Ponte, Carmen Redondo Marey, Maite Pena Fernandez, Manuel Enguix Castelo, Maria Torres, Manuel Calaza (BREOGAN), Jose Antunez, Maximo Fraga and the staff of the Department of Pathology and Biobank of the University Hospital Complex of Santiago-CHUS, Instituto de Investigacion Sanitaria de Santiago, IDIS, Xerencia de Xestion Integrada de Santiago-SERGAS; Joaquin Gonzalez-Carrero and the staff of the Department of Pathology and Biobank of University Hospital Complex of Vigo, Instituto de Investigacion Biomedica Galicia Sur, SERGAS, Vigo, Spain. BSUCH thanks Peter Bugert, Medical Faculty Mannheim. CBCS thanks study participants, co-investigators, collaborators and staff of the Canadian Breast Cancer Study, and project coordinators Agnes Lai and Celine Morissette. CCGP thanks Styliani Apostolaki, Anna Margiolaki, Georgios Nintos, Maria Perraki, Georgia Saloustrou, Georgia Sevastaki, Konstantinos Pompodakis. CGPS thanks staff and participants of the Copenhagen General Population Study. For the excellent technical assistance: Dorthe Uldall Andersen, Maria Birna Arnadottir, Anne Bank, Dorthe Kjeldgard Hansen. The Danish Cancer Biobank is acknowledged for providing infrastructure for the collection of blood samples for the cases. CNIO-BCS thanks Guillermo Pita, Charo Alonso, Nuria alvarez, Pilar Zamora, Primitiva Menendez, the Human Genotyping-CEGEN Unit (CNIO). The CTS Steering Committee includes Leslie Bernstein, Susan Neuhausen, James Lacey, Sophia Wang, Huiyan Ma, and Jessica Clague DeHart at the Beckman Research Institute of City of Hope, Dennis Deapen, Rich Pinder, and Eunjung Lee at the University of Southern California, Pam Horn-Ross, Peggy Reynolds, Christina Clarke Dur and David Nelson at the Cancer Prevention Institute of California, Hoda Anton-Culver, Argyrios Ziogas, and Hannah Park at the University of California Irvine, and Fred Schumacher at Case Western University. DIETCOMPLYF thanks the patients, nurses and clinical staff involved in the study. The DietCompLyf study was funded by the charity Against Breast Cancer (Registered Charity Number 1121258) and the NCRN. We thank the participants and the investigators of EPIC (European Prospective Investigation into Cancer and Nutrition). ESTHER thanks Hartwig Ziegler, Sonja Wolf, Volker Hermann, Christa Stegmaier, Katja Butterbach. GC-HBOC thanks Stefanie Engert, Heide Hellebrand, Sandra Krober and LIFE - Leipzig Research Centre for Civilization Diseases (Markus Loeffler, Joachim Thiery, Matthias Nuchter, Ronny Baber). The GENICA Network: Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, and University of Tubingen, Germany [HB, Wing-Yee Lo], German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ) Partner Site Tubingen [[HB], gefordert durch die Deutsche Forschungsgemeinschaft (DFG) im Rahmen der Exzellenzstrategie des Bundes und der Lander - EXC 2180 - 390900677 [HB], Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany [YDK, Christian Baisch], Institute of Pathology, University of Bonn, Germany [Hans-Peter Fischer], Molecular Genetics of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany [Ute Hamann], Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany [Thomas Bruning, Beate Pesch, Sylvia Rabstein, Anne Lotz]; and Institute of Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Germany [Volker Harth]. HABCS thanks Michael Bremer. HEBCS thanks Kirsimari Aaltonen, Irja Erkkila. HUBCS thanks Shamil Gantsev. KARMA and SASBAC thank the Swedish Medical Research Counsel. KBCP thanks Eija Myohanen, Helena Kemilainen. kConFab/AOCS wish to thank Heather Thorne, Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics, and the Clinical Follow-Up Study (which has received funding from the NHMRC, the National Breast Cancer Foundation, Cancer Australia, and the National Institute of Health (USA)) for their contributions to this resource, and the many families who contribute to kConFab. LMBC thanks Gilian Peuteman, Thomas Van Brussel, EvyVanderheyden and Kathleen Corthouts. MARIE thanks Petra Seibold, Dieter Flesch-Janys, Judith Heinz, Nadia Obi, Alina Vrieling, Sabine Behrens, Ursula Eilber, Muhabbet Celik, Til Olchers and Stefan Nickels. MBCSG (Milan Breast Cancer Study Group): Mariarosaria Calvello, Davide Bondavalli, Aliana Guerrieri Gonzaga, Monica Marabelli, Irene Feroce, and the personnel of the Cogentech Cancer Genetic Test Laboratory. The MCCS was made possible by the contribution of many people, including the original investigators, the teams that recruited the participants and continue working on follow-up, and the many thousands of Melbourne residents who continue to participate in the study. We thank the coordinators, the research staff and especially the MMHS participants for their continued collaboration on research studies in breast cancer. MSKCC thanks Marina Corines, Lauren Jacobs. MTLGEBCS would like to thank Martine Tranchant (CHU de Quebec - Universite Laval Research Center), Marie-France Valois, Annie Turgeon and Lea Heguy (McGill University Health Center, Royal Victoria Hospital; McGill University) for DNA extraction, sample management and skilful technical assistance. J.S. is Chair holder of the Canada Research Chair in Oncogenetics. NBHS and SBCGS thank study participants and research staff for their contributions and commitment to the studies. For NHS and NHS2 the study protocol was approved by the institutional review boards of the Brigham and Women's Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. We would like to thank the participants and staff of the NHS and NHS2 for their valuable contributions as well as the following state cancer registries for their help: A.L., A.Z., A.R., C.A., C.O., C.T., D.E., F.L., G.A., I.D., I.L., I.N., I.A., K.Y., L.A., M.E., M.D., M.A., M.I., N.E., N.H., N.J., N.Y., N.C., N.D., O.H., O.K., O.R., P.A., R.I., S.C., T.N., T.X., V.A., W.A., and W.Y. The authors assume full responsibility for analyses and interpretation of these data. OFBCR thanks Teresa Selander, Nayana Weerasooriya. ORIGO thanks E. Krol-Warmerdam, and J. Blom for patient accrual, administering questionnaires, and managing clinical information. PBCS thanks Louise Brinton, Mark Sherman, Neonila Szeszenia-Dabrowska, Beata Peplonska, Witold Zatonski, Pei Chao, Michael Stagner. The ethical approval for the POSH study is MREC /00/6/69, UKCRN ID: 1137. We thank staff in the Experimental Cancer Medicine Centre (ECMC) supported Faculty of Medicine Tissue Bank and the Faculty of Medicine DNA Banking resource. RBCS thanks Jannet Blom, Saskia Pelders, Annette Heemskerk and the Erasmus MC Family Cancer Clinic. We thank the SEARCH and EPIC teams. SKKDKFZS thanks all study participants, clinicians, family doctors, researchers and technicians for their contributions and commitment to this study. SZBCS thanks Ewa Putresza. UCIBCS thanks Irene Masunaka. UKBGS thanks Breast Cancer Now and the Institute of Cancer Research for support and funding of the Breakthrough Generations Study, and the study participants, study staff, and the doctors, nurses and other health care providers and health information sources who have contributed to the study. We acknowledge NHS funding to the Royal Marsden/ICR NIHR Biomedical Research Centre. We acknowledge funding to the Manchester NIHR Biomedical Research Centre (IS-BRC-1215-20007). The authors thank the WHI investigators and staff for their dedication and the study participants for making the program possible. CIMBA acknowledgments. All the families and clinicians who contribute to the studies; Catherine M. Phelan for her contribution to CIMBA until she passed away on 22 September 2017; Sue Healey, in particular taking on the task of mutation classification with the late Olga Sinilnikova; Maggie Angelakos, Judi Maskiell, Gillian Dite, Helen Tsimiklis; members and participants in the New York site of the Breast Cancer Family Registry; members and participants in the Ontario Familial Breast Cancer Registry; Vilius Rudaitis and Laimonas Grikeviius; Drs Janis Eglitis, Anna Krilova and Aivars Stengrevics; Yuan Chun Ding and Linda Steele for their work in participant enrollment and biospecimen and data management; Bent Ejlertsen and Anne-Marie Gerdes for the recruitment and genetic counseling of participants; Alicia Barroso, Rosario Alonso and Guillermo Pita; all the individuals and the researchers who took part in CONSIT TEAM (Consorzio Italiano Tumori Ereditari Alla Mammella), in particular: Bernard Peissel, Dario Zimbalatti, Daniela Zaffaroni, Alessandra Viel, Giuseppe Giannini Liliana Varesco, Viviana Gismondi, Maria Grazia Tibiletti, Daniela Furlan, Antonella Savarese, Aline Martayan, Stefania Tommasi, Brunella Pilato and the personnel of the Cogentech Cancer Genetic Test Laboratory, Milan, Italy. Ms. JoEllen Weaver and Dr. Betsy Bove; FPGMX: members of the Cancer Genetics group (IDIS): Marta Santamarina, Miguel Aguado and Olivia Rios; IFE - Leipzig Research Centre for Civilization Diseases (Markus Loeffler, Joachim Thiery, Matthias Nuchter, Ronny Baber); We thank all participants, clinicians, family doctors, researchers, and technicians for their contributions and commitment to the DKFZ study and the collaborating groups in Lahore, Pakistan (Noor Muhammad, Sidra Gull, Seerat Bajwa, Faiz Ali Khan, Humaira Naeemi, Saima Faisal, Asif Loya, Mohammed Aasim Yusuf) and Bogota, Colombia (Ignacio Briceno, Fabian Gil). Genetic Modifiers of Cancer Risk in BRCA1/2 Mutation Carriers (GEMO) study is a study from the National Cancer Genetics Network UNICANCER Genetic Group, France. We wish to pay a tribute to Olga M. Sinilnikova, who with Dominique Stoppa-Lyonnet initiated and coordinated GEMO until she sadly passed away on the 30th June 2014. The team in Lyon (Olga Sinilnikova, Melanie Leone, Laure Barjhoux, Carole Verny-Pierre, Sylvie Mazoyer, Francesca Damiola, Valerie Sornin) managed the GEMO samples until the biological resource centre was transferred to Paris in December 2015 (Noura Mebirouk, Fabienne Lesueur, Dominique Stoppa-Lyonnet). We want to thank all the GEMO collaborating groups for their contribution to this study: Coordinating Centre, Service de Genetique, Institut Curie, Paris, France: Muriel Belotti, Ophelie Bertrand, Anne-Marie Birot, Bruno Buecher, Sandrine Caputo, Anais Dupre, Emmanuelle Fourme, Marion Gauthier-Villars, Lisa Golmard, Claude Houdayer, Marine Le Mentec, Virginie Moncoutier, Antoine de Pauw, Claire Saule, Dominique Stoppa-Lyonnet, and Inserm U900, Institut Curie, Paris, France: Fabienne Lesueur, Noura Mebirouk. Contributing Centres: Unite Mixte de Genetique Constitutionnelle des Cancers Frequents, Hospices Civils de Lyon - Centre Leon Berard, Lyon, France: Nadia Boutry-Kryza, Alain Calender, Sophie Giraud, Melanie Leone. Institut Gustave Roussy, Villejuif, France: Brigitte Bressac-de-Paillerets, Olivier Caron, Marine Guillaud-Bataille. Centre Jean Perrin, Clermont-Ferrand, France: Yves-Jean Bignon, Nancy Uhrhammer. Centre Leon Berard, Lyon, France: Valerie Bonadona, Christine Lasset. Centre Francois Baclesse, Caen, France: Pascaline Berthet, Laurent Castera, Dominique Vaur. Institut Paoli Calmettes, Marseille, France: Violaine Bourdon, Catherine Nogues, Tetsuro Noguchi, Cornel Popovici, Audrey Remenieras, Hagay Sobol. CHU Arnaud-de-Villeneuve, Montpellier, France: Isabelle Coupier, Pascal Pujol. Centre Oscar Lambret, Lille, France: Claude Adenis, Aurelie Dumont, Francoise Revillion. Centre Paul Strauss, Strasbourg, France: Daniele Muller. Institut Bergonie, Bordeaux, France: Emmanuelle Barouk-Simonet, Francoise Bonnet, Virginie Bubien, Michel Longy, Nicolas Sevenet, Institut Claudius Regaud, Toulouse, France: Laurence Gladieff, Rosine Guimbaud, Viviane Feillel, Christine Toulas. CHU Grenoble, France: Helene Dreyfus, Christine Dominique Leroux, Magalie Peysselon, Rebischung. CHU Dijon, France: Amandine Baurand, Geoffrey Bertolone, Fanny Coron, Laurence Faivre, Caroline Jacquot, Sarab Lizard. CHU St-Etienne, France: Caroline Kientz, Marine Lebrun, Fabienne Prieur. Hotel Dieu Centre Hospitalier, Chambery, France: Sandra Fert Ferrer. Centre Antoine Lacassagne, Nice, France: Veronique Mari. CHU Limoges, France: Laurence Venat-Bouvet. CHU Nantes, France: Stephane Bezieau, Capucine Delnatte. CHU Bretonneau, Tours and Centre Hospitalier de Bourges France: Isabelle Mortemousque. Groupe Hospitalier Pitie-Salpetriere, Paris, France: Chrystelle Colas, Florence Coulet, Florent Soubrier, Mathilde Warcoin. CHU Vandoeuvre-les-Nancy, France: Myriam Bronner, Johanna Sokolowska. CHU Besancon, France: Marie-Agnes Collonge-Rame, Alexandre Damette. CHU Poitiers, Centre Hospitalier d'Angouleme and Centre Hospitalier de Niort, France: Paul Gesta. Centre Hospitalier de La Rochelle: Hakima Lallaoui. CHU Nimes Caremeau, France: Jean Chiesa. CHI Poissy, France: Denise Molina-Gomes. CHU Angers, France: Olivier Ingster; Ilse Coene en Brecht Crombez; Ilse Coene and Brecht Crombez; Alicia Tosar and Paula Diaque; Drs.Sofia Khan, Taru A. Muranen, Carl Blomqvist, Irja Erkkila and Virpi Palola; The Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON) consists of the following Collaborating Centers: Coordinating center: Netherlands Cancer Institute, Amsterdam, NL: M.A. Rookus, F.B.L. Hogervorst, F.E. van Leeuwen, S. Verhoef, M.K. Schmidt, N.S. Russell, D.J. Jenner; Erasmus Medical Center, Rotterdam, NL: J.M. Collee, A.M.W. van den Ouweland, M.J. Hooning, C. Seynaeve, C.H.M. van Deurzen, I.M. Obdeijn; Leiden University Medical Center, NL: C.J. van Asperen, J.T. Wijnen, R.A.E.M. Tollenaar, P. Devilee, T.C.T.E.F. van Cronenburg; Radboud University Nijmegen Medical Center, NL: C.M. Kets, A.R. Mensenkamp; University Medical Center Utrecht, NL: M.G.E.M. Ausems, R.B. van der Luijt, C.C. van der Pol; Amsterdam Medical Center, NL: C.M. Aalfs, T.A.M. van Os; VU University Medical Center, Amsterdam, NL: J.J.P. Gille, Q. Waisfisz, H.E.J. Meijers-Heijboer; University Hospital Maastricht, NL: E.B. Gomez-Garcia, M.J. Blok; University Medical Center Groningen, NL: J.C. Oosterwijk, A.H. van der Hout, M.J. Mourits, G.H. de Bock; The Netherlands Foundation for the detection of hereditary tumours, Leiden, NL: H.F. Vasen; The Netherlands Comprehensive Cancer Organization (IKNL): S. Siesling, J.Verloop; the ICO Hereditary Cancer Program team led by Dr. Gabriel Capella; the ICO Hereditary Cancer Program team led by Dr. Gabriel Capella; Dr Martine Dumont for sample management and skillful assistance; Ana Peixoto, Catarina Santos and Pedro Pinto; members of the Center of Molecular Diagnosis, Oncogenetics Department and Molecular Oncology Research Center of Barretos Cancer Hospital; Heather Thorne, Eveline Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics, and the Clinical Follow-Up Study (which has received funding from the NHMRC, the National Breast Cancer Foundation, Cancer Australia, and the National Institute of Health (USA)) for their contributions to this resource, and the many families who contribute to kConFab; the investigators of the Australia New Zealand NRG Oncology group; members and participants in the Ontario Cancer Genetics Network; Leigha Senter, Kevin Sweet, Caroline Craven, Julia Cooper, Amber Aielts, and Michelle O'Conor; HVH: acknowledgments to the Cellex Foundation for providing research facilities and equipment. Dr Juliette Coignard was supported by a fellowship of INCa Institut National du Cancer N degrees 2015-181, la Ligue Nationale contre le Cancer IP/SC-15229 and Olga Sinilnikova's fellowship (2016). BCAC Funding. BCAC is funded by Cancer Research UK [C1287/A16563, C1287/A10118], the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and by the European Communitys Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministere de l'Economie, Science et Innovation du Quebec through Genome Quebec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. The Australian Breast Cancer Family Study (ABCFS) was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia) and the Victorian Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow. M.C.S. is a NHMRC Senior Research Fellow. The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]. The Australian Breast Cancer Tissue Bank (ABCTB) was supported by the National Health and Medical Research Council of Australia, The Cancer Institute NSW and the National Breast Cancer Foundation. The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. The BBCS is funded by Cancer Research UK and Breast Cancer Now and acknowledges NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). The BCEES was funded by the National Health and Medical Research Council, Australia and the Cancer Council Western Australia and acknowledges funding from the National Breast Cancer Foundation (JS). For the BCFR-NY, BCFR-PA, BCFR-UT this work was supported by grant UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. The BREast Oncology GAlician Network (BREOGAN) is funded by Accion Estrategica de Salud del Instituto de Salud Carlos III FIS PI12/02125/Cofinanciado FEDER; Accion Estrategica de Salud del Instituto de Salud Carlos III FIS Intrasalud (PI13/01136); Programa Grupos Emergentes, Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur. Xerencia de Xestion Integrada de Vigo-SERGAS, Instituto de Salud Carlos III, Spain; Grant 10CSA012E, Conselleria de Industria Programa Sectorial de Investigacion Aplicada, PEME I + D e I + D Suma del Plan Gallego de Investigacion, Desarrollo e Innovacion Tecnologica de la Conselleria de Industria de la Xunta de Galicia, Spain; Grant EC11-192. Fomento de la Investigacion Clinica Independiente, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain; and Grant FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). CBCS is funded by the Canadian Cancer Society (grant # 313404) and the Canadian Institutes of Health Research. CCGP is supported by funding from the University of Crete. The CECILE study was supported by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Agence Nationale de Securite Sanitaire, de l'Alimentation, de l'Environnement et du Travail (ANSES), Agence Nationale de la Recherche (ANR). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council, and Herlev and Gentofte Hospital. The CNIO-BCS was supported by the Instituto de Salud Carlos III, the Red Tematica de Investigacion Cooperativa en Cancer and grants from the Asociacion Espanola Contra el Cancer and the Fondo de Investigacion Sanitario (PI11/00923 and PI12/00070). The CTS was initially supported by the California Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is currently funded through the National Institutes of Health (R01 CA77398, UM1 CA164917, and U01 CA199277). Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. The University of Westminster curates the DietCompLyf database funded by Against Breast Cancer Registered Charity No. 1121258 and the NCRN. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by: Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Generale de l'Education Nationale, Institut National de la Sante et de la Recherche Medicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation, the Stavros Niarchos Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucia, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom). The ESTHER study was supported by a grant from the Baden Wurttemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837, coordinator: Rita K. Schmutzler, Cologne). This work was also funded by the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, 14575/2470). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0 and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany. The GESBC was supported by the Deutsche Krebshilfe e. V. [70492] and the German Cancer Research Center (DKFZ). The HABCS study was supported by the Claudia von Schilling Foundation for Breast Cancer Research, by the Lower Saxonian Cancer Society, and by the Rudolf Bartling Foundation. The HEBCS was financially supported by the Helsinki UniversityHospital Research Fund, the Finnish Cancer Society, and the Sigrid Juselius Foundation. The HUBCS was supported by a grant from the German Federal Ministry of Research and Education (RUS08/017), and by the Russian Foundation for Basic Research and the Federal Agency for Scientific Organizations for support the Bioresource collections and RFBR grants 14-04-97088, 17-29-06014 and 17-44-020498. Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee foundation and Bert von Kantzows foundation. The KARMA study was supported by Marit and Hans Rausings Initiative Against Breast Cancer. The KBCP was financially supported by the special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, and by the strategic funding of the University of Eastern Finland. kConFab is supported by a grant from the National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the United States Army Medical Research and Materiel Command [DAMD17-01-1-0729], Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia, The Cancer Foundation of Western Australia, Cancer Council Tasmania and the National Health and Medical Research Council of Australia (NHMRC; 400413, 400281, 199600). G.C.T. and P.W. are supported by the NHMRC. RB was a Cancer Institute NSW Clinical Research Fellow. LMBC is supported by the 'Stichting tegen Kanker'. The MARIE study was supported by the Deutsche Krebshilfe e.V. [70-2892-BR I, 106332, 108253, 108419, 110826, 110828], the Hamburg Cancer Society, the German Cancer Research Center (DKFZ) and the Federal Ministry of Education and Research (BMBF) Germany [01KH0402]. MBCSG is supported by grants from the Italian Association for Cancer Research (AIRC; IG2014 no.15547) to P. Radice. The MCBCS was supported by the NIH grants CA192393, CA116167, CA176785 an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [CA116201], and the Breast Cancer Research Foundation and a generous gift from the David F. and Margaret T. Grohne Family Foundation. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database. The MEC was support by NIH grants CA63464, CA54281, CA098758, CA132839 and CA164973. The MISS study is supported by funding from ERC-2011-294576 Advanced grant, Swedish Cancer Society, Swedish Research Council, Local hospital funds, Berta Kamprad Foundation, Gunnar Nilsson. The MMHS study was supported by NIH grants CA97396, CA128931, CA116201, CA140286 and CA177150. MSKCC is supported by grants from the Breast Cancer Research Foundation and Robert and Kate Niehaus Clinical Cancer Genetics Initiative. The work of MTLGEBCS was supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research for the CIHR Team in Familial Risks of Breast Cancer program - grant # CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade - grant # PSR-SIIRI-701. The NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The Northern California Breast Cancer Family Registry (NC-BCFR) and Ontario Familial Breast Cancer Registry (OFBCR) were supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The Carolina Breast Cancer Study was funded by Komen Foundation, the National Cancer Institute (P50 CA058223, U54 CA156733, U01 CA179715), and the North Carolina University Cancer Research Fund. The NHS was supported by NIH grants P01 CA87969, UM1 CA186107, and U19 CA148065. The NHS2 was supported by NIH grants UM1 CA176726 and U19 CA148065. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The POSH study is funded by Cancer Research UK (grants C1275/A11699, C1275/C22524, C1275/A19187, C1275/A15956 and Breast Cancer Campaign 2010PR62, 2013PR044. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318. SEARCH is funded by Cancer Research UK [C490/A10124, C490/A16561] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academic Reserve. The Sister Study (SISTER) is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES049033). The Two Sister Study (2SISTER) was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES102245), and, also by a grant from Susan G. Komen for the Cure, grant FAS0703856. SKKDKFZS is supported by the DKFZ. The SMC is funded by the Swedish Cancer Foundation and the Swedish Research Council (VR 2017-00644) grant for the Swedish Infrastructure for Medical Population-based Life-course Environmental Research (SIMPLER). The SZBCS and IHCC were supported by Grant PBZ_KBN_122/P05/2004 and the program of the Minister of Science and Higher Education under the name Regional Initiative of Excellence in 2019-2022 project number 002/RID/2018/19 amount of financing 12 000 000 PLN. The TNBCC was supported by: a Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a grant from the Breast Cancer Research Foundation, a generous gift from the David F. and Margaret T. Grohne Family Foundation. The UCIBCS component of this research was supported by the NIH [CA58860, CA92044] and the Lon V Smith Foundation [LVS39420]. The UKBGS is funded by Breast Cancer Now and the Institute of Cancer Research (ICR), London. The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. CIMBA Funding. CIMBA: The CIMBA data management and data analysis were supported by Cancer Research - UK grants C12292/A20861, C12292/A11174. GCT and ABS are NHMRC Research Fellows. iCOGS: the European Community's Seventh Framework Programme under grant agreement no 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), and the Ministry of Economic Development, Innovation and Export Trade (PSR-SIIRI-701), Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The PERSPECTIVE project was supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministry of Economy, Science and Innovation through Genome Quebec, and The Quebec Breast Cancer Foundation. BCFR: UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. BIDMC: Breast Cancer Research Foundation. CNIO: Spanish Ministry of Health PI16/00440 supported by FEDER funds, the Spanish Ministry of Economy and Competitiveness (MINECO) SAF2014-57680-R and the Spanish Research Network on Rare diseases (CIBERER). COH-CCGCRN: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under grant number R25CA112486, and RC4CA153828 (PI: J. Weitzel) from the National Cancer Institute and the Office of the Director, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. CONSIT TEAM: Funds from Italian citizens who allocated the 5x1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects '5x1000') to S. Manoukian. Associazione Italiana Ricerca sul Cancro (AIRC; IG2015 no.16732) to P. Peterlongo. DEMOKRITOS: European Union (European Social Fund - ESF) and Greek national funds through the Operational Program Education and Lifelong Learning of the National Strategic Reference Framework (NSRF) - Research Funding Program of the General Secretariat for Research & Technology: SYN11_10_19 NBCA. Investing in knowledge society through the European Social Fund. DKFZ: German Cancer Research Center. EMBRACE: Cancer Research UK Grants C1287/A10118 and C1287/A11990. D. Gareth Evans and Fiona Lalloo are supported by an NIHR grant to the Biomedical Research Centre, Manchester. The Investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Ros Eeles and Elizabeth Bancroft are supported by Cancer Research UK Grant C5047/A8385. Ros Eeles is also supported by NIHR support to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. FCCC: A.K.G. was in part funded by the NCI (R01 CA214545), The University of Kansas Cancer Center Support Grant (P30 CA168524), The Kansas Institute for Precision Medicine (P20 GM130423), and the Kansas Bioscience Authority Eminent Scholar Program. A.K.G. is the Chancellors Distinguished Chair in Biomedical Sciences Professorship. A.Vega is supported by the Spanish Health Research Foundation, Instituto de Salud Carlos III (ISCIII), partially supported by FEDER funds through Research Activity Intensification Program (contract grant numbers: INT15/00070, INT16/00154, INT17/00133), and through Centro de Investigacion Biomedica en Red de Enferemdades Raras CIBERER (ACCI 2016: ER17P1AC7112/2018); Autonomous Government of Galicia (Consolidation and structuring program: IN607B), and by the Fundacion Mutua Madrilena (call 2018). GC-HBOC: German Cancer Aid (grant no 110837, Rita K. Schmutzler) and the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, 14575/2470). GEMO: Ligue Nationale Contre le Cancer; the Association Le cancer du sein, parlons-en! Award, the Canadian Institutes of Health Research for the CIHR Team in Familial Risks of Breast Cancer program and the French National Institute of Cancer (INCa grants 2013-1-BCB-01-ICH-1 and SHS-E-SP 18-015). GEORGETOWN: the Non-Therapeutic Subject Registry Shared Resource at Georgetown University (NIH/NCI grant P30-CA051008), the Fisher Center for Hereditary Cancer and Clinical Genomics Research, and Swing Fore the Cure. G-FAST: Bruce Poppe is a senior clinical investigator of FWO. Mattias Van Heetvelde obtained funding from IWT. HCSC: Spanish Ministry of Health PI15/00059, PI16/01292, and CB-161200301 CIBERONC from ISCIII (Spain), partially supported by European Regional Development FEDER funds. HEBCS: Helsinki University Hospital Research Fund, the Finnish Cancer Society and the Sigrid Juselius Foundation. HEBON: the Dutch Cancer Society grants NKI1998-1854, NKI2004-3088, NKI2007-3756, the Netherlands Organization of Scientific Research grant NWO 91109024, the Pink Ribbon grants 110005 and 2014-187.WO76, the BBMRI grant NWO 184.021.007/CP46 and the Transcan grant JTC 2012 Cancer 12-054. HEBON thanks the registration teams of Dutch Cancer Registry (IKNL; S. Siesling, J. Verloop) and the Dutch Pathology database (PALGA; L. Overbeek) for part of the data collection. ICO: The authors would like to particularly acknowledge the support of the Asociacion Espanola Contra el Cancer (AECC), the Instituto de Salud Carlos III (organismo adscrito al Ministerio de Economia y Competitividad) and Fondo Europeo de Desarrollo Regional (FEDER), una manera de hacer Europa (PI10/01422, PI13/00285, PIE13/00022, PI15/00854, PI16/00563 and CIBERONC) and the Institut Catala de la Salut and Autonomous Government of Catalonia (2009SGR290, 2014SGR338 and PERIS Project MedPerCan). INHERIT: Canadian Institutes of Health Research for the CIHR Team in Familial Risks of Breast Cancer program - grant # CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade - grant # PSR-SIIRI-701. IOVHBOCS: Ministero della Salute and 5x1000 Istituto Oncologico Veneto grant. kConFab: The National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. MAYO: NIH grants CA116167, CA192393 and CA176785, an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201),and a grant from the Breast Cancer Research Foundation. MCGILL: Jewish General Hospital Weekend to End Breast Cancer, Quebec Ministry of Economic Development, Innovation and Export Trade. Marc Tischkowitz is supported by the funded by the European Union Seventh Framework Program (2007Y2013)/European Research Council (Grant No. 310018). MSKCC: the Breast Cancer Research Foundation, the Robert and Kate Niehaus Clinical Cancer Genetics Initiative, the Andrew Sabin Research Fund and a Cancer Center Support Grant/Core Grant (P30 CA008748). NCI: the Intramural Research Program of the US National Cancer Institute, NIH, and by support services contracts NO2-CP-11019-50, N02-CP-21013-63 and N02-CP-65504 with Westat, Inc, Rockville, MD. NNPIO: the Russian Foundation for Basic Research (grants 17-00-00171, 18-515-45012 and 19-515-25001). NRG Oncology: U10 CA180868, NRG SDMC grant U10 CA180822, NRG Administrative Office and the NRG Tissue Bank (CA 27469), the NRG Statistical and Data Center (CA 37517) and the Intramural Research Program, NCI. OSUCCG: Ohio State University Comprehensive Cancer Center. PBCS: Italian Association of Cancer Research (AIRC) [IG 2013 N.14477] and Tuscany Institute for Tumours (ITT) grant 2014-2015-2016. SMC: the Israeli Cancer Association. SWE-BRCA: the Swedish Cancer Society. UCHICAGO: NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA125183), R01 CA142996, 1U01CA161032 and by the Ralph and Marion Falk Medical Research Trust, the Entertainment Industry Fund National Women's Cancer Research Alliance and the Breast Cancer research Foundation. UCSF: UCSF Cancer Risk Program and Helen Diller Family Comprehensive Cancer Center. UPENN: Breast Cancer Research Foundation; Susan G. Komen Foundation for the cure, Basser Research Center for BRCA. UPITT/MWH: Hackers for Hope Pittsburgh. VFCTG: Victorian Cancer Agency, Cancer Australia, National Breast Cancer Foundation. WCP: Dr Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124. HVH: Supported by the Carlos III National Health Institute funded by FEDER funds - a way to build Europe - PI16/11363. MT Parsons is supported by a grant from Newcastle University. Kelly-Anne Phillips is an Australian National Breast Cancer Foundation Fellow. ; Sí
El objetivo principal de esta Tesis Doctoral es evaluar el desempeño financiero de las inversiones socialmente responsables (ISR). En las últimas décadas, la gestión de inversiones ha experimentado un proceso progresivo de adaptación en el que los objetivos financieros convencionales se han complementado con atributos no financieros como los criterios medioambientales, sociales y de gobernanza (ESG). Esta tendencia refleja una creciente conciencia sobre cuestiones ambientales, sociales y éticas que influye de manera importante en las decisiones de compra de los inversores (Mollet y Ziegler, 2014). La ISR atrae a inversores que desean ir más allá de la utilidad financiera de sus inversiones y que esperan una utilidad no financiera que refleje sus valores sociales (Auer, 2016; Auer y Schuhmacher, 2016). Los aspectos ESG se están convirtiendo en una parte importante del proceso de toma de decisiones de los inversores al ayudarles a identificar oportunidades y riesgos en el largo plazo. De acuerdo con el Global Sustainable Investment Review de 2016, en 2016 hubo 22,89 billones de dólares gestionados profesionalmente en el marco de estrategias de inversión responsable a nivel mundial, lo que representa un aumento del 25% desde 2014. En 2016, el 53% de los gestores en Europa utilizaron estrategias de inversión responsable, siendo esta proporción del 22% en EE.UU. y del 51% en Australia/Nueva Zelanda. Esta tendencia se ha ratificado para los dos últimos años. Los gestores de activos estadounidenses consideraron criterios ESG en su gestión por valor de 11,6 billones de dólares, un 44 por ciento más que los 8,1 billones de dólares de 2016 (USSIF, 2018). El informe EUROSIF (2018) también revela un crecimiento sostenido en Europa de las estrategias de inversión sostenibles. Los dos últimos años (2016-2018) muestran signos manifiestos de que la ISR se está convirtiendo en parte integrante de la gestión de los fondos europeos. La idea básica de la ISR es aplicar un conjunto de filtros al universo de inversión disponible con el fin de seleccionar o excluir activos en función de criterios ESG (Auer, 2016). En la práctica, existen diferentes estrategias ISR, como la integración, la selección positiva/best-in-class, la selección ética/negativa, la gobernanza, el compromiso, etc., todas ellas con el objetivo de dirigir los fondos hacia empresas socialmente responsables con proyectos y políticas constructivas y sostenibles. Desde la perspectiva de los inversores, la cuestión crítica es si la selección de acciones socialmente responsable conduce a ganancias o pérdidas en términos de rendimiento financiero. Por parte de las empresas, la cuestión es si el gasto de recursos en prácticas de responsabilidad social de las empresas (RSE) redundará en beneficio de la empresa y aumentará su valor. Si hacer el bien (social y medioambiental) está vinculado a hacerlo bien (financieramente), las empresas podrían verse incentivadas a comportarse de manera más sostenible. Una relación positiva entre el desempeño social y el financiero legitimaría incluso la RSE sobre razones económicas (Margolis et al. 2009). El crecimiento de la ISR y sus consecuencias ha estimulado la realización de estudios empíricos evaluando su comportamiento financiero. Una parte importante de la literatura se ha centrado en el rendimiento financiero de los fondos de inversión ISR. En general, estos estudios encuentran que no hay diferencias significativas en el desempeño financiero de fondos ISR y fondos de tipo convencional (Leite et al. 2018)2. Sin embargo, la evaluación del impacto financiero de la ISR mediante el análisis del rendimiento de los fondos de inversión ISR gestionados activamente presenta algunas deficiencias. Por ejemplo, como señalan Brammer et al (2006) y Kempf y Osthoff (2007), existen efectos confusos -como las habilidades de gestión del gestor y los honorarios y tasas por la gestión- que pueden dificultar la identificación del rendimiento de las ISR. Además, la evidencia de Utz y Wimmer (2014), Humphrey et al. (2016), y Statman y Glushkov (2016) sugiere que la etiqueta "socialmente responsable" puede ser una estrategia de marketing de los fondos, lo que suscitaría dudas entre los inversores sobre si un fondo ISR es realmente socialmente responsable. En consecuencia, los inversores pueden tener dificultades para saber en qué medida un fondo ISR tiene realmente en cuenta los criterios sociales en su proceso de selección. Para superar las limitaciones asociadas a los estudios sobre fondos de inversión ISR gestionados activamente, un enfoque alternativo para evaluar los efectos financieros de la ISR consiste en analizar el rendimiento de carteras sintéticas formadas utilizando características sociales, medioambientales y de gobernanza de las empresas. En esta Tesis Doctoral, seguimos este enfoque para evaluar las inversiones socialmente responsables. Esta Tesis Doctoral está organizada en dos secciones. La primera incluye los capítulos 1 y 2 en los que se evalúan algunos aspectos metodológicos relacionados con una medida de rendimiento financiero que se utiliza para evaluar el rendimiento financiero de la ISR en la sección dos. La segunda sección incluye los capítulos 3, 4, 5 y 6 en los que se evalúa el desempeño financiero de la ISR desde diferentes perspectivas. Primera Sección. En el Capítulo 1 se evalúa la utilidad de una estrategia de inversión sectorial basada en el modelo de tres factores de Fama y French (1992). En este capítulo desarrollamos un proceso de inversión, que hasta donde sabemos es nuevo, incluyendo en una cartera acciones que están infravaloradas con respecto a sus índices sectoriales, es decir, tomamos como factor de mercado relevante el índice sectorial al que pertenecen las empresas. Nuestro principal objetivo en este capítulo es comprobar si es posible conseguir de forma consistente una rentabilidad extraordinaria mediante una estrategia sectorial basada en el modelo de Fama y French (1992) para la toma de decisiones de inversión. En el Capítulo 2 se evalúa si el modelo Fama y French (1992) puede convertirse en una herramienta más versátil y flexible, capaz de incorporar las variaciones en las características de las empresas de una forma más dinámica. Específicamente, prestamos atención al procedimiento que siguen Fama y French (1992) para formar los factores de riesgo. Ellos toman datos anuales y evalúan las carteras de valor y tamaño una vez al año, manteniéndolas invariables durante todo el período. Sin embargo, observamos que las características de las empresas pueden variar durante un periodo de 12 meses. Argumentamos que en ese periodo la valoración de una empresa puede cambiar como resultado de, por ejemplo, variaciones en su precio de mercado, su tamaño o su precio en libros; sin embargo el modelo de Fama y French (1992) no refleja con precisión esta dinámica. Nuestro principal objetivo en este capítulo es probar la eficacia del modelo tomando datos mensuales y reformando las carteras de valor y tamaño al final de cada mes para desarrollar una herramienta más dinámica y adaptable. Segunda Sección. En el Capítulo 3 se evalúa el rendimiento financiero de carteras que pueden formar inversores minoristas con conciencia social en comparación con inversiones convencionales. Observamos que la mayoría de los estudios previos que evalúan el rendimiento financiero de la ISR se llevan a cabo desde la perspectiva de las decisiones de inversión de los inversores institucionales y no desde la perspectiva de los inversores particulares que desean mantener carteras ISR. Sin embargo, ha habido un aumento considerable de la popularidad de la ISR entre los inversores minoristas (Benijts, 2010). Nilsson (2015) destaca que los inversores particulares optan por dedicar al menos una parte de sus fondos a inversiones que incluyan algún tipo de preocupación social o medioambiental, convirtiéndose así en un factor importante en la configuración de la ISR. Según el Global Sustainable Investment Review de 2016, aunque el mercado ISR en la mayoría de las regiones está dominado por inversores institucionales profesionales, el interés de los inversores particulares por la ISR está adquiriendo relevancia. De hecho, la proporción relativa de inversiones en ISR al por menor en Canadá, Europa y Estados Unidos aumentó del 13 por ciento en 2014 al 26 por ciento a comienzos de 2016 (GSIA, 2016). El objetivo de este capítulo es evaluar el rendimiento de las carteras que pueden formar los inversores minoristas socialmente responsables en comparación con las inversiones convencionales. Utilizamos varias medidas de rendimiento financiero; entre otras, la desarrollada en el capítulo 2 de esta Tesis Doctoral. Como punto relevante para los inversores minoristas, para la selección de las empresas socialmente responsables acudimos a una fuente de información de acceso libre al público a la que puede acceder cualquier inversor minorista. Adicionalmente, en este capítulo analizamos el impacto que pueden tener diferentes estados del mercado (alcistas y bajistas) sobre el rendimiento financiero de las carteras ISR. Investigaciones recientes muestran que el rendimiento de fondos de renta variable ISR (Nofsinger y Varma, 2014; Becchetti et al., 2015, Leite y Cortez, 2015), fondos de renta fija de ISR (Henke, 2016) y empresas socialmente responsables (Brzeszczyński y McIntosh, 2014; Carvalho y Areal, 2016) son sensibles a diferentes estados del mercado. En el Capítulo 4 evaluamos el desempeño financiero de carteras de acciones construidas con criterios de RSC a nivel internacional. Observamos que los estudios previos que abordan el desempeño de las carteras sintéticas socialmente responsables adolecen de algunas limitaciones e inconsistencias, a saber: (1) la mayoría de los estudios previos se centran en los mercados bursátiles de EE.UU. y Europa; (2) con la excepción de Badía et al. (2017), los estudios anteriores no comparan el desempeño de las carteras de ISR de diferentes regiones del mundo; (3) existen estudios que miden la RSC sólo a través de una de sus dimensiones individuales, mientras que otros consideran medidas agregadas de la RSC; (4) la mayoría de los estudios no evalúan la influencia de la industria en el desempeño financiero de las carteras de acciones ISR; (5) en varios de los estudios que evalúan a empresas europeas, se utilizan muestras de tamaño reducido; (6) falta evidencia actualizada; y (7) algunos investigadores simplemente dividen los períodos de análisis en subperíodos para evaluar el ―efecto de tiempo‖, sin embargo, es posible que se haya descuidado un efecto importante, el impacto de diferentes estados del mercado sobre el rendimiento financiero. Nuestro principal objetivo en este capítulo es evaluar el rendimiento financiero de carteras construidas sobre la base de criterios RSC superando las limitaciones previas. Formamos carteras de acciones con valoraciones de sostenibilidad altas y bajas e investigamos el rendimiento de dichas carteras utilizando modelos multifactoriales. En este capítulo, ampliamos el análisis sobre el impacto de la utilización de filtros socialmente responsables en el rendimiento de las carteras de inversión a otras áreas geográficas (Norteamérica, Europa, Japón y Asia-Pacífico); comparamos el rendimiento financiero de las carteras ISR de estas regiones entre sí; formamos carteras basadas en una medida agregada de RSE, así como en tres de sus dimensiones específicas ESG; evaluamos la influencia de la industria en el rendimiento financiero de las carteras de acciones ISR; y, por último, evaluamos el rendimiento financiero de las carteras de acciones ISR en diferentes estados de los mercados: alcistas, bajistas y períodos de mercados mixtos. En el Capítulo 5 evaluamos el rendimiento financiero de carteras de deuda pública formadas según criterios ESG. Observamos que, aunque el concepto de ISR se relacionó originalmente con la selección de acciones, la proporción de inversores que aplican criterios ISR a bonos ha crecido significativamente en los últimos años. Según el Foro Europeo de Inversión Sostenible (EUROSIF, 2016), la renta variable representaba más del 30% de los activos de ISR en diciembre de 2015, lo que supone un descenso significativo respecto al 50% del año anterior. Por otra parte, se ha producido un fuerte aumento de los bonos, que han pasado del 40% registrado en diciembre de 2013 al 64%. Tanto los bonos corporativos como los bonos gubernamentales experimentaron un crecimiento notable. En este sentido, las implicaciones financieras de los procesos de selección ESG sobre bonos corporativos pueden estar estrechamente relacionadas con la selección de acciones, ya que los bonos corporativos están asociados a empresas. De hecho, estudios previos (por ejemplo, Derwall y Koedijk, 2009; Leite y Cortez, 2016) que evalúan el desempeño financiero de fondos que invierten en bonos de renta fija socialmente responsables, encuentran que en promedio tuvieron un desempeño similar al de los fondos convencionales. Estos resultados están en línea con la mayoría de los estudios empíricos sobre el desempeño de los fondos ISR que muestran que tienden a tener un desempeño similar al de sus pares convencionales (Revelly y Viviani, 2015). Sin embargo, los procesos de selección ESG sobre bonos gubernamentales, dado que no están relacionados con las empresas, pueden ayudar a comprender las consecuencias de la ISR para activos alternativos. A pesar del crecimiento del mercado de deuda pública ISR y del desarrollo de calificaciones de los países basadas en factores ESG en los últimos años, se ha pasado por alto el vínculo entre la rentabilidad de la deuda pública y el rendimiento de los países en términos de preocupaciones ESG. De hecho, hasta donde sabemos, ninguna investigación previa ha evaluado el rendimiento financiero de las inversiones responsables en bonos gubernamentales. El objetivo principal de este capítulo es llenar este vacío. Evaluamos el rendimiento financiero de carteras de deuda pública formadas según criterios ESG. A diferencia de estudios previos, en los que se aplican calificaciones de sostenibilidad de las empresas, se utilizan calificaciones de sostenibilidad relacionadas con los países. En el capítulo 6 se estudia un aspecto poco evaluado de la RSE: la distinción entre inversiones en cuestiones de sostenibilidad materiales e inmateriales. Sólo las empresas que se centran en cuestiones de sostenibilidad material asociadas a sus operaciones principales deberían lograr una ventaja competitiva y obtener un mayor rendimiento social y financiero. Las actividades de RSE y las innovaciones relacionadas deben realizarse sobre aspectos materiales, ya que de lo contrario no se debería espera un efecto positivo en el rendimiento financiero. De hecho, las inversiones en cuestiones inmateriales pueden implicar costes empresariales adicionales sin un rendimiento social y financiero asociado. Para las empresas es importante centrarse en las cuestiones materiales ya que de este modo invierten en aspectos sociales que realmente afectan a sus operaciones. A pesar de que temas como la seguridad de los productos, el cambio climático y la intensidad en el uso de los recursos tienen impactos en varias industrias, como señalan Herz and Rogers (2016), esos efectos a menudo varían en gran medida de una industria a otra. Los riesgos pueden estar en todas partes, aunque también son particulares. Como consecuencia, las empresas de diferentes industrias tienen sus perfiles de sostenibilidad particulares. Es probable que una empresa que invierte sobre temas de sostenibilidad material en su industria logre un desempeño financiero positivo. Mientras tanto, es probable que una empresa que invierte en cuestiones de sostenibilidad materiales pero también inmateriales no logre un rendimiento financiero superior. En este capítulo, el objetivo principal es evaluar el rendimiento financiero de carteras de acciones formadas en función de cuestiones de RSC materiales e inmateriales. Khan et al (2016) muestran que las empresas estadounidenses con un fuerte desempeño en aspectos materiales superan a las empresas con un desempeño pobre en temas materiales. Nuestro conjunto de datos incluye empresas de estadounidenses y Europa. De este modo, ampliamos las pruebas anteriores de Khan et al. (2016) a las empresas europeas. La evaluación de las empresas estadounidenses y europeas es particularmente interesante dada la heterogeneidad de las pautas de desarrollo de la ISR en los distintos países (Neher y Hebb, 2015). En este capítulo utilizamos las puntuaciones de las empresas a partir de un conjunto de datos original que integra los estándares del Mapa de Materialidad SASB que, hasta donde sabemos, no se ha utilizado antes en este contexto. ; The main objective of this Doctoral Thesis is to evaluate the financial performance of socially responsible investments (SRI). In recent decades, investment management has undergone a progressive adaption process in which conventional financial objectives are increasingly being complemented by non-financial attributes such as environment, social and governance (ESG) criteria. This trend reflects an increasing awareness of environmental, social, and ethical issues that is strongly influencing the purchase decisions of investors (Mollet and Ziegler, 2014). SRI appeals to investors who wish to go beyond the financial utility of their investments and also derive non-financial utility from holding securities that reflect their social values (Auer, 2016; Auer and Schuhmacher, 2016). Additionally, ESG issues are becoming an important part of investors' decision-making process by helping them to identify firms' long-term opportunities and risks. According to the 2016 Global Sustainable Investment Review, in 2016 there were $22.89 trillion of assets being professionally managed under responsible investment strategies globally, representing an increase of 25% since 2014. In 2016, 53% of managers in Europe used responsible investment strategies, this proportion being 22% in the US and 51% in Australia/New Zealand. This tendency has been ratified recently for the last two years. US asset managers considered ESG criteria across $11.6 trillion in assets, up 44 percent from $8.1 trillion in 2016 (USSIF, 2018). The EUROSIF (2018) report discloses sustained growth for most sustainable and responsible investment strategies. The past two years (2016-2018) show manifest signs of SRI becoming integral to European fund management. The basic idea of SRI is to apply a set of screens to the available investment universe, in order to select or exclude assets based on ESG criteria (Auer, 2016). In practice, there is a range of SRI strategies, such as integration, positive/best-in-class screening, ethical/negative screening, governance and engagement, etc. All of these aim to drive funds towards socially responsible firms with constructive sustainable projects and policies. From an investors' perspective, the critical issue is whether socially responsible stock selection leads to gains or losses in terms of financial performance. On the firms' side, the question is whether spending resources on corporate social responsibility (CSR) practices will render benefits for the firm and increase its value. If doing good is indeed linked to doing well, firms may be led to behave in a more sustainable way. A positive relationship between social and financial performance would even legitimize CSR on economic grounds (Margolis et al. 2009). The growth of SRI and its consequences has stimulated empirical studies assessing financial behaviours. An important stream of the literature has focused on the financial performance of SRI mutual funds. In general, these studies find that there are no significant differences between the performance of SRI mutual funds and conventional funds (Leite et al. 2018). However, assessing the financial impact of SRI by evaluating the performance of actively managed SRI mutual funds has some shortcomings. For instance, as Brammer et al. (2006), and Kempf and Osthoff (2007) point out, there are confounding effects - such as fund manager skills and management fees - that may make it difficult to identify the performance that is due to the social characteristics of the underlying holdings. Furthermore, the evidence of Utz and Wimmer (2014), Humphrey et al. (2016), and Statman and Glushkov (2016) suggests that the 'socially responsible' label may be more akin to a marketing strategy, thus raising doubts among investors that an SRI fund is really socially responsible. As a consequence, investors may find it difficult to know the extent to which an SRI fund is really considering social criteria in its selection process. To overcome the limitations associated to studies on actively managed SRI mutual funds, an alternative approach to evaluate the financial effects of SRI involves evaluating the performance of synthetic portfolios formed on assets' social characteristics. In this Doctoral Thesis, we follow this approach to evaluating socially responsible investments. This Doctoral Thesis is organized in two sections. The first includes chapters 1 and 2 in which we evaluate some methodological aspects related to a financial performance measure which is used to assess the financial performance of SRI in Section two. The second Section includes Chapters 3, 4, 5, and 6 in which we evaluate the financial performance of SRI from different perspectives. First Section. In Chapter 1 we assess the usefulness of a sector investment strategy based on the three-factor Fama and French (1992) model. We develop an investment process that is, as far as we know, new by including stocks that are undervalued with respect to their sector indices in a portfolio. We take as the relevant market factor the sector index to which firms belong. We base the strategy on the difficulty entailed in effectively choosing the appropriate market portfolio (Roll, 1977).Our main objective in this chapter is to test whether it is possible to consistently achieve extra-financial returns by means of a sector strategy using the Fama and French model (1992) as a basis for decision-making. In Chapter 2 we evaluate whether the Fama and French (1992) model may be adapted to become a more versatile and flexible tool, capable of incorporating variations of firms characteristics in a more dynamic form. We pay attention to the procedure that Fama and French (1992) follow to form the risk factors. They take annual data, and the value and size portfolios are assessed once a year, maintaining invariability during the whole period. However, we note that firms' characteristics can change during any given 12-month period. We argue that, over time, firms' valuation may change as a result of variations in its market price, size or book price, and we are aware that the Fama and French (1992) model does not accurately reflect these dynamics. Our main objective in this chapter is to test the effectiveness of the model by taking month-to-month data and reforming the value and size portfolios at the end of each month, aiming to develop a more dynamic and adaptable tool. Second Section. In Chapter 3 we evaluate the financial performance of portfolios that can be formed by socially conscious retail investors compared to conventional investments. We note that most previous studies evaluating the financial performance of SRI are conducted from the perspective of institutional investors' investment decisions and not from the perspective of retail investors who wish to hold SRI portfolios. Nonetheless, there has been a considerable increase in the popularity of SRI among retail investors (Benijts, 2010). Nilsson (2015) highlights that retail investors choose to devote at least part of their funds to investments that include some kind of social or environmental concerns, thereby having become an important factor in shaping SRI. According to the 2016 Global Sustainable Investment Review, although the SRI market in most of the regions is dominated by professional institutional investors, retail investors' interest in SRI is gaining relevance. Indeed, the relative proportion of retail SRI investments in Canada, Europe and the United States increased from 13 percent in 2014 to 26 percent at the start of 2016 (GSIA, 2016). Furthermore, over one third of SRI assets in the United States come from retail investors. The objective of this chapter is to assess the performance of portfolios that can be formed by socially responsible retail investors compared to conventional investments. We use several financial performance measures. Among others, that developed in chapter 2 of this Doctoral Thesis. As a relevant point to retail investors, we use stocks listed on a source freely available to the public that any retail investor may access. Additionally, we analyse the impact of different market states on the financial performance of SRI portfolios. Recent research shows that the performance of SRI equity funds (Nofsinger and Varma, 2014; Becchetti et al., 2015, Leite and Cortez, 2015), SRI fixed-income funds (Henke, 2016), and socially responsible stocks (Brzeszczyński and McIntosh, 2014; Carvalho and Areal, 2016) is sensitive to different market states (e.g., expansion and recession periods). In Chapter 4 we evaluate the financial performance of international stock portfolios based on CSR criteria. We note that previous studies that address the performance of socially screened synthetic portfolios suffer from some limitations and inconsistencies, namely, (1) the majority of prior evidence only refers to the US and European stock markets; (2) with the exception of Badía et al. (2017), previous studies do not compare the performance of SRI portfolios of different regions worldwide; (3) there are studies that measure CSR through one of its individual dimension only, whereas others consider an aggregate construct of CSR; (4) most studies do not evaluate the influence of specific industries on the financial performance of SRI stock portfolios; (5) in several studies assessing European firms, undersized samples are used; (6) up-to-date evidence is lacking; and (7) some researchers who split sample periods merely into sub-periods to evaluate a 'time effect', i.e., whether SRI returns were better in earlier years and yet declined in more recent periods, may have neglected an important effect, specifically, the impact of different market states. Our main objective in this chapter is to evaluate the financial performance of international stock portfolios based on CSR criteria aiming to overcome previous limitations in the evaluation of SRI stock portfolio performance. We form portfolios of stocks with high and low sustainability scores and investigate the performance of such portfolios using multi-factor models. In this chapter, we extend the analysis on the impact of including socially responsible screens on investment portfolios performance to additional geographical areas (North America, Europe, Japan, and Asia Pacific); we compare the financial performance of SRI portfolios of these regions to each other; we form portfolios based on an aggregate measure of CSR as well as on three of its specific ESG dimensions; we evaluate the influence of specific industries on the financial performance of SRI stock portfolios; and finally, we assess the financial performance of SRI stock portfolios over different market states: bear, bull and mixed market periods. In Chapter 5 we evaluate the financial performance of government bond portfolios formed according to ESG criteria. We note that although the concept of SRI was originally related to stock selection, the proportion of portfolio investors applying SRI criteria to bonds has grown significantly in recent years. According to the European Sustainable Investment Forum (EUROSIF, 2016), equities represented over 30% of SRI assets in December 2015, a significant decrease from the previous year's 50%. Meanwhile, there was a strong increase in bonds from the 40% registered in December 2013 to 64%. Both corporate bonds and government bonds underwent a remarkable growth. The former rose from 21.3% to 51.17% of the bond allocation, while the latter increased from 16.6% to 41.26%.In this regard, the financial implications of ESG screening processes on corporate bonds may be closely related to stock selections since corporate bonds are associated with firms. Indeed, previous studies (e.g., Derwall and Koedijk, 2009; Leite and Cortez, 2016) which evaluate the financial performance of mutual funds that invest in socially responsible fixed-income stocks, find that the average SRI bond funds performed similarly to conventional funds. These results are in line with most empirical studies about the performance of SRI funds, which show that they tend to have a similar performance to their conventional peers (Revelly and Viviani, 2015). However, ESG screening processes on government bonds, since they are not related to firms, can help gain an in-depth understanding of SRI consequences for alternative assets. Despite the SRI government bond market growth and the development of country ratings based on ESG factors in recent years, the link between government bond returns and country performance in terms of ESG concerns has been overlooked. In fact, to the best of our knowledge, no previous research has evaluated the financial performance of responsible government bond investments. The main objective of this chapter is to fill this gap. We assess the financial performance of government bond portfolios formed according to ESG criteria. We thus open a discussion on the financial performance of SRI for an alternative asset to firms.In contrast to previous studies, which apply firm sustainability ratings, we use sustainability ratings related to countries. In Chapter 6 we ascertain a less assessed aspect in CSR: distinguishing between investments in material versus immaterial sustainability issues. We note that only firms focused on material sustainability issues associated with their main operations should achieve a competitive advantage and obtain a higher social and financial performance. CSR activities and innovations should be performed on material aspects since otherwise a positive effect on financial performance is not expected. Indeed, investments on immaterial issues may involve additional corporate costs without a social and financial performance associated return. Focusing on material issues is important for firms since they do investments in social aspects that truly affect their operations. Despite issues as prod¬uct safety, climate change, and resource intensity have impacts across several industries, as Hertz et al. (2016) note, those effects often vary to a great extent from one industry to the next. Risks may be everywhere, although they are indeed also particular. As a consequence, firms of specific industries have their particular sustainability profiles. Thus, a firm investing and reporting on material sustainability issues is likely achieved positive financial performance. Meanwhile, a firm investing on material but also on immaterial sustainability issues is likely not achieved superior financial performance. In this chapter, the main objective is to assess the financial performance of stock portfolios formed according to material and immaterial CSR issues. Khan et al. (2016) show that US firms with strong performance on material aspects outperform firms with poor performance on material topics. Our dataset includes companies from US and Europe. We thus extend the previous evidence of Khan et al. (2016) to European firms. Evaluating firms from US and Europe is particularly interesting given the heterogeneity in the patterns of development of SRI across countries (Neher and Hebb, 2015). Furthermore, we use firm' scores from an original dataset that integrates the SASB Materiality Map standards which, to our knowledge, has not been used before.
Not Available ; The land resource inventory of Tumkur-1Microwatershed was conducted using village cadastral maps and IRS satellite imagery on 1:7920 scale. The false colour composites of IRS imagery were interpreted for physiography and the physiographic delineations were used as base for mapping soils. The soils were studied in several transects and a soil map was prepared with phases of soil series as mapping units. Random checks were made all over the area outside the transects to confirm and validate the soil map unit boundaries. The soil map shows the geographic distribution and extent, characteristics, classification, behavior and use potentials of the soils in the microwatershed. The present study covers an area of 643 ha in Yadgir taluk & district, Karnataka. The climate is semiarid and categorized as drought-prone with an average annual rainfall of 866 mm, of which about 652 mm is received during south-west monsoon, 138 mm during north-east and the remaining 76 mm during the rest of the year. An area of 605 ha in the microwatershed is covered by soils and 38 ha by others (habitation and water body). The salient findings from the land resource inventory are summarized briefly below. The soils belong to 10 soil series and 11 soil phases (management units) and 4 land management units. The length of crop growing period is about 120-150 days starting from 1st week of June to 4th week of October. From the master soil map, several interpretative and thematic maps like land capability, soil depth, surface soil texture, soil gravelliness, available water capacity, soil slope and soil erosion were generated. Soil fertility status maps for macro and micronutrients were generated based on the surface soil samples collected at every 320 m grid interval. Land suitability for growing 29 major agricultural and horticultural crops was assessed and maps showing the degree of suitability along with constraints were generated. Entire area in the microwatershed is suitable for agriculture. About 25 per cent area are shallow to moderately shallow (25-75 cm), 69 per cent area of the microwatershed has soils that are moderately deep to very deep (75 - >150 cm). About 12 per cent area in the microwatershed has sandy soils, 15 per cent area is loamy soils and 68per cent clayey soils at the surface. Maximum of 91 per cent area in the microwatershed is non gravelly (200 mm/m) in available water capacity, 14 per cent is medium (101-150 mm/m), 14 per cent area low (51-100 mm/m) and 12 per cent area very low (0.75%) in 1 per cent area. 4 per cent area is high (>57 kg/ha) in available phosphorus and 90 per area is medium (23-57 kg/ha). About 55 per cent is medium (145-337 kg/ha) in available potassium and 39 per cent is high (>337 kg/ha). Available sulphur is low (20 ppm) in 25 per cent. About 4.5 ppm) in an area of about 80 per cent and deficient (0.6 ppm) in 24 per cent area of the microwatershed. The land suitability for 29 major crops grown in the microwatershed were assessed and the areas that are highly suitable (S1) and moderately suitable (S2) are given below. It is however to be noted that a given soil may be suitable for various crops but what specific crop to be grown may be decided by the farmer looking to his capacity to invest on various inputs, marketing infrastructure, market price and finally the demand and supply position. Land suitability for various crops in the Microwatershed Crop Suitability Area in ha (%) Crop Suitability Area in ha (%) Highly suitable (S1) Moderately suitable (S2) Highly suitable (S1) Moderately suitable (S2) Sorghum 436(68) 92(14) Guava - 75(12) Maize 67(10) 452(70) Sapota - 75(12) Bajra 75(12) 453(70) Pomegranate - 444(69) Groundnut - 75(12) Musambi 335(52) 109(17) Sunflower 149(23) 295(46) Lime 335(52) 109(17) Redgram - 444(69) Amla 340(53) 188(29) Bengal gram 436(68) 84(13) Cashew - - Cotton 335(52) 184(29) Jackfruit - 75(12) Chilli - 528(82) Jamun - 369(57) Tomato 67(10) 133(20) Custard apple 436(68) 91(14) Brinjal 142(22) 386(60) Tamarind - 369(57) Onion 142(22) 92(14) Mulberry - 75(12) Bhendi 142(22) 386(60) Marigold - 528(82) Drumstick - 444(69) Chrysanthemum - 528(82) Mango - 41(6) Apart from the individual crop suitability, a proposed crop plan has been prepared for the identified LMUs by considering only the highly and moderately suitable lands for different crops and cropping systems with food, fodder, fibre and other horticulture crops. Maintaining soil-health is vital for crop production and conserve soil and land resource base for maintaining ecological balance and to mitigate climate change. For this, several ameliorative measures have been suggested for these problematic soils like saline/alkali, highly eroded, sandy soils etc., Soil and water conservation treatment plan has been prepared that would help in identifying the sites to be treated and also the type of structures required. As part of the greening programme, several tree species have been suggested to be planted in marginal and submarginal lands, field bunds and also in the hillocks, mounds and ridges. This would help in not only supplementing the farm income but also provide fodder and fuel and generate lot of biomass which would help in maintaining an ecological balance and also contribute to mitigating the climate change. SALIENT FINDINGS OF THE STUDY Results indicated that 36 farmers were sampled in Tumkur-1 micro watershed among them 8 (22.22%) were marginal farmers, 10 (27.78%) were small farmers, 12 (33.33%) were semi medium farmers, 3 (8.33%) were medium farmers, 1 (2.78%) and 2 (5.56%) landless farmers were also interviewed for the survey. The data indicated that there were 161 population households were there in the studied micro watershed. Among them 100 (62.11%) men and 61 (37.89%) were women. The average family size of landless, marginal and small farmers was 5. Semi medium and large farmers were 4 and medium farmer was 3. On an average the family size was 4. The data indicated that 22 (13.66%) people were in 0-15 years of age, 74 (45.96%) were in 16-35 years of age, 52 (32.30%) were in 36-60 years of age and 13 (8.07%) were above 61 years of age. The results indicated that the Tumkur-1 had 37.89 per cent illiterates, 18.63 per cent of them had primary school education, 9.94 per cent of them had both middle school, 14.29 per cent them had high school education, 9.94 per cent of them had PUC education, 1.24 per cent them had ITI education, 4.97 per cent of them had degree education, 1.24 per cent of them had masters education and 1.86 per cent them had others. The results indicated that, 86.11 per cent of households practicing agriculture, 2.78 per cent of the household heads were general labour and in government service. 5.56 per cent of the household heads were in private service. The results indicated that agriculture was the major occupation for 68.32 per cent of the household members, 0.62 per cent were agricultural labourers, 4.97 per cent were general labours, 0.62 percent were in household industry and in government service, 1.86 per cent of them were in private sector, 11.80 per cent of them were students, 3.11 per cent of them were children and 6.83 per cent were housewives. In case of landless households 80 per cent were general labourers and 20 per cent were doing other jobs. In case of marginal farmers 72.97 per cent were agriculturist and 10.81 per cent were students. In case of small farmers 86.96 per cent of them were agriculturist and 10.87 per cent of them were students. In case of semi medium farmers 59.26 per cent of the family members were agriculturist, 1.85 per cent of them were agricultural labour and in government service, 5.56 per cent of them were in private service, 16.67 per cent of them were students, 12.96 per cent of them were housewives and 1.85 per cent of them were children. In case of medium farmers 82 per cent of the family members were agriculturist and 10 per cent of them were students and housewives respectively. In medium farmers, 75 per cent of them were doing agriculture and 25 per cent of them were children. 2 100 per cent of them have not participated in any local institutions. The results indicated that 47.22 per cent of the households possess Katcha house, 41.67 per cent of the households possess Pucca house and 11.11 per cent of them possess Thatched house. The results showed that, 80.56 per cent of the households possess TV, 61.11 per cent of the households possess Mixer grinder, 8.33 per cent of the households possess bicycle, 86.11 per cent of the households possess motor cycle, 2.78 per cent of the households possess landline and 91.67 per cent of the households possess mobile phones. The results showed that the average value of television was Rs. 6,655, mixer grinder was Rs.1650, bicycle was Rs.3666, motor cycle was Rs.59375, landline phone was Rs. 500 and mobile phone was Rs.1475. Data showed that 16.67 per cent of the households possess bullock cart, 22.22 per cent of them possess plough, 36.11 per cent of the households possess tractor, 13.89 per cent of the households possess sprayer, 5.56 per cent of the households possess sprinkler, 22.22 per cent of the households possess weeder and 11.11 per cent of the households possess harvester. The results showed that the average value of bullock cart was Rs.14875; the average value of plough was Rs. 1772, the average value of tractor was Rs. 234615, the average value of sprayer was Rs. 2812, the average value of sprinkler was Rs. 2333, the average value of weeder was Rs. 169 and the average value of harvester was Rs. 220. The results indicated that, 33.33 per cent of the households possess bullocks, 52.78 per cent of the households possess local cow, 2.78 per cent of the households possess crossbred cow, sheep and goat respectively and 27.78 per cent of the households possess buffalo. In land less farmers 50 per cent of the households possess buffalo and goat respectively. In case of marginal farmers, 50 per cent of the households possess bullock, 62.50 per cent of the households possess local cow, 25 per cent of the households possess buffalo and 12.50 per cent of the households possess sheep and poultry birds respectively. In case of small farmers, 30 per cent of households possess bullock and buffalo and 40 per cent of the households possess local cow respectively. In case of semi medium farmers 41.67 per cent of the households possess bullock, 75 per cent of the households possess local cow, 8.33 per cent of the households possess crossbred cow and 16.67 per cent of the households possess buffalo. In medium farmers 33.33 per cent of the households possess buffalo and in large farmers 100 per cent of the households possess local cow and buffalo respectively. The results indicated that, average own labour men available in the micro watershed was 2.00, average own labour (women) available was 1.26, average 3 hired labour (men) available was 9.62 and average hired labour (women) available was 9.97. In case of marginal farmers, average own labour men available was 1.88, average own labour (women) was also 1.38, average hired labour (men) was 6.38 and average hired labour (women) available was 6.88. In case of small farmers, average own labour men available was 2.20, average own labour (women) was 1.30, average hired labour (men) was 9.40 and average hired labour (women) available was 9.20. In case of semi medium farmers, average own labour men available was 2.00, average own labour (women) was 1.25, average hired labour (men) was 10.67 and average hired labour (women) available was 11.50. In medium farmers average own labour men available was 1.67, average own labour (women) was 1.00, average hired labour (men) was 13 and average hired labour (women) available was 13. In large farmers, average own labour men available was 2, average own labour (women) was 1.00, average hired labour (men) was 15 and average hired labour (women) available was 15. The results indicated that, 94.44 per cent of the household opined that hired labour was adequate. The results indicated that, households of the Tumkur-1 micro watershed possess 28.96 ha (53.67%) of dry land and 24.99 ha (46.33%) of irrigated land. Marginal farmers possess 4.46 ha (91.69%) of dry land and 0.40 ha (8.31%) of irrigated land. Small farmers possess 11.12 ha (86.25%) of dry land and 1.77 ha (13.75%) of irrigated land. Semi medium farmers possess 13.38 ha (57.26%) of dry land and 9.98 ha (42.74%) of irrigated land. Medium farmers possess 7.72 ha (100%) of irrigated land and large farmers possess 5.12 ha (100%) of irrigated land. The results indicated that, the average value of dry land was Rs. 448,777.08 and average value of irrigated was Rs. 448,727.33. In case of marginal famers, the average land value was Rs. 761,378.07 for dry land and Rs. 1482000 for irrigated land. In case of small famers, the average land value was Rs. 557,480.90 for dry land Rs. 507,534.26 for irrigated land. In case of semi medium famers, the average land value was Rs. 254,099.85 for dry land and Rs. 532,646.94 for irrigated land. In case of medium famers, the average land value was Rs. 310,854.74 for irrigated land and in large famers; the average land value was Rs. 390,822.77. The results indicated that, there were 1 functioning and 1 defunctioning bore wells in the micro watershed. The results indicated that, bore well was the major irrigation source for 2.78 per cent of the farmers. The results indicated that on an average the depth of the bore well was 2.12 meters. 4 The results indicated that, in case of marginal farmers there was 0. 40 ha of irrigated land. The results indicated that, farmers have grown cotton (20.57 ha), paddy (17.34 ha) and redgram (10.88 ha). Marginal and small farmers had grown cotton and redgram respectively. Semi medium farmers had grown cotton, paddy and redgram. Medium and large farmers had grown paddy. The results indicated that, the cropping intensity in Tumkur-1 micro watershed was found to be 95.09 per cent. In case of marginal farmers it was 100 per cent, in small farmers it was 96.27, in semi medium farmers it was 98.18, in medium farmers it was 79.02 per cent and in large farmers it was 100 per cent. The results indicated that, 61.11 per cent of the households have bank account. The results indicated that, 37.50 per cent of marginal, 60 per cent of small, 25 per cent of the semi medium and 100 per cent of medium farmers have borrowed credit from different sources. The results indicated that, the total cost of cultivation for cotton was Rs. 37349.61. The gross income realized by the farmers was Rs. 70103.06. The net income from cotton cultivation was Rs. 32753.44. Thus the benefit cost ratio was found to be 1:1.88. The results indicated that, the total cost of cultivation for paddy was Rs. 31366.95. The gross income realized by the farmers was Rs. 96591.22. The net income from paddy cultivation was Rs. 65224.27. Thus the benefit cost ratio was found to be 1:3.08. The results indicated that, the total cost of cultivation for redgram was Rs. 44880.62. The gross income realized by the farmers was Rs. 114607.74. The net income from redgram cultivation was Rs. 69727.12, thus the benefit cost ratio was found to be 1:2.55. The results indicated that, 63.89 per cent of the households opined that dry fodder was adequate and 2.78 per cent of the households opined that green fodder was adequate. The table indicated, in landless farmers, that the average income from wage was Rs. 105000, in marginal farmers, the average income from wage was Rs. 36250, agriculture was Rs.51000, dairy farm was Rs.6250 and goat farming was RS.2500. In small farmers the average income from service/salary was Rs. 10000, wage was Rs.39000, agriculture was Rs.68350 and dairy farm was Rs.6000. In semi medium farmers, the average income from wage was Rs. 31666.67, agriculture was Rs.156875 and dairy farm was Rs.1250. In medium farmers, the average income from agriculture was Rs.222000 and dairy farm was Rs.5000. The results indicated that in landless farmers, the average expenditure from wage was Rs.40000. In marginal farmers, the average expenditure from wage was Rs.12142.86, agriculture was Rs.23750, dairy farm was Rs.4250 and goat farming 5 was Rs.10000. In small farmers, the average expenditure from service/salary was Rs.30000, wage was Rs.13750, agriculture was Rs.25000 and dairy farm was Rs.5000. In semi medium farmers the average expenditure from wage was Rs. 19,375, agriculture was Rs. 54,833.33 and dairy farm was Rs.5000. In medium farmers, the average expenditure from agriculture was Rs.108333.33 and dairy farm was Rs.5000. In large farmers the average expenditure from agriculture was Rs. 70,000. The results indicated that, sampled households have grown 1 mango tree in their field. The results indicated that, households have planted 52 neem trees and 1 banyan trees in their field. The results indicated that, cotton, paddy and red gram crops were sold to the extent of 95.25 per cent, 86.67 per cent and 100 per cent respectively. The results indicated that, 94.44 percent of the households have sold their produce to local/village merchant. The results indicated that 72.22 per cent of the households have used tractor as a mode of transport and 22.22 per cent of them have used truck. The results indicated that, 25 per cent of the households have experienced the soil and water erosion problems i.e. 12.50 percent of marginal farmers, 30 per cent of small farmers, 33.33 per cent of semi medium farmers and 100 per cent of medium farmers. The results indicated that, 97.22 per cent of the households have shown interest in soil testing. The results indicated that, 75 percent used fire wood as a source of fuel and 25 percent of the households used LPG as a source of fuel. The results indicated that, piped supply was the source of drinking water for 100 per cent and 2.78 per cent of them were using bore well for drinking water. The results indicated that, electricity was the major source of light for 100 per cent of the households. The results indicated that, 50 per cent of the households possess sanitary toilet i.e. 100 per cent of landless, semi medium and large farmers, 12.50 per cent of marginal, 10 per cent of small and 33.33 per cent of medium had sanitary toilet facility respectively. The results indicated that, 91.67 per cent of the sampled households possessed BPL card and 11.11 per cent of the sampled households have not possessed BPL card. The results indicated that, 69.44 per cent of the households participated in NREGA programme. 6 The results indicated that, cereals, pulses, oilseeds, vegetables, fruits , milk, egg and meat were adequate for 94.44 per cent, 100 per cent, 61.11 per cent, 44.44 per cent, 8.33 per cent, 75 per cent, 30.56 per cent and 8.33 per cent respectively. The results indicated that, cereals and pulses were inadequate for 2.78 per cent of the households, oilseeds, vegetables, fruits , milk, egg and meat were inadequate for 38.89 per cent, 55.56 per cent, 91.67 per cent, 22.22 per cent, 69.44 per cent and 91.67 per cent respectively. The results indicated that, Lower fertility status of the soil was the constraint experienced by 91.67 per cent of the households, wild animal menace on farm field (88.89%), frequent incidence of pest and diseases (86.11%), inadequacy of irrigation water (27.78%), high cost of Fertilizers and plant protection chemicals (80.56%), high rate of interest on credit (27.78%), low price for the agricultural commodities (77.78%), lack of marketing facilities in the area (58.33%), inadequate extension services (25%), lack of transport for safe transport of the agricultural produce to the market (88.89%) and less rainfall (13.89%). ; Watershed Development Department, Government of Karnataka (World Bank Funded) Sujala –III Project
Not Available ; The land resource inventory of Mainahalli microwatershed was conducted using village cadastral maps and IRS satellite imagery on 1:7920 scale. The false colour composites of IRS imagery were interpreted for physiography and these physiographic delineations were used as base for mapping soils. The soils were studied in several transects and a soil map was prepared with phases of soil series as mapping units. Random checks were made all over the area outside the transects to confirm and validate the soil map unit boundaries. The soil map shows the geographic distribution and extent, characteristics, classification, behavior and use potentials of the soils in the microwatershed. The present study covers an area of 510 ha in Koppal taluk and district, Karnataka. The climate is semiarid and categorized as drought - prone with an average annual rainfall of 662 mm, of which about 424 mm is received during south–west monsoon, 161 mm during north-east and the remaining 77 mm during the rest of the year. An area of about 99 per cent is covered by soils and 1 per cent by water bodies, settlements and others. The salient findings from the land resource inventory are summarized briefly below. The soils belong to 11 soil series and 20 soil phases (management units) and 7land Management Units. The length of crop growing period is 150 cm) soils. About 8 per cent area has loamy soils and 92 per cent area has clayey soils at the surface. About 66 per cent of the area has non-gravelly (200 mm/m) in available water capacity. About 87 per cent area has very gently sloping (1-3%) and 13 per cent area has nearly sloping (0-1%) lands. An area of about 28 per cent has soils that are slightly eroded (e1) and 72 per cent moderately eroded (e2) lands. An area of about 7 per cent are slightly alkaline (pH 7.3 to 8.4), 32 per cent are moderately alkaline (pH 7.8-8.4), 35 per cent are strongly alkaline (pH 8.4-9.0) and 26 per cent are very strongly alkaline (pH >9.0) in soil reaction. The Electrical Conductivity (EC) of the soils is 0.75%) in organic carbon. Available phosphorus is low (337 kg/ha) in available potassium content. Available sulphur is medium (10-20 ppm) in about 57 per cent and 42 per cent soils are high (>20 ppm) in the microwatershed. Available boron is low (0.5 ppm) in about 32 per cent area and 67 per cent area is medium (0.5-1.0 ppm). Available iron is sufficient (>4.5 ppm) in the entire area. Available zinc is deficient (0.6 ppm) in about 1 per cent area. Available manganese and copper are sufficient in all the soils. The land suitability for 31 major agricultural and horticultural crops grown in the microwatershed were assessed and the areas that are highly suitable (S1) and moderately suitable (S2) are given below. It is however to be noted that a given soil may be suitable for various crops but what specific crop to be grown may be decided by the farmer looking to his capacity to invest on various inputs, marketing infrastructure, market price and finally the demand and supply position. Land suitability for various crops in the microwatershed Crop Suitability Area in ha (%) Crop Suitability Area in ha (%) Highly suitable (S1) Moderately suitable (S2) Highly suitable (S1) Moderately suitable (S2) Sorghum 119(23) 315(62) Sapota 38 (8) 45 (9) Maize - 434 (85) Pomegranate 63 (12) 247 (49) Bajra 63 (12) 416 (82) Musambi 119 (23) 192 (38) Groundnut 38 (8) 89 (17) Lime 119 (23) 181 (35) Sunflower 119 (23) 178 (35) Amla 63 (12) 416 (82) Red gram 38 (8) 254 (50) Cashew 63 (12) 14 (3) Bengalgram 80 (16) 384 (76) Jackfruit 38 (8) 45 (9) Cotton 119 (23) 315 (62) Jamun 38 (8) 261 (51) Chilli 38 (8) 75 (15) Custard apple 143 (28) 336 (66) Tomato 38 (8) 75 (15) Tamarind 38 (8) 247 (49) Brinjal 24 (5) 366 (72) Mulberry 63 (12) 104 (21) Onion 24 (5) 45 (9) Marigold 38 (8) 249 (49) Bhendi 24 (5) 366 (72) Chrysanthemum 38 (8) 395 (78) Drumstick 63 (12) 234 (46) Jasmine 38 (8) 214 (42) Mango 38 (8) 24 (5) Crossandra 38 (8) 79 (15) Guava 38 (8) 45 (9) Apart from the individual crop suitability, a proposed crop plan has been prepared for the 7 identified LMUs by considering only the highly and moderately suitable lands for different crops and cropping systems with food, fodder, fibre and other horticulture crops that helps in maintaining productivity and ecological balance in the microwatershed. Maintaining soil-health is vital for crop production and conserve soil and land resource base for maintaining ecological balance and to mitigate climate change. For this, several ameliorative measures have been suggested for these problematic soils like saline/alkali, highly eroded, sandy soils etc. Soil and water conservation treatment plan has been prepared that would help in identifying the sites to be treated and also the type of structures required. As part of the greening programme, several tree species have been suggested to be planted in marginal and submarginal lands, field bunds and also in the hillocks, mounds and ridges. That would help in supplementing the farm income, provide fodder and fuel, and generate lot of biomass which in turn would help in maintaining the ecological balance and contribute to mitigating the climate change. SALIENT FINDINGS OF THE STUDY Results indicated that 37 farmers were sampled in Mainahalli micro watershed among them 15 (40.54%) were marginal farmers, 12 (32.43%) were small farmers, 5 (13.51 %) were semi medium farmers, 1 (2.70%) were medium farmers and 4 (10.81%) landless farmers were also interviewed for the survey. The data indicated that there were 168 population households were there in the studied micro watershed. Among them 94 (55.95%) men and 74 (44.05%) were women. The average family size of landless and small farmer was 4, marginal farmers and semi medium farmers were 5 and medium farmers were 7. On an average the family size was 6. The data indicated that 36 (21.43%) people were in 0-15 years of age, 65 (38.69%) were in 16-35 years of age, 52 (30.95%) were in 36-60 years of age and 15 (8.93 %) were above 61 years of age. The results indicated that the Mainahalli had 29.76 per cent illiterates, 30.36 per cent of them had primary school education, 9.52 per cent of them had both middle school, 13.10 per cent them had high school education, 8.93 per cent of them had PUC education, 0.60 per cent of them had diploma and ITI education, 6.55 per cent of them had degree education. The results indicated that, 86.49 per cent of households practicing agriculture, 10.81 per cent of the household heads were agricultural labour and 2.70 per cent of the household heads were doing trade and business. The results indicated that agriculture was the major occupation for 20.24 per cent of the household members, 48.81 per cent were agricultural laborers, 0.60 per cent were general labours, private sector, trade and business and children respectively. 28.57 per cent of them were students. In case of landless households 6.67 per cent were agriculturist, 86.67 per cent were agricultural labors and 6.67 per cent were students. In case of marginal farmers 19.18 per cent were agriculturist, 54.79 per cent were agricultural labour and 24.66 per cent were students. In case of small farmers 27.66 per cent of them were agriculturist, 36.17 per cent of them were agriculture labour, 2.13 per cent were trade and business and 34.04 per cent of them were students. In case of semi medium farmers 19.23 per cent of the family members were agriculturist, 34.62 per cent were agriculture labour and 42.31 per cent of them were students. In case of medium farmers 14.29 per cent of the family members were agriculturist, 42.86 per cent of them were agriculture labors and 28.57 per cent were students and 14.29 per cent of them were children. The results showed 100 per cent of the farmers have not participated in any local institutions. 2 The results indicated that 94.59 per cent of the households possess Katcha house, 5.41 per cent of the households possess Pucca house and 2.70 per cent of them possess Thatched house. The results showed that, 70.27 per cent of the households possess TV, 2.70 per cent of them possess DVD/VCD Player, 10.81 per cent of the households possess Mixer grinder, 5.41 per cent of the households possess bicycle, 24.32 per cent of the households possess motor cycle and 75.68 per cent of the households possess mobile phones. The results showed that the average value of television was Rs. 6076, DVD/VCD Player was Rs.3000, mixer grinder was Rs.1250, bicycle was Rs.400, motor cycle was Rs.37666 and mobile phone was Rs.4336. About 8.11 per cent of the households possess bullock cart, 2.70 per cent of them possess plough 21.62 per cent of the households possess weeder. The results showed that the average value of bullock cart was Rs.21666; the average value of plough was Rs. 2000 and the average value of weeder was Rs. 24. The results indicated that, 10.81 per cent of the households possess bullocks, 5.41 per cent of the households possess local cow, 2.70 per cent of the households possess crossbred cow, 8.11 per cent of the household possess buffalo and 5.41 per cent of the households possess sheep. In case of marginal farmers, 6.67 per cent of the households possess bullock, local cow, crossbred cow and buffalo respectively. In case of small farmers, 8.33 per cent of households possess bullock, local cow, buffalo and sheep respectively. In case of semi medium farmers, 40 per cent of the households possess bullock and 20 per cent possess buffalo and sheep respectively. The results indicated that, average own labour men available in the micro watershed was 1.78, average own labour (women) available was 1.36, average hired labour (men) available was 8.11 and average hired labour (women) available was 8.69. In case of marginal farmers, average own labour men available was 2.00, average own labour (women) was also 1.47, average hired labour (men) was 6.47 and average hired labour (women) available was also 6.73. In case of small farmers, average own labour men available was 1.58, average own labour (women) was 1.25, average hired labour (men) was 6.75 and average hired labour (women) available was 7.58. In case of semi medium farmers, average own labour men available was 1.80, average own labour (women) was 1.40, average hired labour (men) was 9.40 and average hired labour (women) available was 10.60. In medium farmers average own labour men available was 2, average own labour (women) was 2, average hired labour (men) was 30 and average hired labour (women) available was 30. The results indicated that, 8.11 per cent of the household opined that hired labour was adequate and 89.19 per cent of the household opined that hired labour was inadequate. 3 The results indicated that, households of the Mainahalli micro watershed possess 38.26 ha (90.61%) of dry land and 3.97 ha (9.39%) of irrigated land. Marginal farmers possess 10.44 ha (100%) of dry land. Small farmers possess 15.95 ha (92.27 %) of dry land and 1.34 ha (7.73 %) of irrigated land. Semi medium farmers possess 7.43 ha (73.84%) of dry land and 2.63 ha (26.16%) of irrigated land. Medium farmers possess 4.45 ha (100%) of dry land. The results indicated that, the average value of dry land was Rs. 233,807.51 and average value of irrigated was Rs. 441,071.43. In case of marginal famers, the average land value was Rs. 426,027.13 for dry land. In case of small famers, the average land value was Rs. 206,878.17 for dry land Rs. 486,515.16 for irrigated land. In case of semi medium famers, the average land value was Rs. 121,144.41 for dry land and Rs. 418,000 for irrigated land. In case of medium famers, the average land value was Rs. 67,363.64 for dry land and. The results indicated that, there were 1 functioning and 1 defunctioning bore wells in the micro watershed. The results indicated that, bore well was the major irrigation source for 2.70 per cent of the farmers. The results indicated that on an average the depth of the bore well was 3.77 meters. The results indicated that, in case of small farmers there was 0.81 ha of irrigated land. The results indicated that, farmers have grown bajra (7.81 ha), maize (19.33 ha), sorghum (5.26 ha), red gram (0.20 ha) and sunflower (8.15 ha) in kharif season. Marginal farmers had grown bajra, sorghum and maize. Small farmers had grown bajra, sorghum, maize and sunflower. Semi medium farmers had grown bajra, sorghum, maize, red gram and sunflower. Medium farmers had grown sunflower. The results indicated that, the cropping intensity in Mainahalli micro watershed was found to be 78.16 per cent. In case of marginal farmers it was 83.93 per cent, in case of small farmers it was 75.66 per cent, in case semi medium it was 70.24 per cent and medium farmers it was 100 per cent. The results indicated that, 81.08 per cent of the households have both bank account and savings. 100 percent of the marginal and medium farmers possess bank account and savings respectively. 83.33 per cent of small farmers possess both bank account and savings. Semi medium farmers possess 80 per cent of both bank account and savings. The results indicated that, 100 per cent of marginal, 83.33 per cent of small, 80 per cent of semi medium and 100 per cent of medium farmers have borrowed credit from different sources. The results indicated that, the total cost of cultivation for bajra was Rs. 33170.04. The gross income realized by the farmers was Rs. 26696.83. The net income from bajra cultivation was Rs. -6473.21, thus the benefit cost ratio was found to be 1:0.8. 4 The results indicated that, the total cost of cultivation for maize was Rs. 38478.23. The gross income realized by the farmers was Rs. 31675.95. The net income from maize cultivation was Rs. -6802.28. Thus the benefit cost ratio was found to be 1:0.82. The results indicated that, the total cost of cultivation for Sunflower was Rs. 61336.98. The gross income realized by the farmers was Rs. 49804.52. The net income from Sunflower cultivation was Rs. -11532.46. Thus the benefit cost ratio was found to be 1:0.81. The results indicated that, the total cost of cultivation for Sorghum was Rs. 41245.83. The gross income realized by the farmers was Rs. 27371.03. The net income from Sorghum cultivation was Rs. -13874.80. Thus the benefit cost ratio was found to be 1:0.66. The results indicated that, 5.41 per cent of the households opined that dry fodder was adequate and 13.51 per cent of the households opined that green fodder was adequate. The table indicated that, in case of landless, the average income from wage was Rs. 52000. In case marginal farmers the average income from wage was Rs.666.67, agriculture was Rs.40666.6 and dairy farm was Rs.1333.33. In small farmers, the average income from service/salary was Rs.3333.33 and agriculture was Rs. 46,666.67. In semi medium farmers the average income from wage was Rs. 2400, agriculture was Rs.137000, dairy farm was Rs. 1200 and goat farming was Rs.8000. In medium farmers the average income from agriculture was Rs.100000. The results indicated that, in case of land less, the average expenditure from wage Rs. 17500. In marginal farmers, the average expenditure from wage was Rs.5000, agriculture was Rs.20200 and dairy farm was Rs.3500. In small farmers, the average expenditure from service/salary was Rs.10000 and agriculture was Rs.24833.33. In semi medium farmers the average expenditure from wage was Rs.8000, agriculture was Rs.39000, dairy farm was Rs.2000 and goat farming was Rs.15000. In medium farmers the average expenditure from agriculture was Rs.70000. The results indicated that, sampled households have grown 3 coconut trees in their field. The results indicated that, households have planted 2 teak trees, 21 neem trees, 1tamarind tress, 4 banyan and 1 peeple trees in their field. The results indicate that, households have an average investment capacity of Rs. 4108.11 for land development and Rs.270.27 for improved crop production. Marginal farmers have an average investment capacity of Rs. 5333.33 for land development and Rs.266.67 for improved crop production. Small farmers have an average investment capacity of Rs. 4250 for land development. Semi medium farmers have an average investment capacity of Rs. 3800 for land development and Rs. 600 5 in irrigation facility. Medium farmers have an average investment capacity of Rs. 2000 for land development and Rs. 3000 in irrigation facility. The results indicated that for land development, 2.63 per cent of the households were dependent on loan from bank and own funds respectively and 50 per cent of the households were dependent on soft loan. 7.89 per cent of the households were dependent on soft loan for improved crop production. The results indicated that, bajra, sorghum, maize, redgram and sunflower crops were sold to the extent of 100 per cent. The results indicated that, 48.65 percent of the households have sold their produce to local/village merchant and 59.46 percent of the households sold their produce to regulated market. The results indicated that 2.70 per cent of the households have used head load and truck as a mode of transport, 13.51 per cent of the households used cart as a mode of transport and 89.19 per cent of them have used tractor. The results indicated that, 27.03 per cent of the households have experienced the soil and water erosion problems i.e. 46.67 percent of marginal farmers and 25 per cent of small farmers. The results indicated that, 75.68 per cent of the households have shown interest in soil testing includes 93.33 per cent of the marginal, 75 per cent of the small, 80 per cent of the semi medium and 100 per cent of the medium farmers. The results indicated that, 100 percent used fire wood as a source of fuel and 2.70 percent of them used LPG. The results indicated that, piped supply was the source of drinking water for 35.14 per cent households and 67.57 per cent of them were using bore well for drinking water. The results indicated that, electricity was the major source of light for 100 per cent of the households. The results indicated that, 59.46 per cent of the households possess sanitary toilet i.e. 100 per cent of landless, 100 per cent of marginal, 8.33 per cent of the small, 20 per cent of the semi medium and 100 medium farmers had sanitary toilet facility. The results indicated that, 100 per cent of the sampled households possessed BPL card. The results indicated that, 37.84 per cent of the households participated in NREGA programme which included 100 per cent of the landless, 26.67 percent of the marginal, 25 per cent of the small, 40 per cent of the semi medium and 100 per cent of the medium farmers. The results indicated that, cereals, pulses, oilseeds, vegetables, fruits , milk and egg were adequate for 97.30 per cent, 86.49 per cent, 43.24 per cent, 43.24 per cent, 62.16 per cent, 29.73 per cent and 51.35 per cent respectively. 6 The results indicated that, pulses, oilseed, vegetables, fruits milk, egg and meat were inadequate for 10.81 per cent, 51.35 per cent, 54.05 per cent, 24.32 per cent, 24.32 per cent, 35.14 per cent and 5.41 per cent of the households. The results indicated that, 89.19 per cent of the households experienced by lower fertility status of the soil was the constraint, wild animal menace on farm field (67.57%), frequent incidence of pest and diseases (51.35%), inadequacy of irrigation water (24.32%), high cost of Fertilizers and plant protection chemicals (32.43%), high rate of interest on credit (10.81%), low price for the agricultural commodities (16.22%), lack of marketing facilities in the area (8.11%), inadequate extension services (10.81%), lack of transport for safe transport of the agricultural produce to the market (27.03%), less rainfall (64.86%) and Source of Agri-technology information(Newspaper/TV/Mobile) (13.51%). ; Watershed Development Department, Government of Karnataka (World Bank Funded) Sujala –III Project
Not Available ; The land resource inventory of Abbagiri Tanda-1 microwatershed was conducted using village cadastral maps and IRS satellite imagery on 1:7920 scale. The false colour composites of IRS imagery were interpreted for physiography and these physiographic delineations were used as base for mapping soils. The soils were studied in several transects and a soil map was prepared with phases of soil series as mapping units. Random checks were made all over the area outside the transects to confirm and validate the soil map unit boundaries. The soil map shows the geographic distribution and extent, characteristics, classification, behavior and use potentials of the soils in the microwatershed. The present study covers an area of 877 ha in Koppal taluk and district, Karnataka. The climate is semiarid and categorized as drought - prone with an average annual rainfall of 662 mm, of which about 424 mm is received during south –west monsoon, 161 mm during north-east and the remaining 77 mm during the rest of the year. An area of 96 per cent is covered by soils and 4 per cent is by water bodies. The salient findings from the land resource inventory are summarized briefly below. The soils belong to 16 soil series and 52 soil phases (management units) and 8 Land Management Units. The length of crop growing period is 150 cm). About 6 per cent area in the microwatershed has sandy soils, 57 per cent soils are loamy and 16 per cent clayey soils at the surface About 32 per cent area has non-gravelly (200 mm/m) in available water capacity. About 4 per cent in the microwatershed has nearly level (0-1% slope) lands, 69 per cent has very gently sloping (1-3% slope) lands and 6 per cent area is gently sloping (3-5% slope) An area of about 59 per cent is moderately (e2) eroded and 19 per cent area is slightly (e1) eroded. An area of about 21 per cent soils are strongly acid (pH 5.0-5.5), an area of about 21 per cent soils are moderately acid (pH 5.5-6.0), an area of about 13 per cent soils are slightly acid (pH 6.0-6.5) in soil reaction, an area of 10 per cent is neutral (pH 6.5- 7.3), 5 per cent soils are slightly alkaline (pH 7.3-7.8), 7 per cent soils are moderately alkaline (pH 7.8-8.4) and 2 per cent is strongly alkaline (pH 8.4-9.0). The Electrical Conductivity (EC) of the soils in the entire area of the microwatershed is 0.75%) in 61 per cent area. Available phosphorus is medium (23-57 kg/ha) in an area 25 per cent and high (>57 kg/ha) in an area of 54 per cent. About 43 per cent is low (145 kg/ha) in available potassium, 27 per cent is medium (145-337 kg/ha) and 8 per cent is high (>337 kg/ha). Available sulphur is low (4.5 ppm) in 67 per cent area. Available zinc is deficient (0.6 ppm) in 38 per cent area. Available copper and manganese are sufficient in all the soils. The land suitability for 28 major crops grown in the microwatershed were assessed and the areas that are highly suitable (S1) and moderately suitable (S2) are given below. It is however to be noted that a given soil may be suitable for various crops but what specific crop to be grown may be decided by the farmer looking to his capacity to invest on various inputs, marketing infrastructure, market price and finally the demand and supply position. Land suitability for various crops in the microwatershed Crop Suitability Area in ha (%) Crop Suitability Area in ha (%) Highly suitable (S1) Moderately suitable (S2) Highly suitable (S1) Moderately suitable (S2) Sorghum 40 (5) 177 (20) Pomegranate - 180 (20) Maize 15 (2) 202 (23) Guava - 158 (18) Bajra 19 (2) 316 (36) Jackfruit - 158 (18) Groundnut - 471 (54) Jamun - 160 (18) Sunflower 22 (3) 58 (7) Musambi 22 (3) 158 (18) Cotton 22 (3) 194 (22) Lime 22 (3) 158 (18) Red gram - 80 (9) Cashew 15 (2) 233 (27) Bengalgram 22 (3) 205 (23) Custard apple 79 (9) 531 (60) Chilli 18 (2) 199 (23) Amla 46 (5) 557 (64) Tomato 18 (2) 199 (23) Tamarind - 24 (3) Drumstick 2 (<1) 253 (29) Marigold 15 (2) 202 (23) Mulberry 2 (<1) 426 (48) Chrysanthemum 15 (2) 202 (23) Mango - 2 (<1) Jasmine 15 (2) 180 (20) Sapota - 158 (18) Crossandra 15 (2) 180 (20) Apart from the individual crop suitability, a proposed crop plan has been prepared for the 8 identified LMUs by considering only the highly and moderately suitable lands for different crops and cropping systems with food, fodder, fibre and other horticulture crops. Maintaining soil-health is vital for crop production and conserve soil and land resource base for maintaining ecological balance and to mitigate climate change. For this, several ameliorative measures have been suggested for these problematic soils like saline/alkali, highly eroded, sandy soils etc., Soil and water conservation treatment plan has been prepared that would help in identifying the sites to be treated and also the type of structures required. As part of the greening programme, several tree species have been suggested to be planted in marginal and submarginal lands, field bunds and also in the hillocks, mounds and ridges. That would help in supplementing the farm income, provide fodder and fuel, and generate lot of biomass which in turn would help in maintaining the ecological balance and contribute to mitigating the climate change. SALIENT FINDINGS OF THE SURVEY The data indicated that there were 124 (53.45%) men and 108 (46.55%) were women among the sampled households. The average family size of land less farmers was 4, marginal farmers were 4, small farmer was 5, semi medium farmer was 5 and medium farmers were 3. There were 55 (23.71%) people were in 0-15 years of age, 101 (43.53%) were in 16-35 years of age, 57 (24.57 %) were in 36-60 years of age and 19 (8.19%) were above 61 years of age. The micro watershed had 34.48 per cent illiterates, 0.43 per cent functional literates, 34.48 per cent of them had primary school education, 2.59 per cent of them had middle school education, 13.36 per cent of them had high school education, 7.33 per cent of them had PUC education, 1.66 per cent of them diploma, 0.86 per cent of them had ITI, 3.45 per cent of them had degree education and 1.29 per cent of them had other education. The results indicate that, 81.63 per cent of households practicing agriculture and 4.08 per cent of the household heads were agricultural laborers. The results indicate that agriculture was the major occupation for 53.88 per cent of the household members, 7.33 per cent were agricultural laborers, 4.31 per cent were general labours, 0.43 percent were in government service, 0.86 per cent of them were in private sector, 0.43 per cent of them were in trade and business, 21.98 per cent of them were students and 1.72 per cent were housewives. The results shows that 1.72 per cent of them participated in self help groups, 0.86 per cent of them participated in gram panchayat, 0.43 per cent of them participated in Raitha Sangha and 96.98 per cent of them have not participated in any local institutions. Landless and medium farmers were found to have no participation in any local institutions. Semi medium farmers were found to participate in one or the other local institutions. The results indicate that 87.76 per cent of the households possess Katcha house and 10.20 per cent of them possess Pucca house and 2.04 per cent them possess semi Pucca house. 100 percent of the landless, marginal and small farmers possess Katcha house. The results shows that 57.14 per cent of the households possess TV, 34.69 per cent of the households possess Mixer grinder, 28.57 per cent of the households possess bicycle, 42.86 per cent of the households possess motor cycle, and 83.67 per cent of the households possess mobile phones. The average value of television was Rs.3178, mixer grinder was Rs.1876, DVD player was Rs.2000, motor cycle was Rs.33857, bicycle was Rs.1250, Auto Rs.200000 and mobile phone was Rs.1736. About 34.69 per cent of the households possess plough, 30.61 per cent of them possess bullock cart and 26.53 per cent of the households possess sprayer, 12.24 2 per cent of them possess chaff cutter and 46.94 per cent of the households possess weeder. The average value of plough was Rs.748, the average value of bullock cart was Rs. 18750 and the average value of sprayer was Rs.2117. The results indicate that, 36.73 per cent of the households possess bullocks, 18.37 per cent of the households possess local cow, 6.12 per cent of the households possess buffalo and 2.04 per cent of the households possess equally for sheep and goat respectively. Average own labour men available in the micro watershed was 1.91, average own labour (women) available was 1.45, average hired labour (men) available was 11.36 and average hired labour (women) available was 11.41. The results indicate that, 89.80 per cent of the household opined that hired labour was adequate. The results indicate that, 1 person was migrated from micro watershed that belonged to semi medium farmer category. People have migrated on an average of 450 Kms and average duration was 10 months. Job/work was important reason for migration for all the migrants. Households of the Abbagiri Tanda-1 micro watershed possess 40.54 ha (68.45%) of dry land and 18.69 ha (31.55%) of irrigated land. The average value of dry land was Rs.260116.79 and average value of irrigated was Rs.316639.24. There were 14 functioning and 7 defunct bore wells in the micro watershed. Bore well was the major irrigation source for 28.57 per cent of the farmers. There were 10.76 ha of irrigated area in total in the micro watershed. The results indicate that, farmers have grown Maize (29.38 ha), Navane (0.81 ha), Bajra (7.32 ha), Paddy (1.22 ha), Sorghum (3.04 ha), and Red gram (2.49ha) in kharif season and Bengal gram (4.08 ha), groundnut (1.21 ha), Red gram (1.21 ha) and sorghum (0.81 ha) in Rabi season. Marginal farmers have grown Maize, Bajra, sorghum, Ground nut, Bengal gram and Redgram. Small farmers have grown Maize, Bajra, sorghum, Bengal gram and paddy. Semi medium farmers have grown Maize, bajra, Bengal gram and Paddy. Medium farmers have grown Maize and Bengal gram. Medium farmers have grown Maize and Bengal gram. The cropping intensity in Abbagiri Tanda-1 micro watershed was found to be 90.91 per cent. In case of Marginal farmers it was 96.40 per cent, for small farmers it was 87.36 per cent, in case of semi medium farmers it was 89.84 per cent and medium farmers had cropping intensity of 100 per cent. The results indicate that, 55.10 per cent and 53.06 per cent of the households have both bank account and savings respectively. Among marginal farmers 63.64 percent of them possess both bank account and savings. 65 per cent of small farmers possess bank account and 60 per cent of savings. Semi medium farmers possess 54.55 per cent of both bank account and savings and medium category of farmers possess 50 per cent of bank account and also savings. 3 The results indicate that, 65.22 per cent have availed loan in Grameena bank, 43.48 per cent have availed loan in money lender and 4.35 per cent have availed loan from cooperative bank. Marginal, small, semi medium and medium have availed Rs.16142.86, Rs. 114444.44, Rs.50000, and Rs. 250000 respectively. Overall average credit amount availed by households in the micro watershed is 73608.70. The results indicate that, 18.75 per cent of the households have repaid their institutional credit partially which includes 22.22 per cent of small farmers, 33.33 per cent of semi medium farmers. The data also showed that 56.25 per cent of households have unpaid their loans and only 25 per cent of households have fully repaid their loans. The results also indicated that 20 per cent of the households have repaid their private credit partially and 80 percent of the households have unpaid their loan. The results indicated that, the total cost of cultivation for bajra was Rs. 23153.0. The gross income realized by the farmers was Rs. 22838.19. The net income from bajra cultivation was Rs. -314.89, thus the benefit cost ratio was found to be 1:0.99. The results indicate that, the total cost of cultivation for maize was Rs. 20389.96. The gross income realized by the farmers was Rs. 47012.31. The net income from maize cultivation was Rs. 26622.35, and the income generated from red gram was Rs. 10618.55, thus the benefit cost ratio was found to be 1:2.31. Total cost of cultivation for navane was Rs. 12067.31. The gross income realized by the farmers was Rs. 25935. The net income from navane cultivation was Rs. 13867.69. Thus the benefit cost ratio was found to be 1:2.15. Total cost of cultivation for paddy was Rs. 35259.34. The gross income realized by the farmers was Rs. 41003.63. The net income from paddy cultivation was Rs. 5744.09. Thus the benefit cost ratio was found to be 1:1.16. The total cost of cultivation for sorghum was Rs. 14260.22. The gross income realized by the farmers was Rs. 27694.88. The net income from sorghum cultivation was Rs. 13715.62. Thus the benefit cost ratio was found to be 1:1.96. The total cost of cultivation for bengalgram was Rs. 41141.81. The gross income realized by the farmers was Rs. 80509.57. The net income from bengalgram cultivation was Rs. 39367.76. Thus the benefit cost ratio was found to be 1:1.96. Total cost of cultivation for groundnut was Rs. 48921.12. The gross income realized by the farmers was Rs. 80878.78. The net income from groundnut cultivation was Rs. 31957.66. Thus the benefit cost ratio was found to be 1:1.65. The total cost of cultivation for redgram was Rs. 13963.32. The gross income realized by the farmers was Rs. 25468.44. The net income from redgram cultivation was Rs. 11505.12. Thus the benefit cost ratio was found to be 1:1.82. 4 The results indicate that, 59.18 per cent of the households opined that dry fodder was adequate, 4.08 per cent of the households opined that dry fodder was inadequate also the data revealed that 24.49 per cent of the farmers opined that green fodder is adequate. The table indicated that the average income from service/salary was Rs. 3571.43, business Rs.3632.65, wage Rs.27367.35, agriculture Rs. 52992.86, farm income Rs.1632.65, Non farm income Rs.3061.22, dairy farm Rs.2215.31 and goat farming was Rs.3061.22. The results indicated that, 44.90 per cent of the households are interested in growing horticultural crops which include 45.45 per cent marginal farmers, 55 per cent small farmers, 45.45 per cent semi medium farmers and 50 per cent medium farmers. The results indicated that for 12 per cent of the households were dependent on government subsidy for land development. Similarly for the dependency was for irrigation facility 48 percent and only 2 percent for improved crop production. The results indicated that, only Navane was sold to the extent of 100 per cent. The data regarding marketing channels used for sale of agricultural produce was showing that 57.14 percent of the households have sold their produce to local/village merchants, 42.86 percent of the households sold their produce in regulated markets and only 4.08 per cent of the households sold their produce to agents/traders. The data about mode of transport of agricultural produce indicated that 75.51 per cent of the households have used cart as a mode of transport and 28.57 per cent have used tractor. The results indicated that, 57.14 per cent of the households have shown interest in soil testing i.e. 63.64 per cent of marginal farmers, 70 per cent of small farmers, 54.55 per cent of semi medium and 50 per cent of medium farmers have shown interest in soil testing. The data pertaining to soil and water conservation practices and structures adopted in micro watershed was indicating that, 10.20 per cent of the households have adopted field bunding which includes 18.18 per cent of marginal, 10 per cent of small farmers, and 50 per cent of medium farmers. Summer ploughing was adopted by 57.14 per cent of the households i.e.63.64 per cent of the marginal farmers, 70 per cent of the small farmers, 54.55 per cent of semi medium and 50 per cent medium farmers. Form pond was adopted by the farmers was 2.04 per cent. The data regarding agencies involved in soil conservation structures in was showing that 2.04 per cent of soil conservation structure is constructed by farmers on their own, 8.16 per cent of the soil conservation structures are constructed by the government and another 2.04 per cent is constructed by farmer's organization. 5 The results indicated that, 83.67 percent used fire wood as a source of fuel, and 14.29 percent of the households used LPG. Also results indicated that, piped supply was the major source for drinking water for 53.06 per cent which includes 100 per cent of landless, 45.45 per cent of marginal, 40 per cent of small farmers, 63.64 per cent of semi medium and 50 per cent of medium farmers and 10.20 per cent of the households were using bore well as a source of drinking water. The results indicated that, electricity was the major source of light which was found to be 93.88 per cent. The results indicated that, 34.69 per cent of the households possess sanitary toilet i.e. 60 per cent of landless, 45.45 per cent of marginal, 3 per cent of small, 9.09 per cent of semi medium and 50 per cent of medium had sanitary toilet facility. The results indicated that, 91.84 per cent of the sampled households possessed BPL card and 2.04 per cent of the sampled households have not possessed BPL card. The results indicated that, 36.73 per cent of the households participated in NREGA programme which included 60 per cent of the landless, 45.45 percent of the marginal, 4 per cent of the small, 36.36 per cent of the semi medium and 100 percent of the medium farmers. The result of data regarding adequacy of food items was showing that that, 97.97 per cent of cereals, 79.59 per cent of pulses, 63.27 per cent of oilseeds and 67.35 percent of both milk and egg were adequate for the households. Vegetables and fruits were adequate only for 10.20 per cent and meat was 4.08 per cent for the households respectively. Also, the results indicated that, both vegetables and fruits were inadequate for 85.71 per cent of the households. Milk and egg were inadequate for 30.61 per cent respectively; meat was inadequate for 89.90 per cent. Cereals, pulses and oilseeds were inadequate for 2.04 per cent, 18.37 per cent and 8.16 per cent respectively. The results of the farming constraints experienced by households in studied micro watershed was indicating that Lower fertility status of the soil was the constraint experienced by 32.65 per cent of the households, wild animal menace on farm field (59.18%), frequent incidence of pest and diseases (55.10%), inadequacy of irrigation water (55.10%), high cost of Fertilizers and plant protection chemicals (65.31%), high rate of interest on credit (63.27%), low price for the agricultural commodities (65.31%), lack of marketing facilities in the area (67.35%), inadequate extension services (63.27%), lack of transport for safe transport of the agricultural produce to the market (79.59%), less rainfall (30.61%) and Source of Agri-technology information(Newspaper/TV/Mobile) (12.24). ; Watershed Development Department, Government of Karnataka (World Bank Funded) Sujala –III Project
Not Available ; The land resource inventory of Katrahalli microwatershed was conducted using village cadastral maps and IRS satellite imagery on 1:7920 scale. The false colour composites of IRS imagery were interpreted for physiography and these physiographic delineations were used as base for mapping soils. The soils were studied in several transects and a soil map was prepared with phases of soil series as mapping units. Random checks were made all over the area outside the transects to confirm and validate the soil map unit boundaries. The soil map shows the geographic distribution and extent, characteristics, classification, behavior and use potentials of the soils in the Microwatershed. The present study covers an area of 444 ha in Koppal taluk and district, Karnataka. The climate is semiarid and categorized as drought - prone with an average annual rainfall of 662 mm, of which about 424 mm is received during south –west monsoon, 161 mm during north-east and the remaining 77 mm during the rest of the year. An area of about 90 per cent is covered by soil and 10 per cent by water bodies, settlements and others. The salient findings from the land resource inventory are summarized briefly below The soils belong to 14 soil series and 23 soil phases (management units) and 7 land management units The length of crop growing period is 150cm) soils. About 3 per cent is sandy (loamy sand) at the surface, 15 per cent loamy (sandy loam and sandy clay loam) and 72 per cent has clayey (sandy clay and clay) soils at the surface. About 56 per cent of the area has non-gravelly (200mm/m) available water capacity. An area of about 13 per cent has nearly level (0-1%) and 77 per cent has very gently sloping (1-3%) lands. An area of about 50 per cent is slightly eroded (e1) and 40 per cent is moderately eroded (e2) lands. An area of about 86 per cent is strongly alkaline (pH 8.4-9.0) and 4 per cent is very strongly alkaline (pH >9.0). The Electrical Conductivity (EC) of the soils are dominantly 57 kg/ha) in 31 per cent of the soils. Available potassium is medium (145-337 kg/ha) in 5 per cent and high (>337 kg/ha) in 85 per cent of the soils. Available sulphur is low (20 ppm) in 64 per cent area of the soils. Available boron is low (1.0 ppm) in 4.5 ppm) in 42 per cent of the area. Available zinc is deficient (0.6 ppm) in <1 per cent of the microwatershed. Available manganese and copper are sufficient in the entire area. The land suitability for 28 major agricultural and horticultural crops grown in the microwatershed was assessed and the areas that are highly suitable (class S1) and moderately suitable (class S2) are given below. It is however to be noted that a given soil may be suitable for various crops but what specific crop to be grown may be decided by the farmer looking to his capacity to invest on various inputs, marketing infrastructure, market price, and finally the demand and supply position. Land suitability for various crops in the microwatershed Crop Suitability Area in ha (%) Crop Suitability Area in ha (%) Highly suitable (S1) Moderately suitable (S2) Highly suitable (S1) Moderately suitable (S2) Sorghum 127 (29) 157 (35) Pomegranate 48(11) 267(60) Maize 69 (16) 217 (49) Guava 14(3) 140(31) Bajra 82(18) 269(60) Jackfruit 48(11) 105(24) Redgram 34(8) 145 (32) Jamun 34(8) 168(22) Bengal gram 58(13) 234(53) Musambi 106(24) 209(47) Groundnut 14(3) 179 (40) Lime 106(24) 209 (47) Sunflower 93 (21) 165 (37) Cashew 48(11) 105(24) Cotton 93(21) 193(43) Custard apple 154(35) 227(51) Chilli 82(18) 14(3) Amla 95(22) 285 (64) Tomato 82(18) 14(3) Tamarind 95(22) 285(64) Drumstick 48(11) 240(54) Marigold 69(16) 215(49) Mulberry 48(11) 263(59) Chrysanthemum 69(16) 215(49) Mango 34(8) 39(9) Jasmine 69(16) 53(12) Sapota 48(11) 105(24) Crossandra 69(16) 141(32) Apart from the individual crop suitability, a proposed crop plan has been prepared for the 7 identified LMUs by considering only the highly and moderately suitable lands for different crops and cropping systems with food, fodder, fibre and other horticulture crops. Maintaining soil-health is vital for crop production and conserve soil and land resource base for maintaining ecological balance and to mitigate climate change. For this, several ameliorative measures have been suggested to these problematic soils like saline/alkali, highly eroded, sandy soils etc., Soil and water conservation and drainage line treatment plans have been prepared that would help in identifying the sites to be treated and also the type of structures required. As part of the greening programme, several tree species have been suggested to be planted in marginal and submarginal lands, field bunds and also in the hillocks, mounds and ridges. That would help in supplementing the farm income, provide fodder and fuel, and generate lot of biomass which in turn would help in maintaining the ecological balance and contribute to mitigating the climate change. SALIENT FINDINGS OF THE SURVEY The data on households sampled for socio economic survey in Katarahalli microwatershed indicated that 35 farmers were sampled in Katarahalli microwatershed among them 5 (14.71 %) were landless farmers, 7 (20.59 %) were marginal farmers, 10 (29.41 %) were small farmers, 5 (14.71 %) were semi medium farmer, 6 (17.65 %) were medium farmers and 1(2.94 %) were large farmers. The data indicated that there were 95 (56.21 %) men and 74 (43.79 %) were women among the sampled households. The average family size of large farmers' was 5, marginal farmers' was 3.4, small farmers' was 5.8, semi medium farmers' was 6, medium farmers' was 4.5 and large farmers' was 5. The data indicated that, 28 (16.57 %) people were in 0-15 years of age, 72 (42.60 %) were in 16-35 years of age, 51 (30.18 %) were in 36-60 years of age and 18 (10.65 %) were above 61 years of age. The results indicated that Katarahalli had 27.22 per cent illiterates, 19.53 per cent of them had primary school education, 10.06 per cent of them had middle school education, 13.02 per cent of them had high school education, 15.98 per cent of them had PUC education, 1.18 per cent of them had Diploma, 1.78 per cent of them had ITI, 7.10 per cent of them had degree education and 0.59 per cent of them did Masters. The results indicate that, 50 per cent of household heads were practicing agriculture, 47.06 per cent of the household heads were agricultural labourers and 2.94 per cent of the household heads were in Trade & Business. The results indicate that agriculture was the major occupation for 26.04 per cent of the household members, 33.14 per cent were agricultural labourers, 1.78 per cent were in government service, 5.33 per cent were in private service, 2.37 per cent were in trade and business, 22.49 per cent were students, 2.96 per cent were housewives and 3.55 per cent were children. The results show that, 0.59 per cent of the population in the micro watershed has participated in Gram Panchayat, Dairy Cooperative and Raitha Sangha. The results indicate that 64.71 per cent of the households possess katcha house, 14.71 per cent of them possess pucca/RCC house and 20.59 per cent of them possess semi pucca house. The results show that 76.47 per cent of the households possess TV, 2.94 per cent of the households possess DVD/VCD Player, 58.82 per cent of them possess mixer/grinder, 2.94 per cent of the households possess Refrigerator, 26.47 per cent of them possess bicycle, 52.94 per cent of the households possess motor cycle, 2.94per cent of the households possess Computer/Laptop and 91.18 per cent of the households possess mobile phones. 2 The results show that the average value of television was Rs 5,807, DVD/VCD Player mixer was Rs 2,000, grinder was Rs 2,350, Refrigerator was Rs 12,000, bicycle was Rs 5,425, motor cycle was Rs.38,250 mobile phone was Rs. 1,998 and Computer/Laptop was Rs. 45,000. About 5.88 per cent of the households possess bullock cart, 26.47 per cent of them possess plough, 2.94 per cent of them possess Irrigation Pump, 5.88 per cent possess tractor, 17.65 per cent of them possess sprayer, 2.94 per cent of them possess Sprinkler, 76.47 per cent of them possess weeder, 23.53 per cent of them possess Thresher, 2.94 per cent of them possess chaff cutter and 2.94 per cent of them possess earth remover/duster. The results show that the average value of bullock cart was Rs. 17,500, plough was Rs. 1,820, Irrigation Pump was Rs.7,000, tractor was Rs 350,000, sprayer was Rs.3,828, Sprinkler was Rs. 8,000, average value of weeder was Rs. 159, Harvester was Rs. 84,000, Thresher was Rs. 277, average value of chaff cutter was 3,000 and the average value of earth mover/duster was Rs. 15,000. The data regarding the Livestock possession by the households in Katarahalli micro-watershed is presented in Table 13. The results indicate that, 8.82 per cent of the households possess bullocks and 17.65 per cent of the households possess local cow, 23.53 per cent of the households possess Crossbred cow, 5.88 per cent of the households possess Buffalo and 5.88 per cent of the households possess Goat. The results indicate that, average own labour men available in the micro watershed was 1.41, average own labour (women) available was 1.19, average hired labour (men) available was 13.55 and average hired labour (women) available was 11.23. The results indicate that, 91.18 per cent of the households opined that the hired labour was adequate and 5.88 per cent of the households opined that the hired labour was inadequate. The results indicate that, households of the Katarahalli micro-watershed possess 17.74 ha (36.01 %) of dry land and 31.51 ha (63.99 %) of irrigated land. Marginal farmers possess 3.30 ha (85.80 %) of dry land and 0.55 ha (14.20 %) of irrigated land. Small farmers possess 5.65 ha (52.11 %) of dry land and 5.19 ha (47.89 %) of irrigated land. Semi medium farmers possess 5.90 ha (58.47 %) of dry land and 4.19 ha (41.53 %) of irrigated land. Medium farmers possess 2.89 ha (16.56 %) of dry land and 14.56 ha (83.44 %) of irrigated land. Large farmers possess 7.03 ha (100 %) of irrigated land. The results indicate that, the average value of dry land was Rs. 431,108.83 and the average value of irrigated land was Rs. 382,220.36. In case of marginal famers, the average land value was Rs. 726,470.58 for dry land and Rs. 1,646,666.67. In case of small famers, the average land value was Rs. 398,101.72 3 for dry land and Rs. 731,566.65 for irrigated land. In case of semi medium famers, the average land value was Rs. 237,336.99 for dry land and Rs. 441,497.59 for irrigated land. In case of medium farmers, the average land value was Rs. 553,501.40 for dry land and Rs. 322,651.47 for irrigated land. In case of large farmers, the average land value was Rs. 113,824.88 for irrigated land. The results indicate that, there were 22 functioning and 17 de-functioning bore wells in the micro watershed. The results indicate that, bore well was the major irrigation source in the micro water shed for 64.71 per cent of the farmers. The results indicate that, the depth of bore well was found to be 48.68 meters. The results indicate that marginal, small, semi medium, medium farmers and large farmers had an irrigated area of 1.09 ha, 7.31 ha, 4.96 ha, 11.91 ha and 2.83 ha respectively. The results indicate that, farmers have grown Bajra (2.91 ha), Bengal gram (2.18 ha), Cotton (5.26 ha), Drumstick (1.26 ha), Groundnut (3.79 ha), maize (12.01 ha), Sorghum (0.88 ha), Onion (1.76 ha), Red gram (.96 ha), bajra (2.91 ha) and Pearl millet (8.10 ha). The results indicate that, the cropping intensity in Katarahalli micro-watershed was found to be 74.72 per cent. The results indicate that, the total cost of cultivation for bajra was Rs. 47751.46. The gross income realized by the farmers was Rs. 33186.64. The net income from maize cultivation was Rs. -14564.81. Thus the benefit cost ratio was found to be 1: 0.69. The results indicate that, the total cost of cultivation for bengal gram was Rs. 51818.36. The gross income realized by the farmers was Rs. 74236.52. The net income from green gram cultivation was Rs. 22418.17. Thus the benefit cost ratio was found to be 1: 1.43. The results indicate that, the total cost of cultivation for cotton was Rs. 318503.50. The gross income realized by the farmers was Rs. 78255.19. The net income from mango cultivation was Rs. -240248.31. Thus the benefit cost ratio was found to be 1: 0.25. The results indicate that, the total cost of cultivation for groundnut was Rs. 57538.09. The gross income realized by the farmers was Rs. 52673.65. The net income from groundnut cultivation was Rs. -4864.44. Thus the benefit cost ratio was found to be 1: 0.92. The results indicate that, the total cost of cultivation for Onion was Rs. 113731.40. The gross income realized by the farmers was Rs. 195184.89. The net income from Onion cultivation was Rs. 81453.49. Thus the benefit cost ratio was found to be 1: 1.72. 4 The results indicate that, the total cost of cultivation for maize was Rs. 46264.13. The gross income realized by the farmers was Rs. 107609.70. The net income from maize cultivation was Rs. 61345.57. Thus the benefit cost ratio was found to be 1: 2.33. The results indicate that, the total cost of cultivation for sorghum was Rs. 51344.94. The gross income realized by the farmers was Rs. 23903.22. The net income from sorghum cultivation was Rs. -27441.72. Thus the benefit cost ratio was found to be 1: 0.47. The results indicate that, the total cost of cultivation for sunflower was Rs. 60030.25. The gross income realized by the farmers was Rs. 32057.96. The net income from sunflower cultivation was Rs. -27972.29. Thus the benefit cost ratio was found to be 1: 0.53. The results indicate that, the total cost of cultivation for Drumstick was Rs. 40072.33. The gross income realized by the farmers was Rs. 223096.78. The net income from Drumstick cultivation was Rs. 183024.45. Thus the benefit cost ratio was found to be 1: 5.57. The results indicate that, the total cost of cultivation for redgram was Rs. 78158.55. The gross income realized by the farmers was Rs. 61688.25. The net income from redgram cultivation was Rs. -16470.30. Thus the benefit cost ratio was found to be 1: 0.79. The results indicate that, the total cost of cultivation for Cowpea was Rs. 29807.28. The gross income realized by the farmers was Rs. 38285. The net income from Cowpea cultivation was Rs. 8477.72. Thus the benefit cost ratio was found to be 1: 1.28. The results indicate that, 26.47 per cent of the households opined that dry fodder was adequate, 23.53 per cent of the households opined that green fodder was adequate and dry fodder was inadequate for 17.65 per cent of the households. The results indicate that the annual gross income was Rs. 2,400 for landless households, for marginal farmers it was Rs. 89,571.43, for small farmers it was Rs. 184,770, for semi medium farmers it was Rs. 103,200, for medium farmers it was Rs. 126,208.33 and for semi large farmers it was Rs. 138,000. The results indicate that the average annual expenditure is Rs. 18,960.57. For landless households it was Rs. 600, for marginal farmers it was Rs. 14,234.69, for small farmers it was Rs. 24,260, for semi medium farmers it was Rs. 22,400, for medium farmers it was Rs. 12,069.44 and for large farmers it was Rs. 115,000. The results indicate that, sampled households have grown 68 coconut trees in their field and1 in backyard, 3 Custard apple trees in their field, 5 Guava trees in their field, 10 Lemon trees in their field, 19 Mango trees in their field, 4 Pomegranate in their field and 3 lime in their field. . 5 The results indicate that, households have planted 40 Teak, 47 neem, 2 tamarind and 7 banyan trees in their field. Also, 1 neem trees in their backyard. The results indicated that, Bengalgram, Cow Pea, Drumstick, Garlic, cotton and Redgram were sold to the extent of 100 per cent, bajra was sold to the extent of 91.83 per cent, Groundnut was sold to the extent of 40.38 per cent, Maize was sold to the extent of 98.01 per cent, Onion was sold to the extent of 64.14 per cent, Sunflower was sold to the extent of 62.5 per cent and sorghum was sold to the extent of 86.67 per cent. The results indicated that, about 61.76 per cent of the farmers sold their produce to local/village merchants and 2.94 per cent of the farmers sold their produce to regulated market, 8.82 per cent of the farmers sold their produce to Cooperative marketing Society and 52.94 per cent of the farmers sold their produce to Agent/Traders. The results indicated that 100 per cent of the households used tractor as a mode of transportation for their agricultural produce. The results indicated that, 41.18 per cent of the households have experienced soil and water erosion problems in the farm. The results indicated that, 47.06 per cent have shown interest in soil test. The results indicated that, 73.53 per cent of the households used firewood, 20.59 per cent of the households used LPG as a source of fuel and 2.94 per cent of the households used Kerosene and Biogas as a source of fuel . The results indicated that, piped supply was the major source of drinking water for 67.65 per cent of the households and bore well was the source of drinking water for 32.35 per cent of the households in micro watershed. The results indicated that, Electricity was the major source of light for 100 per cent of the households in micro watershed. The results indicated that, 41.18 per cent of the households possess sanitary toilet facility. The results indicated that, 97.06 per cent of the sampled households possessed BPL card and 2.94 per cent of the households did not possess PDS card. The results indicated that, 55.88 per cent of the households participated in NREGA programme. The results indicated that, cereals were adequate for 67.65 per cent of the households, pulses were adequate for 41.18 per cent, oilseeds were adequate for 61.76 per cent, vegetables were adequate for 100 per cent, milk was adequate for 88.24 per cent, Fruits was adequate for 11.76 per cent, eggs were adequate for 29.41 per cent and meat was adequate for 47.06 per cent. The results indicated that, Cereals were inadequate for 32.35 per cent of the households, pulses were inadequate for 58.82 per cent of the households, oilseeds were inadequate for 38.24 per cent, fruits were inadequate for 50 per cent, milk 6 was inadequate for 2.86 per cent, eggs were inadequate for 35.29 per cent and meat was inadequate for 20.59 per cent of the households. The results indicated that, lower fertility status of the soil was the constraint experienced by 44.12 per cent of the households, wild animal menace on farm field (50 %), frequent incidence of pest and diseases (47.06 %), inadequacy of irrigation water (35.29 %), high cost of fertilizers and plant protection chemicals (52.94 %), high rate of interest on credit (47.06 %), low price for the agricultural commodities (35.29 %), lack of marketing facilities in the area (50 %), lack of transport for safe transport of the agricultural produce to the market (20.59 %), less rainfall (38.24 %) and source of agri-technology information (26.47 %). ; Watershed Development Department, Government of Karnataka (World Bank Funded) Sujala –III Project
Not Available ; The land resource inventory of Kanakapura-1 microwatershed was conducted using village cadastral maps and IRS satellite imagery on 1:7920 scale. The false colour composites of IRS imagery were interpreted for physiography and these physiographic delineations were used as base for mapping soils. The soils were studied in several transects and a soil map was prepared with phases of soil series as mapping units. Random checks were made all over the area outside the transects to confirm and validate the soil map unit boundaries. The soil map shows the geographic distribution and extent, characteristics, classification, behavior and use potentials of the soils in the microwatershed. The present study covers an area of 410 ha in Koppal taluk and district, Karnataka. The climate is semiarid and categorized as drought - prone with an average annual rainfall of 662 mm, of which about 424 mm is received during south–west monsoon, 161 mm during north-east and the remaining 77 mm during the rest of the year. An area of 79 per cent is covered by soils, 1 per cent by rock outcrops, 14 per cent by mining/industrial, 1 per cent by railway and 5 per cent is by water bodies. The salient findings from the land resource inventory are summarized briefly below. The soils belong to 16 soil series and 23 soil phases (management units) and 6 Land Management Units. The length of crop growing period is 150 cm). About 8 per cent area in the microwatershed has sandy soils, 27 per cent area in the microwatershed has loamy soils and 44 per cent clayey soils at the surface. About 61 per cent area has non-gravelly (200 mm/m) in available water capacity. About 21 per cent area of the microwatershed is nearly level (0-1% slope) lands, 54 per cent area of the microwatershed is very gently sloping (1-3% slope) lands and 4 per cent area of microwatershed is gently sloping (3-5% slope) lands. An area of about 29 per cent area is moderately (e2) eroded and about 50 per cent area is slightly (e1) eroded. An area of about 69 per cent soil are moderately alkaline to strongly alkaline (pH 7.8-9.0) and 10 per cent soil are very strongly alkaline (pH >9.0) in soil reaction. The Electrical Conductivity (EC) of the soils in the entire cultivated area of the microwatershed is dominantly 0.75%) in 51 per cent area. An area of about 14 per cent is medium (23-57 kg/ha) and 64 per cent is high (>57 kg/ha) in available phosphorus. An area of about 51 per cent is medium (145-337 kg/ha) and 28 per cent is high (>337 kg/ha) in available potassium. Available sulphur is medium (10 -20 ppm) in 1 per cent and high (>20 ppm) in 78 per cent area of the microwatershed. Available boron is low (1.0 ppm) in 4.5 ppm) and 21 per cent is deficient (1.0 ppm) in available manganese content. Entire cultivated area is sufficient (>0.2 ppm) in available copper content. Entire cultivated area is sufficient (>0.6 ppm) in available zinc content. The land suitability for 31 major crops grown in the microwatershed was assessed and the areas that are highly suitable (S1) and moderately suitable (S2) are given below. It is however to be noted that a given soil may be suitable for various crops but what specific crop to be grown may be decided by the farmer looking to his capacity to invest on various inputs, marketing infrastructure, market price and finally the demand and supply position. Land suitability for various crops in the microwatershed Crop Suitability Area in ha (%) Crop Suitability Area in ha (%) Highly suitable (S1) Moderately suitable (S2) Highly suitable (S1) Moderately suitable (S2) Sorghum 18(4) 103(25) Sapota 18(4) 84(20) Maize 18(4) 113(27) Pomegranate 18(4) 138(34) Bajra 21(5) 148(36) Guava 16(4) 85(21) Groundnut - 183(45) Jackfruit 18(4) 84(20) Sunflower 18(4) 102(25) Jamun 2(<1) 154(13) Cotton 2(<1) 119(29) Musambi 18(4) 138(34) Red gram 18(4) 102(25) Lime 18(4) 138(34) Bengalgram 54(13) 70(17) Cashew 16(4) 108(26) Chilli 21(5) 46(11) Custard apple 21(5) 276(67) Tomato 21(5) 46(11) Amla 21(5) 276(67) Brinjal 64(16) 216(52) Tamarind 2(<1) 115(28) Onion 61(15) 219(53) Marigold 18(4) 104(25) Bhendi 61(15) 219(53) Chrysanthemum 18(4) 104(25) Drumstick 18(4) 217(53) Jasmine 18(4) 50(12) Mulberry 18(4) 275(67) Crossandra 18(4) 49(12) Mango 2(<1) 61(15) Apart from the individual crop suitability, a proposed crop plan has been prepared for the 6 identified LMUs by considering only the highly and moderately suitable lands for different crops and cropping systems with food, fodder, fibre and other horticulture crops. Maintaining soil-health is vital for crop production and conserves soil and land resource base for maintaining ecological balance and to mitigate climate change. For this, several ameliorative measures have been suggested for these problematic soils like saline/alkali, highly eroded, sandy soils etc., Soil and water conservation treatment plan has been prepared that would help in identifying the sites to be treated and also the type of structures required. As part of the greening programme, several tree species have been suggested to be planted in marginal and submarginal lands, field bunds and also in the hillocks, mounds and ridges. That would help in supplementing the farm income, provide fodder and fuel, and generate lot of biomass which in turn would help in maintaining the ecological balance and contribute to mitigating the climate change. SALIENT FINDINGS OF THE SURVEY The data indicated that there were 94 (53.11%) men and 83 (46.89%) women among the sampled households. The average family size of landless farmers' was 5, marginal farmers' was 4.08, small farmers' was 3.60, semi medium farmers' was 5.10 and medium farmers' was 7.50. The data indicated that, 27 (15.25%) people were in 0-15 years of age, 90 (50.85%) were in 16-35 years of age, 48 (27.12%) were in 36-60 years of age and 6 (6.78%) were above 61 years of age. The results indicated that Kanakapura-1 had 32.20 per cent illiterates, 20.34 per cent of them had primary school education, 7.34 per cent of them had middle school education, 20.34 per cent of them had high school education, 9.60 per cent of them had PUC education, 2.82 per cent of them had ITI, 3.95 per cent had degree education, 0.56 per cent of master and 2.82 per cent others. The results indicate that, 91.89 per cent of household heads were practicing agriculture and 8.11 per cent of household heads were practicing agriculture labour. The results indicate that agriculture was the major occupation for 40.68 per cent of the household members, 21.47 per cent were agricultural labourers, 0.56 per cent of the households industry, 2.82 per cent were in private service, 23.16 per cent were in students, 6.78 per cent were housewives and 2.26 per cent were in children. The results show that, 100 per cent of the population in the micro watershed has not participated in any local institutions. The results indicate that, 51.35 per cent of the households possess katcha house, 8.11 per cent of the households possess pucca/RCC house and 40.54 per cent of them possess semi pacca house The results show that 100 per cent of the households possess TV, 48.65 per cent of them possess mixer/grinder, 8.11 per cent of them possess bicycle, 45.95 per cent of the households possess motor cycle and 45.95 per cent of them possess mobile phones. The results show that the average value of television was Rs. 7,299, mixer grinder was Rs. 1,625, bicycle was 1,000, motor cycle was Rs. 47,352, and mobile phone was Rs. 2,078. About 8.11 per cent of the households possess plough, 2.70 per cent of them power tiller, 5.41 per cent of them tractor, 2.70 per cent of them possess sprayer and 37.84 per cent of them possess weeder. The results show that the average value of plough was Rs. 1,400, power tiller was Rs. 200000, tractor was Rs. 550,000, sprayer was Rs. 5000 and the average value of weeder was Rs.29. 2 The results indicate that, 18.92 per cent of the households possess bullocks, 29.73 per cent of the households possess local cow, 8.11 per cent possess crossbreed cow and buffalo respectively and 2.70 per cent possess sheep. The results indicate that, average own labour men available in the micro watershed was 1.31, average own labour (women) available was 1.27, average hired labour (men) available was 3.41 and average hired labour (women) available was 2.92. The results indicate that, households of the Kanakapura-1 micro-watershed possess 11.55 ha (31.70%) of dry land and 24.87 ha (68.30%) of irrigated land. Marginal farmers possess 5.70 ha (71.91%) of dry land and 2.23(28.09%) of irrigated land. Small farmers possess 3.78 ha (77.92%) of dry land and 1.07 ha (22.08%) of irrigated land. Semi medium farmers possess 2.06 ha (13.81%) of dry land and 12.88 ha (86.19%) of irrigated land. Medium farmers possess 8.70 ha (100%) of irrigated land. The results indicate that, the average value of dry land was Rs. 701,261.82 and the average value of irrigated land was Rs. 548,576.31. In case of marginal famers, the average land value was Rs. 929,758.50 for dry land and Rs. 1,482,000. In case of small famers, the average land value was Rs. 554,759.35 for dry land and Rs. 1,398,113.16 for irrigated land. In case of semi medium famers, the average land value was Rs. 339,019.61 for dry land and Rs. 454,101.20 for irrigated land. In case of medium farmers, the average land value was Rs. 344,811.54 for irrigated land. The results indicate that, there were 16 functioning bore wells and 1de-functioning bore well in the micro watershed. The results indicate that, there were 5 functioning Open wells in the micro watershed. The results indicate that, bore well and open wells were the major irrigation source in the micro water shed for 43.24 per cent and 13.51per cent of the farmers. The results indicate that, the depth of bore well was found to be 35.29 meters and Open well was found to be 3.38 meters. The results indicate that marginal, small, semi medium and medium farmers had an irrigated area of 2.70 ha, 1.07 ha, 11.51ha and 10.76 ha respectively. The results indicate that, farmers have grown maize (12.13 ha), bajra (4.75 ha), cotton (4.21ha), sunflower (3.7 ha), redgram (2.24 ha), sugarcane (2.11 ha), cow pea (2.06 ha), greengram (0.85 ha), paddy (0.81), sorghum (0.81), groundnut (0.46ha), onion (0.4ha) and tomato (0.4 ha). Marginal farmers have grown bajra, redgram, greengram, sorghum and groundnut. Small farmers have grown bajra, maize, sunflower, redgram and onion. Semi medium farmers have grown bajra, cotton, maize, sunflower, sugarcane, greengram and paddy. Medium farmers have grown bajra, sunflower, maize, cowpea and sugarcane. The results indicate that, the cropping intensity in Kanakapura-1 micro-watershed was found to be 87.86 per cent. 3 The results show that 13.51 per cent of the households possess bank account and saving. The results show that 5.41 per cent of the households possess borrowing status. The results show that 25 per cent for friends/ relative and 75 per cent for Grameena bank of the households possess credit availed. The results show that Rs.51250 of the households average credit amount status. The results show that among 100 per cent of the households purpose of credit borrowed - Institutional credit for agricultural production. The results show that among 100 per cent of the households purpose of credit borrowed - private credit for agricultural production. The results show that 100 per cent of the households helped to perform timely agricultural operations. The results show that 100 per cent of the households helped to perform timely agricultural operations. The results indicate that, the total cost of cultivation for bajra was Rs. 37230.25. The gross income realized by the farmers was Rs. 46287.02. The net income from bajra cultivation was Rs. 9056.77. Thus the benefit cost ratio was found to be 1:1.24. The total cost of cultivation for bajra+redgram was Rs. 19640.10. The gross income realized by the farmers was Rs. 31238.23. The net income from bajra+redgram cultivation was Rs. 11598.13. Thus the benefit cost ratio was found to be 1:1.59. The total cost of cultivation for groundnut was Rs. 60582.20. The gross income realized by the farmers was Rs. 59800.00. The net income from groundnut cultivation was Rs. -782.20. Thus the benefit cost ratio was found to be 1:0.99. The total cost of cultivation for maize was Rs. 41256.23The gross income realized by the farmers was Rs. 50093.01. The net income from maize cultivation was Rs. 8836.78. Thus the benefit cost ratio was found to be 1:1.21. The total cost of cultivation for redgram was Rs. 35927.24. The gross income realized by the farmers was Rs. 47954.59. The net income from redgram cultivation was Rs. 12027.35. Thus the benefit cost ratio was found to be 1:1.33. The total cost of cultivation for sugarcane was Rs. 87301.14. The gross income realized by the farmers was Rs. 168979.36. The net income from sugarcane cultivation was Rs. 81678.22. Thus the benefit cost ratio was found to be 1:1.94. The total cost of cultivation for tomato was Rs. 80494.82. The gross income realized by the farmers was Rs. 55575.00. The net income from tomato cultivation was Rs. -24919.82. Thus the benefit cost ratio was found to be 1:0.69. The total cost of cultivation for onion was Rs. 55759.25. The gross income realized by the farmers was Rs. 49400. The net income from onion cultivation was Rs. - 6359.25.Thus the benefit cost ratio was found to be 1:0.89. 4 The total cost of cultivation for Sorghum was Rs. 23473.12. The gross income realized by the farmers was Rs. 29640.00.The net income from Sorghum cultivation was Rs. 6166.88. Thus the benefit cost ratio was found to be 1:1.26. The total cost of cultivation for Paddy was Rs. 47588.18. The gross income realized by the farmers was Rs. 63232.00. The net income from Paddy cultivation was Rs. 15643.82. Thus the benefit cost ratio was found to be 1:1.33. The total cost of cultivation for Sunflower was Rs. 22280.33 gross income realized by the farmers was Rs. 29640.00. The net income from Sunflower cultivation was Rs. 7359.67. Thus the benefit cost ratio was found to be 1:1.33. The total cost of cultivation for cowpea was Rs. 7811.85. The gross income realized by the farmers was Rs. 7278.98. The net income from cowpea cultivation was Rs. -532.87. Thus the benefit cost ratio was found to be 1:0.93. The total cost of cultivation for green gram was Rs. 48836. The gross income realized by the farmers was Rs. 49111.08. The net income from green gram cultivation was Rs. 275.07. Thus the benefit cost ratio was found to be 1:1.01. The total cost of cultivation for sugarcane was Rs. 22275.13. The gross income realized by the farmers was Rs. 60206.25The net income from sugarcane cultivation was Rs. 37931.12. Thus the benefit cost ratio was found to be 1:2.7. The results indicate that, 29.73 per cent of the households opined that dry fodder and 21.62 per cent of the households opined that green fodder was adequate. The results indicate that the annual gross income was Rs. 46,000.00 for landless farmers, for marginal farmers it was Rs. 51,915.38, for small farmers it was Rs. 66,200, for semi medium farmers it was Rs. 91,700 and for medium farmers it was Rs. 62,750. The results indicate that the average annual expenditure is Rs. 7,560.36. For landless households it was Rs. 3,480, for marginal farmers it was Rs. 2,494.87, for small farmers it was Rs. 10,260, for semi medium farmers it was Rs. 9,310 and for medium farmers it was Rs. 21,375. The results indicate that, sampled households have grown 90 coconut and13 mango trees in their field and 3 coconut and 1 mango trees in backyard. The results indicate that, households have planted 2 cashew and 57 neem, 1tamarind and 11banyan trees in their field. The results indicated that, cotton sugarcane and sunflower were sold to the extent of 100 per cent, tomato was sold to the extent of 93.33 per cent, bajra was sold to the extent of 71.55 per cent, redgram was sold to the extent of 62.96 per cent, greengram ground nut was sold extent of 50 per cent paddy was sold to 45 per cent and sorghum was sold to the extent of 16.67 per cent. The results indicated that, about 62.16 per cent of the farmers sold their produce to agent/traders. 37.84 per cent of the farmers sold their produce to local/village merchant and 27.03 per cent of them sold their produce through regulated market. 5 The results indicated that, 21.62 per cent of the households used head load, 29.53 per cent of the households used cart and 75.68 per cent of them used tractor as a mode of transportation for their agricultural produce. The results indicated that, 10.81 per cent of the households have experienced soil and water erosion problems in the farm. The results indicated that, 18.92 per cent have shown interest in soil test. The results indicated that, 89.19 per cent of the households used firewood and 13.51 per cent of the households used LPG as a source of fuel. The results indicated that, piped supply was the major source of drinking water for 45.95 per cent of the households, bore well was the source of drinking water for 48.65 per cent of the households and 2.70 per cent of the households used Canal/Nala in micro watershed. Electricity was the major source of light for 97.30 per cent of the households in micro watershed. The results indicated that, 45.95 per cent of the households possess sanitary toilet facility. The results indicated that, 89.19 per cent of the sampled household's possessed BPL card and 10.81 per cent not possessed card. The results indicated that, 54.05 per cent of the households participated in NREGA programme. The results indicated that, cereals were adequate for 91.89 per cent of the households, pulses were adequate for 70.27 per cent, oilseeds were adequate for 27.03 per cent, vegetables were adequate for 45.95 per cent, fruits were adequate for 54.05 per cent, milk was adequate for 72.97 per cent and meat was adequate for 54.05 per cent. The results indicated that, cereals were inadequate for 8.11 per cent of the households, pulses were inadequate for 29.73 per cent of the households, oilseeds were inadequate for 70.27 per cent, vegetables were inadequate for 54.05 per cent, fruits were inadequate for 35.14 per cent, milk were inadequate for 16.22 per cent, egg was inadequate for 37.84per cent and meat were inadequate for 35.14 per cent of the households. The results indicated that, lower fertility status of the soil was the constraint experienced by 59.46 per cent of the households, wild animal menace on farm field (43.24%),frequent incidence of pest and diseases (56.76%), inadequacy of irrigation water (16.22%), high cost of fertilizers and plant protection chemicals (54.05%), high rate of interest on credit and lack of marketing facilities in the area (48.65%), low price for the agricultural commodities (27.03%),inadequate extension services (16.22%), lack of transport for safe transport of the agricultural produce to the market (51.35%), less rainfall (51.35%) and source of Agri-technology information (24.32%) . ; Watershed Development Department, Government of Karnataka (World Bank Funded) Sujala –III Project
Not Available ; The land resource inventory of Mukkumpi-1 microwatershed was conducted using village cadastral maps and IRS satellite imagery on 1:7920 scale. The false colour composites of IRS imagery were interpreted for physiography and these physiographic delineations were used as base for mapping soils. The soils were studied in several transects and a soil map was prepared with phases of soil series as mapping units. Random checks were made all over the area outside the transects to confirm and validate the soil map unit boundaries. The soil map shows the geographic distribution and extent, characteristics, classification, behavior and use potentials of the soils in the microwatershed. The present study covers an area of about 621 ha in Koppal taluk and district, Karnataka. The climate is semiarid and categorized as drought - prone with an average annual rainfall of 662 mm, of which about 424 mm is received during south–west monsoon, 161 mm during north-east and the remaining 77 mm during the rest of the year. An area of 51 per cent is covered by soils, 41 per cent is covered by rock-out crops and 8 per cent is by others (Habitation and Settlements). The salient findings from the land resource inventory are summarized briefly below. The soils belong to 12 soil series and 15 soil phases (management units) and 7 Land Management Units. The length of crop growing period is 150 cm). An area of about 4 per cent area in the microwatershed has sandy soils, 5 per cent area in the microwatershed has loamy soils and 42 per cent clayey soils at the surface. An area of about 32 per cent area has non-gravelly (200 mm/m) in available water capacity. An area of about 2 per cent area of the microwatershed has nearly level (0-1% slope) lands and 49 per cent area of the microwatershed has very gently sloping (1-3% slope) lands. An area of about 11 per cent area is moderately (e2) eroded and about 40 per cent area is slightly (e1) eroded. An area of about 34 per cent soils are very strongly acid to slightly acid (pH 4.5-6.5), 15 per cent soils are neutral (pH 6.5-7.3) and 2 per cent soil are slightly alkaline (pH 7.3-7.8) in soil reaction. The Electrical Conductivity (EC) of the soils in the entire cultivated area of the microwatershed is 0.75%) in the entire cultivated are of the microwatershed. An area of about 35 per cent is medium (23-57 kg/ha) and 16 per cent is high (>57 kg/ha) in available phosphorus. An area of about 1 per cent is low (337 kg/ha) in available potassium. Available sulphur content is low (4.5 ppm) in the available iron content. Entire cultivated area of the microwatershed is sufficient (>1.0 ppm) in the available manganese content. Entire cultivated area of the microwatershed is sufficient (>0.2 ppm) in the available copper content. Entire cultivated area of the microwatershed is deficient (<0.6 ppm) in the available zinc content. The land suitability for 31 major crops grown in the microwatershed was assessed and the areas that are highly suitable (S1) and moderately suitable (S2) are given below. It is however to be noted that a given soil may be suitable for various crops but what specific crop to be grown may be decided by the farmer looking to his capacity to invest on various inputs, marketing infrastructure, market price and finally the demand and supply position. Land suitability for various crops in the microwatershed Crop Suitability Area in ha (%) Crop Suitability Area in ha (%) Highly suitable (S1) Moderately suitable (S2) Highly suitable (S1) Moderately suitable (S2) Sorghum 36(6) 248(40) Sapota 12(2) 55(9) Maize 12(2) 272(44) Pomegranate 12(2) 160(26) Bajra 12(2) 278(45) Guava 12(2) 55(9) Groundnut - 45(7) Jackfruit 12(2) 55(9) Sunflower 36(6) 137(22) Jamun - 173(28) Cotton 24(4) 260(42) Musambi 36(6) 137(22) Red gram 12(2) 160(26) Lime 36(6) 137(22) Bengalgram 24(4) 234(38) Cashew 12(2) 55(9) Chilli 12(2) 86(14) Custard apple 38(6) 252(41) Tomato 12(2) 86(14) Amla 13(2) 277(45) Brinjal 68(11) 216(35) Tamarind - 172(28) Onion 66(11) 32(5) Marigold 12(2) 271(44) Bhendi 66(11) 218(35) Chrysanthemum 12(2) 271(44) Drumstick 12(2) 160(26) Jasmine 12(2) 166(27) Mulberry 12(2) 160(26) Crossandra 12(2) 100(16) Mango - 66(11) Apart from the individual crop suitability, a proposed crop plan has been prepared for the 7 identified LMUs by considering only the highly and moderately suitable lands for different crops and cropping systems with food, fodder, fibre and other horticulture crops. Maintaining soil-health is vital for crop production and conserve soil and land resource base for maintaining ecological balance and to mitigate climate change. For this, several ameliorative measures have been suggested for these problematic soils like saline/alkali, highly eroded, sandy soils etc., Soil and water conservation treatment plan has been prepared that would help in identifying the sites to be treated and also the type of structures required. As part of the greening programme, several tree species have been suggested to be planted in marginal and submarginal lands, field bunds and also in the hillocks, mounds and ridges. That would help in supplementing the farm income, provide fodder and fuel, and generate lot of biomass which in turn would help in maintaining the ecological balance and contribute to mitigating the climate change. SALIENT FINDINGS OF THE STUDY Results indicated that 34 farmers were sampled in Mukkumpi-1 micro watershed among them 11 (32.35%) were marginal farmers, 13 (38.24 %) were small farmers, 13 (38.24 %) were semi medium farmers, 5 (2.94%) were medium farmers and 4 (11.76 %) landless farmers were also interviewed for the survey. The data indicated that there were 194 population households were there in the studied micro watershed. Among them 115 (59.28%) men and 78(40.21%) were women. The average family size of landless was 3, marginal farmers and semi medium farmers were 5, small farmer was 8 and large farmer was 4. On an average the family size was 6. The data indicated that 38 (19.59%) people were in 0-15 years of age, 100 (51.55%) were in 16-35 years of age, 48 (24.74%) were in 36-60 years of age and 8 (4.12 %) were above 61 years of age. The results indicated that the Mukkumpi-1 had 31.96 per cent illiterates, 1.03 per cent were functional literate, 23.71 per cent of them had primary school education, 4.64 per cent of them had both middle school, 21.13 per cent them had high school education, 6.19 per cent of them had PUC education, 1.55 per cent them had Diploma education, 0.52 per cent them had ITI, 1.55 per cent of them had degree education, 1.03 per cent of them had masters education and 6.70 per cent them had others. The results indicated that, 82.35 per cent of households practicing agriculture, 11.76 per cent of the household heads were general labour and 2.94 per cent of the household heads were doing other jobs. The results indicated that agriculture was the major occupation for 65.46 per cent of the household members, 2.58 per cent were agricultural labourers, 5.67 per cent were general labours, 0.52 percent were in household industry, 1.03 per cent of them were in private sector, 19.07 per cent of them were students and 4.64 per cent of them were children. In case of landless households 7.14 per cent were agricultural labour, 78.57 per cent were general labourers and 14.29 per cent were students. In case of marginal farmers 61.54 per cent were agriculturist, 5.77 percent was in agricultural labour, 3.85 per cent of were in private and 23.08 per cent were students. In case of small farmers 80.61 per cent of them were agriculturist and 10.20 per cent of them were students. In case of semi medium farmers 53.85 per cent of the family members were agriculturist, 3.85 per cent were agricultural labour and 42.31 per cent of them were students. In case of large farmers 50 per cent of the family members were agriculturist and 50 per cent of them were students. The results showed that 100 per cent of them have not participated in any local institutions. 2 The results indicated that 85.29 per cent of the households possess Katcha house, 5.88 per cent of the households possess Pucca house and 8.82 per cent of them possess Thatched house. The results showed that, 67.65 per cent of the households possess TV, 29.41 per cent of the households possess Mixer grinder, 23.53 per cent of the households possess bicycle, 20.59 per cent of the households possess motor cycle and 94.12 per cent of the households possess mobile phones. The results showed that the average value of television was Rs. 9000, mixer grinder was Rs.1800, bicycle was Rs.2000, motor cycle was Rs.36428 and mobile phone was Rs.2013. Results indicated that about 5.88 per cent of the households possess plough, 2.94 per cent of the households possess sprayer, 55.88 per cent of the households possess weeder and 2.94 per cent of the households possess harvester. The results showed that the average the average value of plough was Rs. 1500, the average value of sprayer was Rs. 1000, the average value of weeder was Rs. 53 and the average value of harvester was Rs. 33. The results indicated that, 20.59 per cent of the households possess local cow and 2.94 per cent of the households possess crossbred cow. 27.27 per cent of the marginal and 7.69 per cent of the small farmers possess local cow. In case of semi medium farmers 60 per cent households possess local cow and 20 per cent households possess crossbred cow. The results indicated that, average own labour men available in the micro watershed was 2.33, average own labour (women) available was 1.77, average hired labour (men) available was 6.63 and average hired labour (women) available was 5.23. In case of marginal farmers, average own labour men available was 1.73, average own labour (women) was also 1.27, average hired labour (men) was 7.00 and average hired labour (women) available was 5.55. In case of small farmers, average own labour men available was 3.08, average own labour (women) was 2.38, average hired labour (men) was 7.77 and average hired labour (women) available was 5.77. In case of semi medium farmers, average own labour men available was 1.60, average own labour (women) was 1.40, average hired labour (men) was 4.20 and average hired labour (women) available was 4.20. In large farmers average own labour men available was 3 and average own labour (women) was 1. The results indicated that, 88.24 per cent of the household opined that hired labour was adequate. The results indicated that, households of the Mukkumpi-1 micro watershed possess 23.49 ha (61.12%) of dry land and 14.94 ha (38.88%) of irrigated land. Marginal farmers possess 5.93 ha (87.99%) of dry land and 0.81 ha (12.01%) of irrigated land. Small farmers possess 15.14 ha (86.18 %) of dry land and 2.43 ha (13.82 %) of 3 irrigated land. Semi medium farmers possess 2.43 ha (32.35 %) of dry land and 5.08 ha (67.65%) of irrigated land. large farmers possess 6.62 ha (100%) of irrigated land. The results indicated that, the average value of dry land was Rs. 408,475.45 and average value of irrigated was Rs. 448,239.43. In case of marginal famers, the average land value was Rs. 623,822.53 for dry land and Rs. 1,482,000 for irrigated land. In case of small famers, the average land value was Rs. 330,213.90 for dry land Rs. 782,166.67 for irrigated land. In case of semi medium famers, the average land value was Rs. 370,500 for dry land and Rs. 511,713.15 for irrigated land. In case of medium famers, the average land value was Rs. 150,885.76 for irrigated land. The results indicated that, there were 10 functioning bore wells in the micro watershed. The results indicated that, bore well was the major irrigation source for 29.41 per cent of the farmers. The results indicated that on an average the depth of the bore well was 30.48 meters. The results indicated that, in case of marginal farmers there was 0.81 ha of irrigated land, in case of small farmers there was 2.43 ha of irrigated land, semi medium farmers were having 5.08 ha of irrigated land and large farmers were having 4.45 ha of irrigated land. On an average there were 12.77 ha of irrigated land. The results indicated that, farmers have grown bajra (7.29 ha), groundnut (3.81 ha), maize (19.43 ha), paddy (4.53 ha) and redgram (1.21 ha) in kharif season. Marginal farmers had grown bajra, maize and paddy. Small farmers had grown bajra, groundnut, maize, paddy and redgram. Semi medium farmers had grown bajra, groundnut, maize and paddy. Large farmers had grown groundnut, maize and paddy. The results indicated that, the cropping intensity in Mukkumpi-1 micro watershed was found to be 74.52 per cent. In case of marginal farmers it was 100 per cent, in small farmers it was 78.34, in semi medium farmers it was 82.26 and in medium farmers it was 42.90 per cent. The results indicated that, 47.06 per cent of the households have bank account and 8.82 per cent of the household possess savings respectively. Among marginal farmers 36.36 percent of them possess bank account and 9.09 per cent of savings. 69.23 per cent of small farmers possess bank account and 7.69 percent of them possess savings. 40 per cent of the semi medium farmers possess bank account and 100 per cent of the large category of farmers possesses bank account and also savings correspondingly. The results indicated that, 36.36 per cent of marginal, 69.23 per cent of small, 40 per cent of the semi medium and 100 per cent of large farmers have borrowed credit from different sources. The results indicated that, 6.25 per cent have availed loan in commercial bank, friends/ relatives and money lender correspondingly, 87.50 per cent have availed loan from Grameena bank. 4 The results indicated that, marginal, small, semi medium and large have availed Rs. 82,500, Rs. 132,222.22, Rs. 225,000 and Rs. 100000 respectively. Overall average credit amount availed by households in the micro watershed is 129,375. The results indicated that, 100 per cent of the households have borrowed loan from institutional source was for agriculture production. The results indicated that, 100 percent of the private credit was taken for agriculture production. Results indicated 100 percent of the households have unpaid their institutional loan. Results indicated that 100 percent of the households have unpaid their private loan. The results indicated that 100 per cent of the households were opined that the loan taken from the institutional sources was helped to perform timely agricultural operations. The results indicated that 50 per cent of the households were opined that the loan taken from the non-institutional sources was helped to perform timely agricultural operations and 50 per cent of the households were opined that loan amount was adequate to fulfill the requirement. The results indicated that, the total cost of cultivation for bajra was Rs. 33337.75. The gross income realized by the farmers was Rs. 24031.25. The net income from bajra cultivation was Rs. -9306.50, thus the benefit cost ratio was found to be 1:0.72. The results indicated that, the total cost of cultivation for maize was Rs. 46766.40. The gross income realized by the farmers was Rs. 51835.57. The net income from maize cultivation was Rs. 5069.18. Thus the benefit cost ratio was found to be 1:1.11. The results indicated that, the total cost of cultivation for paddy was Rs. 130496.14. The gross income realized by the farmers was Rs. 110018.35. The net income from paddy cultivation was Rs. -20477.79. Thus the benefit cost ratio was found to be 1:0.84. The results indicated that, the total cost of cultivation for groundnut was Rs. 60597.92. The gross income realized by the farmers was Rs. 56149.70. The net income from groundnut cultivation was Rs. -4448.22. Thus the benefit cost ratio was found to be 1:0.93. The results indicated that, the total cost of cultivation for Redgram was Rs. 40734.46. The gross income realized by the farmers was Rs. 85215.00. The net income from Redgram cultivation was Rs. 44480.54. Thus the benefit cost ratio was found to be 1:2.09. The results indicated that, 20.59 per cent of the households opined that dry fodder and green fodder was adequate respectively. The table indicated that, in landless, the average income from wage was Rs.66250 and agriculture was Rs.25000. In marginal farmers, the average income from wage was Rs.16181.82, agriculture was Rs.70945.45 and dairy farm was Rs.454.55. In small farmers, the average income from service/salary was Rs.7692.31, wage was 5 Rs.20769.23 and agriculture was Rs.156342.31. In semi medium farmers, the average income from wage was Rs.18000, agriculture was Rs.200000 and dairy farm was Rs.5000. In large farmers the average income from wage was Rs.50000 and agriculture was Rs.130000. The results indicated that, in land less farmers, the average expenditure from wage was Rs. 68333.33 and agriculture was Rs. 85000. In case of marginal farmers average expenditure from wage was Rs.27750, agriculture was Rs.46272.73 and dairy farm was Rs.2000. In small farmers the average expenditure from service/salary was Rs.75000, wage was Rs.29833.33 and agriculture was Rs.88534.46. In semi medium farmers the average expenditure from wage was Rs.27500, agriculture was Rs.107600 and dairy farm was Rs.18000. in large farmers the average expenditure from wage was Rs.25000 and agriculture was Rs.85000. The results indicated that, sampled households have grown 6 coconut trees and 3 mango tree in their field. The results indicated that, households have planted 46 neem trees in their field and also grown 1 Neem tree in the backyard. The results indicated that, Bajra, groundnut, maize and paddy crops were sold to the extent of 100 per cent. Redgram was sold to the extent of 95.83 per cent. The results indicated that, 79.41 percent of the households have sold their produce to local/village merchant and 32.35 percent of the households sold their produce in regulated markets. The results indicated that 2.94 per cent of the households have used cart as a mode of transport and 100 per cent have used tractor. The results indicated that, 41.18 per cent of the households have experienced the soil and water erosion problems i.e. 36.36 percent of marginal farmers, 53.85 per cent of small farmers, 40 per cent of semi medium farmers and 100 per cent of large farmers. The results indicated that, 88.24 per cent of the households have shown interest in soil testing. The results indicated that, 97.06 percent used fire wood as a source of fuel and 2.94 percent of the households used LPG. The results indicated that, piped supply was the source of drinking water for 20.59 per cent, 67.65 per cent of them were using bore well and 11.76 per cents of the households were using lake/tank for drinking water. The results indicated that, electricity was the major source of light for 100 per cent of the households. The results indicated that, 29.41 per cent of the households possess sanitary toilet i.e. 75 per cent of landless, 18.18 per cent of marginal, 23.08 per cent of small, 20 per cent of semi medium and 100 per cent of large had sanitary toilet facility. The results indicated that, 88.24 per cent of the sampled households possessed BPL card and 11.76 per cent of the sampled households have not possessed BPL card. 6 The results indicated that, 64.71 per cent of the households participated in NREGA programme which included 100 per cent of the landless, 54.55 percent of the marginal, 69.23 per cent of the small, 40 per cent of the semi medium and 100 percent of the large farmers. The results indicated that cereals and pulses were adequate for 97.06 per cent of the households. Oilseed, vegetables and fruits were adequate for 5.88 per cent of the households. Milk and egg were adequate for 94.12 per cent of the households and meat was adequate for 8.82 per cent of the households. The results indicated that, oilseed, vegetables and fruits were inadequate for 91.18 per cent of the households respectively. Egg was inadequate for 2.94 per cent of the households and meat was inadequate for 88.24 per cent of the households. The results indicated that, Lower fertility status of the soil was the constraint experienced by 88.24 per cent of the households, wild animal menace on farm field (67.65%), frequent incidence of pest and diseases (79.41%), inadequacy of irrigation water (82.35%), high cost of Fertilizers and plant protection chemicals (85.29%), high rate of interest on credit (82.35%), low price for the agricultural commodities (88.24%), lack of marketing facilities in the area (50%), inadequate extension services and lack of transport for safe transport of the agricultural produce to the market (44.12%). ; Watershed Development Department, Government of Karnataka (World Bank Funded) Sujala –III Project