Suchergebnisse

Die Frage nach dem Zusammenhang zwischen Ernteschwankungen und wirtschaftlichen Wechsellagen (Konjunkturzyklen) ist seit Beginn seiner Erforschung sehr unterschiedlich beantwortet worden. Die Antworten bewegen sich zwischen den beiden Thesen einer passiven Abhängigkeit des industriellen Zyklus von der Lage der Landwirtschaft und einer passiven Abhängigkeit des landwirtschaftlichen Kreislaufes von den gewerblichen Wechsellagen. Wie der Autor in einem knappen Überblick zeigt, nehmen Pigou und Robertson an, dass der Kaufkraftzuwachs, den die Landwirtschaft bei reichen Ernten angeblich erfährt, den Aufschwung auslöst, während nach Timoschenko die niedrigen Preise landwirtschaftlicher Erzeugnisse in den Jahren reicher Ernten die Belebung der Gesamtwirtschaft nach sich zögen. Der Autor stellt ein seiner von Arthur Spiethoff angeregten Arbeit die Aufgabe, die Bedeutung der Ernteschwankungen für den Ablauf der Wechsellagen in einem bestimmten Zeitraum zu untersuchen. Zu diesem Zweck unterzieht er für die Jahre 1874 bis 1913 die Erntemengen der wichtigsten Ackerfrüchte (als Ersatz für einige Kolonialländer die Außenhandelumsätze), die Preise der landwirtschaftlichen Erzeugnisse und der industriellen Rohstoffe für sechs europäische und sechs außereuropäische Länder einer systematischen Bearbeitung.

Wichtigste Ergebnisse:
Die Wechsellagen der großen Industrieländer standen in der Vorkriegszeit in Zusammenhang mit dem Rhythmus der Schwankungen der Welternten. Ab 1874 sind in einem Zeitraum von 40 Jahren 5 Stockungen jedesmal mit einer Senkung, 5 Aufschwünge mit einer Hebung des Index der Mengen der Welternten eingeleitet worden. Daraus folgert [Brandau], daß die Ernteschwankungen unter den Kräften, welche Aufschwünge und Stockungen herbeiführten, die Bedeutung von Anstoßkräften, "Anlässen´, gehabt hätten. ("Verursachende´ Kraft billigt er ihnen mit Recht nicht zu.) Die weiteren [von Brandau] untersuchten Größen der Agrarsphäre - landwirtschaftliche Preise, Kaufkraft der landwirtschaftlichen Erzeugnisse, Geldroherträge der Ernten - weisen keinen bestimmten Zusammenhang mit den Wechsellagenbewegungen auf. Das Fehlen solcher bestimmten Beziehungen erklärt [Brandau] daraus, daß die Preise der landwirtschaftlichen Erzeugnisse keine unmittelbaren Funktionen des Ernterhythmus darstellten. Deshalb gehe es auch nicht an, deduktiv entscheiden zu wollen, ob eine Füllernte durch Vermittlung gestiegener Geldeinnahmen der Landwirte aufschwungsfördernd wirke, oder vielmehr über die Senkung des relativen Preisniveaus landwirtschaftlicher Erzeugnisse. Wie die Preisgestaltung jeweilig historisch bedingt sei, so seien auch die Wege verschieden, auf denen Erntestöße in der wirklichen Wirtschaft bis zum Mittelpunkt der Wechsellagen in den Erzeugungsmittelgewerben vordringen könnten."


Sachliche Gliederung der Datentabellen im Recherche- und Downloadsystem HISTAT (www.histat.gesis.org):

1. Erntemengen, Preise landwirtschaftlicher Erzeugnisse, Indizes für landwirtschaftliche Erzeugung; Preise, Kaufkraft und Geldwert für landwirtsch. Erzeugnisse in Deutschland (1878-1914)

2. Erntemengen, Preise landwirtschaftlicher Erzeugnisse, Indizes in England (1844-1914)

3. Erntemengen, Preise landwirtschaftlicher Erzeugnisse, Indizes für landwirtsch. Erzeugung, für Preise, Kaufkraft und Geldwert in Frankreich (1871-1914)

4. Erntemengen, Preise landwirtschaftlicher Erzeugnisse, Indizes für landwirtsch. Erzeugung, für Preise, Kaufkraft und Geldwert in Österreich und Ungarn (1881-1914)

5. Erntemengen und Index der landwirtschaftlichen Erzeugung in Rumänien (1886-1914)

6. Erntemengen, Getreidedurchschnittspreis, Index der landwirtschaftlichen Erzeugung, Index der Preise landwirtschaftl. Erzeugnissse, Preis-, Kaufkraft-, Geldwertindex landwirtsch. Erzeugung in Rußland (1871-1914)

7. Erntemengen, Getreidedurchschnittspreis, Index der landwirtschaftlichen Erzeugung, Index der Preise landwirtschaftl. Erzeugnissse, Preis-, Kaufkraft-, Geldwertindex landwirtsch. Erzeugung in den Vereinigten Staaten (1866-1914)

8. Ausfuhrmengen und Preise landwirtsch. Erzeugnisse, Index für landwirtsch. Erzeugung, Index für Preise landwirtsch. Erzeugnisse, Preis-, Kaufkraft-, Geldwertindex für landwirtsch. Erzeugnisse in Kanada (1868-1914)

9. Ausfuhrmengen, Preise in Goldpesos, landwirtschaftliche Indizes in Argentinien (1874-1913)

10. Ausfuhrmengen und Ausfuhrpreise für Kaffee in Brasilien (1871-1912)

11. Ausfuhrmengen und Ausfuhrpreise für Kaffee, Baumwolle, Reis, Weizen, Jute, Samen, Tee in Indien, Indizes für Erzeugung, Preise, Kaufkraft, Geldwert (1873-1914)

12. Erntemengen, Preise landwirtschaftlicher Erzeugnisse, landwirtschaftliche Indizes in Australien (1860-1914)

13. Index Welternten und Weltpreise (1873-1910).

Zeitreihen sind online downloadbar über HISTAT (www.histat.gesis.org).

Over half the land in the US is dedicated to agriculture, with the vast majority of all cropland cultivated in corn, wheat, or soybean. Despite continuing advances in agricultural technologies, and consistent yield growth over the twentieth century, research suggests that environmental change is already impacting agricultural yield and future changes are sure to exacerbate challenges to agricultural production. It follows that the future of US agriculture depends on the evolution of the changing climate, the relationship between crop yields and the environment, on-farm management and adaptations, the ecosystems that support agriculture, the political and economic incentives that shape what farmers grow and how they grow it, and the technology developed to improve yields. This study will focus on two pieces of the aforementioned agricultural puzzle—the relationship between crop yields and the environment, and water use management in irrigated agriculture. This study contributes to current literature by exploring trends in irrigated agriculture at the county-scale, and by examining the efficacy of Random Forest (RF) regression in predicting agricultural yield. Results from the second chapter, where we utilize exploratory mapping and data mining techniques to understand trends in irrigated agriculture in the Western US, are pending approval from the USDA-NASS and are not reported here. Alternatively, we build a practical guide to working with operator-level irrigation survey data. Results from the third chapter suggest that RF predicts US corn yields well and point to the importance of space and time in corn yield prediction, and the highly nonlinear response of corn yield to irrigation, climate, and agricultural diversity covariates. These results demonstrate the predictive capacity of RF regression to model complex corn yield responses to biophysical and landscape conditions and point to the power of building an ensemble of different models, each with their own strengths and weaknesses, to characterize and predict agricultural yield.

Theoretical basis for presenting research results in agricultural science is mathematical statistics and probability theory using empirical forms of generalization of experimental data. To improve the methods of planning field experiment and processing its data using digital technologies, we proposed to use mathematical modeling based on physical principle of balance of cause-and-effect interactions in a closed physical system as a priority option. When analyzing impact of environmental factors on crop yields, the initial provisions, the mathematical modeling of the crop yield is based, on are not associated with characteristics of crops and natural conditions, therefore, the model options are universal in application and are valid for any agricultural crop, regardless of the region of cultivation. To ensure statistically correct digital information, based on the established forms of mathematical model, the field experiment layout aimed at establishing the dependence of the crop yield on yield-forming factors should include at least 4 options for nutritional levels (NPK) with a research duration of at least 4 years. To check the accuracy of the developed crop yield model, the data of independent field experiments of Professor N.N. Semenenko with barley and winter triticale has been used. It has been determined that, in Belarus, yield-forming factors, as a result of their impact on the grain yield, are arranged in the following decreasing sequence: total dose of applied NPK º the amount of precipitation during the active phases of growing season → air temperature for the same period. Calculations have shown that decrease in the number of yield-forming factors taken into account in the mathematical model from three (food, moisture and heat) to two (food and moisture) reduces the accuracy of calculating the grain crop yield insignificantly.

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies ; As the largest agricultural country in South East Asia, Indonesia possesses enormous agriculture resources. In the last decade, the Government of Indonesia has focused on the production development of 4F crops, meaning crops for the production of Food, Feed, Fiber, and Fuel. In 2014, Indonesia had about 101 million hectares agricultural land which comprised of approximately 47 million hectares cultivated area and the remaining 54 million hectares were expandable agriculture lands. However, the expansion has to consider Indonesia Law No. 44 in Year 2009 (Undang – Undang No 4 Tahun 2009) regarding the security of sustainable food cropland that restricts the conversion of food cropland into timber forest, industry or settlements. In fact, unwanted land use land cover (LULC) change happened due to the excessive expansion of oil palm, rubber, pulpwood and mining industries particularly in East Kalimantan. Two districts that exhibit significant LULC change are Kutai Barat and Mahakam Ulu. An additional 78.5 thousand ha of rubber, 31 thousand ha of oil palm and 23.6 thousand ha of pulpwood plantations have dominated the LULC change in Kutai Barat and Mahakam Ulu districts from 1990 to 2009. Although in general, the agricultural expansion has become the main cause of unwanted LULC change and forest cover loss, these have also contributed to positive economic benefit. In order to evaluate the economic benefit of historic agricultural expansion as well as to estimate the economic benefit of future agricultural expansion, it is necessary to look thoroughly at the geographic distribution of crop yields within the districts because we would like to understand the crop yield for every agriculture production area. The issue on the existing crop statistic data is that the crop statistics are conveyed as tabular-based data and reported at the national, provincial or district level of detail. Thus, examining crop distribution in district level is certainly challenging. Hence, a spatial crop yield allocation model was applied to generate pixel-level crop yield representation of Kutai Barat and Mahakam Ulu districts in 2000 and 2009 based upon available the regional crop statistics data and the existing LULC maps and further analyze its spatial-temporal pattern within this period of time. Furthermore, an evaluation of crop yield production and the agriculture 2 expansion trend from 2000 to 2009 were applied to a 2030 land use projection from a land use change model to project the pixel level crop yield in 2030. Between 2000 and 2009, rubber plantation exhibits land expansion and followed by the increase of yield. While the expansion of oil palm in 2009 is followed by the degradation of yield. We presume this due to the oil palm plantation in 2009 is still in early harvesting stage. The accuracy of disaggregation model is highly depending on the quality of data particularly crop statistic data and LULC map. The deviation between these two data leads to the occurrence of a high error in disaggregation results. By estimating oil palm and rubber yield based on projected LULC maps in 2030, the future expansion is suggested to follow the Limited Unrestricted scenario since this scenario is able to provide highest average yield with relatively small area among other scenarios. In this manner, either government or people in Kutai Barat and Mahakam Ulu are able to gain optimal agricultural benefit without sacrificing an excessive number of land resources.

Agreeing that increased future global food demand will have to be met by production intensification rather than land use expansion (e.g. Hertel, 2011), scientists have moved to empirically analyse the causes for differences between potentially attainable yields and actually realized yields – the yield gap (e.g. van Ittersum et al., 2013, Neumann et al., 2010). In the long run, we aim at disentangling the effects of biophysical, economic and political impacts and farmers' response to them on crop yields by analysing yield gaps at regional scale in the European Union. Apart from generally improving our understanding of yield gaps and their drivers in the EU, our analysis will contribute to the integration of economic and biophysical models at a later stage of our research. As a first step towards an advanced yield gap analysis, the current paper will give an overview of yield developments in the EU27. The overview will be based on regional yield trend and yield variability estimates derived from socioeconomic panel data from the Farm Accountancy Data Network (FADN). The analysis will continue and extend the work of Ewert et al. (2005) and Reidsma et al. (2009) in terms of drawing on single farm instead of country level/farm type data, including the new EU member states and most recent years (until 2011). The EU-wide analysis of yield trends and variability will serve as a basis for the later analysis of yield gaps.