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Irrigated Acreage Projections in Georgia AgEcon
Cai, Ruohong; Mullen, Jeffrey D.; Bergstrom, John C..
Irrigated acreage is an important indicator for agricultural water demand which is a major category of water use. Three methodologies were applied in this study to project irrigated acreage of major crops in Georgia from 2010 to 2050. These three methodologies show consistent results. Total irrigated acreage of major crops in Georgia is projected to increase for the next 40 years. The acreage projection results provide useful information for Georgia agricultural policy makers and farmers. However, the methodologies used in the study have some limitations. They can only be used under certain assumptions. Thus, better methodologies are needed for future related research.
Tipo: Conference Paper or Presentation Palavras-chave: Irrigated acreage projection; Acreage response elasticities; Crop Production/Industries; Environmental Economics and Policy; Farm Management; Resource /Energy Economics and Policy.
Ano: 2010 URL: http://purl.umn.edu/56468
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A Dynamic Optimal Crop Rotation Model in Acreage Response AgEcon
Cai, Ruohong; Bergstrom, John C.; Mullen, Jeffrey D.; Wetzstein, Michael E..
This paper presents a dynamic crop rotation model that shows how crop yield and price volatility could impact crop mix and acreage response under crop rotation considerations. Specifically, a discrete Markov decision model is utilized to optimize producers’ crop rotation decision within a finite horizon. By maximizing net present value of expected current and future profits, a modified Bellman equation helps develop optimum planting decisions. This model is capable of simulating crop rotations with different lengths and structures. Specifically, the corn-soybeans rotations were simulated using the crop rotation model.
Tipo: Working or Discussion Paper Palavras-chave: Crop rotation; Acreage response; Bellman equation; Crop Production/Industries; Research Methods/ Statistical Methods.
Ano: 2011 URL: http://purl.umn.edu/103949
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Assessing the Effects of Climate Change on Farm Production and Profitability: Dynamic Simulation Approach AgEcon
Cai, Ruohong; Bergstrom, John C.; Mullen, Jeffrey D.; Wetzstein, Michael E..
In this paper, a dynamic optimization model was developed to simulate how farm-level realized price and profitability respond to yield change which was induced by climate change. Producers' acreage response was included in the dynamic model considering crop rotation effect. In the crop rotation model, a modified Bellman equation was used to dynamically optimize the net present value of farm profit for a five-year interval. This simulation process was repeated through the year 2050. Then yield, price, and acreage response were compiled to generate realized profit. Results generally indicated that reduction in crop yields due to climate change results in reduced farm profitability for most of the states studied. Predicted climate change is more likely to...
Tipo: Conference Paper or Presentation Palavras-chave: Dynamic simulation model; Acreage response; Crop rotation; Expected price; Realized price; Agricultural Finance; Crop Production/Industries; Environmental Economics and Policy; Farm Management; Land Economics/Use; Production Economics; Productivity Analysis; Risk and Uncertainty.
Ano: 2011 URL: http://purl.umn.edu/103420
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Crop Price Volatility Impacts on Farmers’ Cropping Patterns: A Dynamic Optimal Crop Rotation Model AgEcon
Cai, Ruohong; Qiu, Cheng; Wetzstein, Michael E..
Tipo: Conference Paper or Presentation Palavras-chave: Crop Production/Industries.
Ano: 2010 URL: http://purl.umn.edu/61665
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Principal Component Analysis of Crop Yield Response to Climate Change AgEcon
Cai, Ruohong; Bergstrom, John C.; Mullen, Jeffrey D.; Wetzstein, Michael E.; Shurley, W. Donald.
The objective of this study is to compare the effects of climate change on crop yields across different regions. A Principal Component Regression (PCR) model is developed to estimate the historical relationships between weather and crop yields for corn, soybeans, cotton, and peanuts for several northern and southern U.S. states. Climate change projection data from three climate models are applied to the estimated PCR model to forecast crop yield response. Instead of directly using weather variables as predictor variables, the PCR model uses weather indices transformed from original weather variables by the Principal Component Analysis (PCA) approach. A climate change impact index (CCII) is developed to compare climate change effects across different...
Tipo: Working or Discussion Paper Palavras-chave: Principal component regression; Crop yield response; Climate change.; Crop Production/Industries.
Ano: 2011 URL: http://purl.umn.edu/103947
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