Testing Model Robustness – Variation of Farmers’ Decision-Making in an Agricultural Land-Use Model

Author(s):  
Georg Holtz ◽  
Marvin Nebel
2015 ◽  
Vol 6 (2) ◽  
pp. 1129-1162 ◽  
Author(s):  
K. F. Ahmed ◽  
G. Wang ◽  
L. You ◽  
M. Yu

Abstract. Agriculture is a key component of anthropogenic land use and land cover changes that influence regional climate. Meanwhile, in addition to socioeconomic drivers, climate is another important factor shaping agricultural land use. In this study, we compare the contributions of climate change and socioeconomic development to potential future changes of agricultural land use in West Africa using a prototype land use projection (LandPro) algorithm. The algorithm is based on a balance between food supply and demand, and accounts for the impact of socioeconomic drivers on the demand side and the impact of climate-induced crop yield changes on the supply side. The impact of human decision-making on land use is explicitly considered through multiple "what-if" scenarios. In the application to West Africa, future crop yield changes were simulated by a process-based crop model driven with future climate projections from a regional climate model, and future changes in food demand were projected using a model for policy analysis of agricultural commodities and trade. Without agricultural intensification, the climate-induced decrease in crop yield together with increase in food demand are found to cause a significant increase in agricultural land use at the expense of forest and grassland by the mid-century. The increase in agricultural land use is primarily climate-driven in the western part of West Africa and socioeconomically driven in the eastern part. Analysis of results from multiple decision-making scenarios suggests that human adaptation characterized by science-informed decision making to minimize land use could be very effective in many parts of the region.


2004 ◽  
Vol 26 (4) ◽  
pp. 537-542
Author(s):  
Yeung-Nan Shieh

One of the very important components in the urban and agricultural land use model is the so-called bid-rent curve. Regional and urban economists, city planners, and economic geographers have used this curve extensively as an analytical device. It is generally accepted that the explicit bid-rent function was first applied to the equilibrium of land use patterns in agricultural production by August Losch (1954) in Germany and Edgar S. Dunn (1954) in America, and was later extended by William Alonso (1964).


2016 ◽  
Vol 7 (1) ◽  
pp. 151-165 ◽  
Author(s):  
Kazi Farzan Ahmed ◽  
Guiling Wang ◽  
Liangzhi You ◽  
Miao Yu

Abstract. Agriculture is a key component of anthropogenic land use and land cover changes that influence regional climate. Meanwhile, in addition to socioeconomic drivers, climate is another important factor shaping agricultural land use. In this study, we compare the contributions of climate change and socioeconomic development to potential future changes of agricultural land use in West Africa using a prototype land use projection (LandPro) algorithm. The algorithm is based on a balance between food supply and demand, and accounts for the impact of socioeconomic drivers on the demand side and the impact of climate-induced crop yield changes on the supply side. The impact of human decision-making on land use is explicitly considered through multiple "what-if" scenarios. In the application to West Africa, future crop yield changes were simulated by a process-based crop model driven with future climate projections from a regional climate model, and future changes of food demand is projected using a model for policy analysis of agricultural commodities and trade. Without agricultural intensification, the climate-induced decrease in crop yield together with future increases in food demand is found to cause a significant increase in cropland areas at the expense of forest and grassland by the mid-century. The increase in agricultural land use is primarily climate-driven in the western part of West Africa and socioeconomically driven in the eastern part. Analysis of results from multiple scenarios of crop area allocation suggests that human adaptation characterized by science-informed decision-making can potentially minimize future land use changes in many parts of the region.


2013 ◽  
Vol 6 (4) ◽  
pp. 6975-7046 ◽  
Author(s):  
F. Souty ◽  
B. Dorin ◽  
T. Brunelle ◽  
P. Dumas ◽  
P. Ciais

Abstract. The central role of land-use change in the Earth System and its implications for food security, biodiversity and climate has spurred the development of global models that combine economical and agro-ecological drivers and constraints. With such a development of integrated approaches, evaluating the performance of global models of land-use against observed historical changes recorded by agricultural data becomes increasingly challenging. The Nexus Land-Use model is an example of land-use model integrating both biophysical and economical processes and constraints. This paper is an attempt to evaluate its ability to simulate historical agricultural land-use changes over 12 large but economically coherent regions of the world since 1961. The evaluation focuses on the intensification vs. extensification response of crop and livestock production in response to changes of socio-economic drivers over time, such as fertiliser price, population and diet. We examine how well the Nexus model can reproduce annual observation-based estimates of cropland vs. pasture areas from 1961 to 2006. Food trade, consumption of fertilisers and food price are also evaluated against historical data. Over the 12 regions considered, the total relative error on simulated cropland area is 2% yr−1 over 1980–2006. During the period 1961–2006, the error is larger (4% yr−1) due to an overestimation of the cropland area in China and Former Soviet Union over 1961–1980. Food prices tend to be underestimated while the performances of the trade module vary widely among regions (net imports are underestimated in Western countries at the expense of Brazil and Asia). Finally, a sensitivity analysis over a sample of input datasets provides some insights on the robustness of this evaluation.


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