Analysis of farm performance in Europe under different climate and management conditions to improve understanding of adaptive capacity

Wageningen University, Wageningen, Gelderland, Netherlands
Climatic Change (Impact Factor: 3.43). 09/2007; 84(3):403-422. DOI: 10.1007/s10584-007-9242-7


The aim of this paper is to improve understanding of the adaptive capacity of European agriculture to climate change. Extensive
data on farm characteristics of individual farms from the Farm Accountancy Data Network (FADN) have been combined with climatic
and socio-economic data to analyze the influence of climate and management on crop yields and income and to identify factors
that determine adaptive capacity. A multilevel analysis was performed to account for regional differences in the studied relationships.
Our results suggest that socio-economic conditions and farm characteristics should be considered when analyzing effects of
climate conditions on farm yields and income. Next to climate, input intensity, economic size and the type of land use were
identified as important factors influencing spatial variability in crop yields and income. Generally, crop yields and income
are increasing with farm size and farm intensity. However, effects differed among crops and high crop yields were not always
related to high incomes, suggesting that impacts of climate and management differ by impact variable. As farm characteristics
influence climate impacts on crop yields and income, they are good indicators of adaptive capacity at farm level and should
be considered in impact assessment models. Different farm types with different management strategies will adapt differently.

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    • "However, higher crop yields did not necessarily trigger higher incomes , in the drier years the price the farmers obtain for the crops being higher. This aspect has been previously outlined in some studies conducted in Europe (Reidsma et al. 2007, 2010). "
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    ABSTRACT: The paper has in view the assessment of the impact of climate change on agriculture in the main agricultural region of Romania (Bărăgan Plain), by understanding the contextual socioeconomic factors of agriculture in the area as a key step towards climate adaptation, but also through identifying the user needs, awareness and requirements in terms of climate information. A special attention was given to the analysis of the changes in the socioeconomic and political context of Romania since 1989, the post-communist period, marked by fundamental transformations in agriculture, with collective and state property being replaced by private property. The poor development of the productive services in agriculture resulted in the degradation of land's productive potential and the intensification the adverse effects of extreme climatic phenomena, proving a strong dependency of crop yields and productivity on climate. The mid-term (2021–2050) and long-term (2071–2100) climate variability and change of some key variables affecting crop development (air temperature, precipitation, evapotranspiration), under different scenarios have been investigated in relation to the potential impacts on main crops. A set of relevant climate extreme and agro-meteorological indices was further used to estimate the potential climate change impacts on agriculture. The study was focused on the interaction with farmers, the main actors of the climate adaptation process in the area, aiming to evaluate their perception and response to climate change. The research approach was mainly done through face-to-face interviews, as farmers did not respond positively to organised meetings. An important difference was noticed in terms of adaptive capacity between the large farms with a high adaptive capacity and low subsistence farms (family-run farms), the most vulnerable category to both socioeconomic and climate change. The main climate adaptation measure considered crucial by the farmers is the rehabilitation/construction of irrigation systems. The study provides useful scientific insights which could improve the understanding of farmers and decision-makers on the potential impacts of the future climate change on crops, but also to mainstream climate adaptation actions in the agriculture policy.
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    • "An important portion of these assessments have focused on the biophysical consequences of climate change and evaluating these consequences through literature surveys (Olesen and Bindi, 2002; Lavalle et al., 2009; Olesen et al., 2011), yield response functions focusing on selected regions of Europe (Quiroga and Iglesias, 2009), or linking biophysical and statistical models for different agro-climatic regions (Iglesias et al., 2009). Other studies have assessed the economic impacts of climate change on EU agriculture by basing their methodologies on spatial-analogue approaches (Reidsma et al., 2007, 2009). Furthermore, economic indicators for Europe that integrate biophysical and economic models have primarily resulted conomics: The Open-Access, Open-Assessment E-Journal "
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    ABSTRACT: The economic effects of climate change on agriculture have been widely assessed in the last two decades. Many of these assessments are based on the integration of biophysical and agro-economic models, allowing to understand the physical and socio-economic responses of the agricultural sector to future climate change scenarios. The evolution of the bio-economic approach has gone through different stages. This review analyses its evolution: firstly, framing the bio-economic approach into the context of the assessments of climate change impacts, and secondly, by reviewing empirical studies at the global and European level. Based on this review, common findings emerge in both global and regional assessments. Among them, the authors show that overall results tend to hide significant disparities on smaller spatial scales. Furthermore, due to the effects of crop prices over yield changes, several authors highlight the need to consider endogenous price models to assess production impacts of climate change. Further, major developments are discussed: the progress made since the last two decades and the recent methods used to provide insights into modeling uncertainties. However, there are still challenges to be met. On this matter, the authors take these unresolved challenges as guidelines for future research.
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    • "l . 2008 ) . We used RURP as proxy for labor availability . The RURP , that is , the number of rural people per km 2 ( 2005 ) , was obtained from HarvestChoice ( 2012 ) . Accessibility to markets is a reliable estimator of crop areas at a regional scale ( Ramankutty 2004 ) . Crop areas can be used as proxy to determine capital intensity of farms ( Reidsma et al . 2007 ) . Low capital intensity prohibits farmers of remote terroirs to transport their crops to the market or modern inputs back to their farms ( Fortanier 2006 ) . Larger crop areas were expected closer to markets , having more capital available for investments in new technologies . Market access ( MARK ) was calculated based on the map of "
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    ABSTRACT: Geographical information systems support the application of statistical techniques to map spatially referenced crop data. To do this in the optimal way, errors and uncertainties have to be minimized that are often associated with operations on the data. This paper applies a spatial statistical approach to upscale crop yields from the field level toward the scale of Burkina Faso. Observed yields were related to the Normalized Difference Vegetation Index derived from SPOT-VEGETATION. The objective was to quantify the uncertainties at the subsequent steps. First, we applied a point pattern analysis to examine uncertainties due to the sampling network of field surveys in the country. Second, geographically weighted regression kriging (GWRK) was applied to upscale the yield observations and to quantify the corresponding uncertainty. The proposed method was demonstrated with the mapping of sorghum yields in Burkina Faso and results were compared with those from regression kriging (RK) and kriging with external drift using a local kriging neighborhood (KEDLN). The proposed method was validated with independent yield observations obtained from field surveys. We observed that the lower uncertainty range value increased by 39%, and the upper uncertainty range value decreased by 51%, when comparing GWRK with RK and KEDLN. Moreover, GWRK reduced the prediction error variance as compared to RK (20 vs. 31) and to KEDLN (20 vs. 39). We found that climate and topography had a major impact on the country’s sorghum yields. Further, the financial ability of farmers influenced the crop management and, thus, the sorghum crop yields. We concluded that GWRK effectively utilized information present in the covariate datasets and improved the accuracies of both the regional-scale mapping of sorghum yields and was able to quantify the associated uncertainty.
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