Simulating the effects of tax exemptions on fertiliser use in Benin by linking biophysical and economic models

Kuhn, Arnim; Gaiser, Thomas; Gandonou, Esaïe

doi:10.1016/j.agsy.2010.05.003

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…In the assessment of optimal rates of nitrogen application as well as in the assessment of nitrogen taxes, the representation of farmers' goal function in bioeconomic models is often focused on (the maximization of) net farm profits (e.g. Berntsen et al, 2003, Hartmann et al, 2008, Kienzler et al, 2011, Kuhn et al, 2010, Meyer-Aurich et al, 2010, Rajsic and Weersink, 2008, Zekri and Herruzo, 1994.…”

Section: Introductionmentioning

confidence: 99%

Nitrogen use and the effects of nitrogen taxation under consideration of production and price risks

Finger

2012

Agricultural Systems

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Section: Introductionmentioning

confidence: 99%

Nitrogen use and the effects of nitrogen taxation under consideration of production and price risks

Finger

2012

Agricultural Systems

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“…It indicates the maximum amount of outputs that can be produced with alternate amounts of variable inputs used in combination with one or more fixed input under a given state of technology. Moreover, it allows explaining the output value generated either by the whole economic based or drivers' combination of factors determining the existing technology (Kuhn, 2010). A factor of production refers to an aggregate of nature (such as water, minerals and forests), human limit (both mental and physical) and a wide range of man-made guides to advance production, (devices, machines and structures).…”

Section: Conceptmentioning

confidence: 99%

Factors Impeding Effective Crop Production in Ethiopia

Merga¹,

Haji²

2019

JAS

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In the Ethiopia crop agriculture, pillar in balancing the food diet of the majority people, as a whole and the cereals specifically is facing serious and vast challenges which in turn affect the supply of food grains. Various research institutes in Ethiopia have revealed several factors which affect crop production over the past three decades, but their findings are not synthesized into a whole and difficult to access as such. This study attempts to review various existing research findings and present them holistically. The most common factors are lack of more recently introduced improved seeds, initial capital for investment, loss of cropland, labor, pesticides, invasive alien species, farm storage techniques, methods of small scale irrigation, and religious and cultural challenges. Thorough understanding these constraints, researchers and policy makers can make useful recommendations to crop growers’ on farm improvement system and not on how to devastate well designed sustainable crop production systems. Succinct and collated scientific information would help to shade light on the best standards to overcome most factors affecting crop production and enhance the productivity and quality of crops. Moreover, this paper offers more implications and recommendations for various stakeholders in Ethiopian and similar contexts.

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“…effects of elevated atmospheric CO2 concentration on plant growth (Stoeckle et al, 1992), detailed soil organic matter cycling (Izaurralde et al, 2006, Izaurralde et al, 2012, and an extended number of crop types and cultivars (e.g. Kiniry et al, 1995;Gaiser et al, 2010) among others (Gassman 2004).…”

Section: The Environmental Policy Integrated Climate Field Scale Modelmentioning

confidence: 99%

“…More recently, the MFW has been setup for applications in sub-Saharan Africa based on a regional calibration (Gaiser et al, 2010) and thoroughly evaluated in various studies (e.g. Folberth et al, 2012;Folberth et al, 2014;van der Velde et al, 2014).…”

Section: Gepicmentioning

confidence: 99%

“…All these hydrologic factors contribute to differences among the EPIC-MFWs in inter-annual yield variability when comparing columns one and two or three and four of Figure 5 and Figure S2 As the parameterizations of the two EPIC-MFWs were exchanged collectively, however, other parameters discussed above for the whole ensemble such as plant stress thresholds for fertilizer application and coefficients 590 for microbial turnover of soil organic matter (SOM) also play a role here. The upper limit for denitrification was set very low in GEPIC due to prior applications for sub-Saharan Africa based on a calibration of the EPIC model for the region (Gaiser et al, 2010), whereas denitrification was allowed to be high in EPIC-IIASA. The microbial decay rate, a coefficient for turnover rates of SOM, in contrast, was strongly limited in EPIC-IIASA and set to a default value corresponding to no limitations due to microbial activity in GEPIC.…”

Section: Impact Of Single Setup Domains On Maize Yield Estimatesmentioning

confidence: 99%

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Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates

Folberth

Elliott

Müller

et al. 2016

Preprint

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Global gridded crop models (GGCMs) combine field-scale agronomic models or sets of plant growth algorithms with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities 40 at the global scale. Differences in GGCM outputs arise from the use of different bio-physical models, setups, and input data. While algorithms have been in the focus of recent GGCM comparisons, this study investigates differences in maize and wheat yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison (GGCMI) project. Albeit using the same crop model, the GGCMs differ in model version, 45 input data, management assumptions, parameterization, geographic distribution of cultivars, and selection of subroutines e.g. for the estimation of potential evapotranspiration or soil erosion. The analyses reveal long-term trends and inter-annual yield variability in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. Absolute yield levels as well depend not only on nutrient supply but also on the parameterization and distribution of crop cultivars. All GGCMs show an intermediate performance 50in reproducing reported absolute yield levels or inter-annual dynamics. Our findings suggest that studies focusing on the evaluation of differences in bio-physical routines may require further harmonization of input data and management assumptions in order to eliminate background noise resulting from differences in model setups. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for bracketing such uncertainties as long as comprehensive global datasets 55 taking into account regional differences in crop management, cultivar distributions and coefficients for parameterizing agro-environmental processes are lacking. Finally, we recommend improvements in the documentation of setups and input data of GGCMs in order to allow for sound interpretability, comparability and reproducibility of published results. 60

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Simulating the effects of tax exemptions on fertiliser use in Benin by linking biophysical and economic models

Cited by 9 publications

References 10 publications

Nitrogen use and the effects of nitrogen taxation under consideration of production and price risks

Nitrogen use and the effects of nitrogen taxation under consideration of production and price risks

Factors Impeding Effective Crop Production in Ethiopia

Uncertainties in global crop model frameworks: effects of cultivar distribution, crop management and soil handling on crop yield estimates

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