Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach

You, Liangzhi; Wood, Stanley; Wood‐Sichra, Ulrike

doi:10.1016/j.agsy.2008.11.003

Cited by 145 publications

(114 citation statements)

References 23 publications

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“…Use of objective skill metrics (i.e. root mean squared error, omission rate), and careful examination of crop suitability simulations against the MapSPAM crop distribution dataset 34 helped ensuring consistency with observational data. For maize, the same method was followed, though it was applied separately for each of the 6 maize mega-environments of Africa 35 .…”

Section: Methodsmentioning

confidence: 99%

Timescales of transformational climate change adaptation in sub-Saharan African agriculture

et al. 2016

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Climate change is projected to constitute a significant threat to food security, if no adaptation actions are taken 1,2 . Transformation of agricultural systems, e.g. switching crop types or moving out of agriculture, is projected to be necessary in some cases [3][4][5] .However, little attention has been paid to the timing of these transformations. Here, we develop a temporal uncertainty framework using the CMIP5 ensemble to assess when and where cultivation of key crops in Sub-Saharan African becomes unviable.We report potential transformational changes for all major crops during the 21 st century, as climates shift and areas become unsuitable. For most crops, however, transformation is limited to small pockets (<15 % of area), and only for beans, maize and banana is transformation more widespread (~30 % area for maize and banana, 60 % for beans). We envision three overlapping adaptation phases to enable projected transformational changes: an incremental adaptation phase focused on improvements to crops and management, a preparatory phase that establishes appropriate policies and enabling environments, and a transformational adaptation phase in which farmers substitute crops, explore alternative livelihoods strategies, or relocate. To best align policies with production triggers for no-regret actions, monitoring capacities to track farming systems as well as climate are needed.Agricultural activities are the main means to reduce poverty and improve food security among 850 million undernourished people 2 . Numerous studies have shown that climate change can be a significant threat to food availability and stability by reducing agricultural productivity and increasing inter-annual variations in yields 1,2,6 . Adaptation will be required if food production is to be increased in both quantity and stability in order to meet food security needs during the 21st century. A recent global meta-analysis 1 reported that decreases of ca. 5 % in crop productivity are expected for every degree of warming above historical levels, and that adapted crops yield roughly 7 % greater than non-adapted crops.Yield gains from adaptation through crop management and varietal substitution, however, are highest with moderate or low (< +3 ºC) levels of warming 1,6 , suggesting that more profound systemic and/or transformational changes may be required when and where higher levels of warming occur 5 .Transformational adaptation is defined by the IPCC 7 as a response to the effects of climate change that "changes the fundamental attributes of a system" (see Text S1 for definitions).Transformational change implies shifts in locations for production of specific crops and livestock, or shifting to farming systems new to a region or resource system 3,5 . Here, we consider one type of transformation: switching of staple crop type grown over a large geographic area of 0.3 million ha (the grid cell size of our analysis) or more. We analyze when and where major cropping systems transformations are likely to occur for important crops in Sub-Saharan A...

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Section: Methodsmentioning

confidence: 99%

Timescales of transformational climate change adaptation in sub-Saharan African agriculture

et al. 2016

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“…Estimates from western Kenya of potential and actual maize yields were 3.7 and 1.7 t ha −1 , respectively (Tittonell et al, 2008), while our results show modelled vs. reported maize yields for Kenya of 3.4 and 1.8 t ha −1 , respectively. Combining statistical, GIS, socio-economic and methodology of agro-ecological zones for large regions of the continent, You et al (2009) have estimated 3.5-5 t ha −1 as potential yields from rain-fed, high-input maize. Modelling potential optimal millet yields in the Sahel region with the ORCHIDEE DGVM, Berg et al (2011) obtained 2-5 times higher yields than FAO-reported millet yields.…”

Section: Comparing Modelled Crop Yields With Fao Statisticsmentioning

confidence: 99%

“…9a). The irrigated area in Africa is well below 5 % of the crop area (You et al, 2009), but large scale irrigation is probably less costeffective than general improvements in agricultural practices to decrease the yield gap in African regions, or as an adaptive measure to mitigate climate change impacts (Liu et al, 2008;Ziervogel et al, 2008). …”

Section: Comparing Modelled Crop Yields With Fao Statisticsmentioning

confidence: 99%

Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa

et al. 2013

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Abstract. Dynamic global vegetation models (DGVMs) are important tools for modelling impacts of global change on ecosystem services. However, most models do not take full account of human land management and land use and land cover changes (LULCCs). We integrated croplands and pasture and their management and natural vegetation recovery and succession following cropland abandonment into the LPJ-GUESS DGVM. The revised model was applied to Africa as a case study to investigate the implications of accounting for land use on net ecosystem carbon balance (NECB) and the skill of the model in describing agricultural production and reproducing trends and patterns in vegetation structure and function. The seasonality of modelled monthly fraction of absorbed photosynthetically active radiation (FPAR) was shown to agree well with satellite-inferred normalised difference vegetation index (NDVI). In regions with a large proportion of cropland, the managed land addition improved the FPAR vs. NDVI fit significantly. Modelled 1991-1995 average yields for the seven most important African crops, representing potential optimal yields limited only by climate forcings, were generally higher than reported FAO yields by a factor of 2-6, similar to previous yield gap estimates. Modelled inter-annual yield variations during 1971-2005 generally agreed well with FAO statistics, especially in regions with pronounced climate seasonality. Modelled land-atmosphere carbon fluxes for Africa associated with land use change (0.07 PgC yr −1 release to the atmosphere for the 1980s) agreed well with previous estimates. Cropland management options (residue removal, grass as cover crop) were shown to be important to the landatmosphere carbon flux for the 20th century.

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“…He defined entropy H(p) as a weighted sum of the information [34]. The entropy of a random variable with probability distribution P (p 1 , p 2 , .…”

Section: Spatial Distribution Optimization Model Based On Cross Entropymentioning

confidence: 99%

Spatial Optimization of Agricultural Land Use Based on Cross-Entropy Method

Hao

Singh

et al. 2017

Entropy

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An integrated optimization model was developed for the spatial distribution of agricultural crops in order to efficiently utilize agricultural water and land resources simultaneously. The model is based on the spatial distribution of crop suitability, spatial distribution of population density, and agricultural land use data. Multi-source remote sensing data are combined with constraints of optimal crop area, which are obtained from agricultural cropping pattern optimization model. Using the middle reaches of the Heihe River basin as an example, the spatial distribution of maize and wheat were optimized by minimizing cross-entropy between crop distribution probabilities and desired but unknown distribution probabilities. Results showed that the area of maize should increase and the area of wheat should decrease in the study area compared with the situation in 2013. The comprehensive suitable area distribution of maize is approximately in accordance with the distribution in the present situation; however, the comprehensive suitable area distribution of wheat is not consistent with the distribution in the present situation. Through optimization, the high proportion of maize and wheat area was more concentrated than before. The maize area with more than 80% allocation concentrates on the south of the study area, and the wheat area with more than 30% allocation concentrates on the central part of the study area. The outcome of this study provides a scientific basis for farmers to select crops that are suitable in a particular area.

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Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach

Cited by 145 publications

References 23 publications

Timescales of transformational climate change adaptation in sub-Saharan African agriculture

Timescales of transformational climate change adaptation in sub-Saharan African agriculture

Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa

Spatial Optimization of Agricultural Land Use Based on Cross-Entropy Method

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