Regional suitability for agricultural intensification: a spatial analysis of the Southern Agricultural Growth Corridor of Tanzania

Nijbroek, Ravic; Andelman, Sandy J.

doi:10.1080/14735903.2015.1071548

Cited by 34 publications

(33 citation statements)

References 33 publications

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“…Analysis of agricultural potential could incorporate more economic factors, differentiate between smallholder and commercial farming practices and incorporate irrigation potential (e.g. [23]). Additional information on land ownership and title would allow a finer delineation of potential agricultural land, while the carbon estimates would be improved by rootshoot ratios specific to each vegetation type.…”

Section: Discussionmentioning

confidence: 99%

“…In the present case, the need is not to select protected areas but to identify areas for cropland expansion that meet the objectives of agricultural development and environmental conservation. Koh & Ghazoul [22] developed one model for examining trade-offs among oil palm development, rice production and carbon and biodiversity production in Indonesia, and a related approach was used to identify sustainable agricultural intensification solutions in southern Tanzania [23]. In both examples, the model assigned equal weight to each land use objective in the compromise scenarios that were analysed.…”

Section: Introductionmentioning

confidence: 99%

“…The model adopts different methods than the two aforementioned models [22,23] to estimate agricultural potential and conservation objectives, and can allocate land for multiple crops simultaneously. More fundamentally, our model is based on linear programming, which allows users to place varying weights on the different land use objectives [21].…”

Section: Introductionmentioning

confidence: 99%

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Reconciling agriculture, carbon and biodiversity in a savannah transformation frontier

Estes

Searchinger

Spiegel

et al. 2016

Phil. Trans. R. Soc. B

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Rapidly rising populations and likely increases in incomes in sub-Saharan Africa make tens of millions of hectares of cropland expansion nearly inevitable, even with large increases in crop yields. Much of that expansion is likely to occur in higher rainfall savannas, with substantial costs to biodiversity and carbon storage. Zambia presents an acute example of this challenge, with an expected tripling of population by 2050, good potential to expand maize and soya bean production, and large areas of relatively undisturbed miombo woodland and associated habitat types of high biodiversity value. Here, we present a new model designed to explore the potential for targeting agricultural expansion in ways that achieve quantitatively optimal trade-offs between competing economic and environmental objectives: total converted land area (the reciprocal of potential yield); carbon loss, biodiversity loss and transportation costs. To allow different interests to find potential compromises, users can apply varying weights to examine the effects of their subjective preferences on the spatial allocation of new cropland and its costs. We find that small compromises from the objective to convert the highest yielding areas permit large savings in transportation costs, and the carbon and biodiversity impacts resulting from savannah conversion. For example, transferring just 30% of weight from a yield-maximizing objective equally between carbon and biodiversity protection objectives would increase total cropland area by just 2.7%, but result in avoided costs of 27–47% for carbon, biodiversity and transportation. Compromise solutions tend to focus agricultural expansion along existing transportation corridors and in already disturbed areas. Used appropriately, this type of model could help countries find agricultural expansion alternatives and related infrastructure and land use policies that help achieve production targets while helping to conserve Africa's rapidly transforming savannahs. This article is part of the themed issue ‘Tropical grassy biomes: linking ecology, human use and conservation’.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reconciling agriculture, carbon and biodiversity in a savannah transformation frontier

Estes

Searchinger

Spiegel

et al. 2016

Phil. Trans. R. Soc. B

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“…Such usage could help optimize the use of soil resource and possibly (major) agronomic interventions across African countries (Vanlauwe et al 2014). These agronomic interventions could consist of: targeting degraded areas that are suitable for restoration projects, and/or targeting areas for agricultural intensification and investment by modeling crop suitability and yield gaps at the regional scale (Nijbroek and Andelman 2016), and/or assessing the nutrient gaps to predict fertilizer nutrient use efficiency.…”

Section: Usability Of Produced Mapsmentioning

confidence: 99%

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Hengl

Leenaars

Shepherd

et al. 2017

Nutr Cycl Agroecosyst

240

153

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Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0-30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable-phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms- random forest and gradient boosting, as implemented in R packages ranger and xgboost-and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40-85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatiotemporal statistical modeling framework.

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“…Scaling involves a degree of extrapolation to areas where the range of environmental variables is beyond that observed in the reference trial sites or areas with new combinations of environmental variables (Zurell et al 2012, Owens et al 2013, Mesgaran et al 2014. To a large extent the environmental conditions determine crop suitability (Nijbroek and Andelman 2015). Therefore determining environmental dissimilarity can guide spatial targeting of agronomic technologies to areas with the highest potential.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Approach for Delineating Extrapolation Domains for Sustainable Agricultural Intensification Technologies

Muthoni

Baijukya

Sseguya

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Sustainable intensification (SI) is a viable pathway to increase agricultural production and improve ecosystem health. Scaling SI technologies in locations with similar biophysical conditions enhance adoption. This paper employs novel extrapolation detection (ExeDet) algorithm and gridded bioclimatic layers to delineate extrapolation domains for improved maize variety (SC719) and inorganic fertilizers (YaraMila-CEREAL ® and YaraBela-Sulfan ® ) in Tanzania. Suitability was based on grain yields recorded in onfarm trials. The ExeDet algorithm generated three maps: (1) the dissimilarity between bioclimatic conditions in the reference trial sites and the target extrapolation domain (Novelty type-1), (2) the magnitude of novel correlations between covariates in extrapolation domain (Novelty type-2) and (3) the most limiting covariate. The novelty type1 and 2 maps were intersected and reclassified into five suitability classes. These classes were cross-tabulated to generate extrapolation suitability index (ESI) for the candidate technology package. An impact based spatial targeting index (IBSTI) was used to identify areas within the zones earmarked as suitable using ESI where the potential impacts for out scaling interventions can be maximized. Application of ESI and IBSTI is expected to guide extension and development agencies to prioritize scaling intervention based on both biophysical suitability and potential impact of particular technology package. Annual precipitation was most limiting factor in largest area of the extrapolation domain. Identification of the spatial distribution of the limiting factor is useful for recommending remedial measures to address the limiting factor that hinder a technology to achieve its full potential. The method outlined in this paper is replicable to other technologies that require extrapolation provided that representative reference trial data and appropriate biophysical grids are available.

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Regional suitability for agricultural intensification: a spatial analysis of the Southern Agricultural Growth Corridor of Tanzania

Cited by 34 publications

References 33 publications

Reconciling agriculture, carbon and biodiversity in a savannah transformation frontier

Reconciling agriculture, carbon and biodiversity in a savannah transformation frontier

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Geospatial Approach for Delineating Extrapolation Domains for Sustainable Agricultural Intensification Technologies

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