International Crops Research Institute for the Semi-Arid Tropics

Although global food demand is expected to increase 60% by 2050 compared with 2005/2007, the rise will be much greater in sub-Saharan Africa (SSA). Indeed, SSA is the region at greatest food security risk because by 2050 its population will increase 2.5-fold and demand for cereals approximately triple, whereas current levels of cereal consumption already depend on substantial imports. At issue is whether SSA can meet this vast increase in cereal demand without greater reliance on cereal imports or major expansion of agricultural area and associated biodiversity loss and greenhouse gas emissions. Recent studies indicate that the global increase in food demand by 2050 can be met through closing the gap between current farm yield and yield potential on existing cropland. Here, however, we estimate it will not be feasible to meet future SSA cereal demand on existing production area by yield gap closure alone. Our agronomically robust yield gap analysis for 10 countries in SSA using location-specific data and a spatial upscaling approach reveals that, in addition to yield gap closure, other more complex and uncertain components of intensification are also needed, i.e., increasing cropping intensity (the number of crops grown per 12 mo on the same field) and sustainable expansion of irrigated production area. If intensification is not successful and massive cropland land expansion is to be avoided, SSA will depend much more on imports of cereals than it does today.

Population growth, arable land and fresh water limits, and climate change have profound implications for the ability of agriculture to meet this century's demands for food, feed, fiber, and fuel while reducing the environmental impact of their production. Success depends on the acceptance and use of contemporary molecular techniques, as well as the increasing development of farming systems that use saline water and integrate nutrient flows.

Numerous studies have been published during the past two decades that use simulation models to assesscrop yield gaps (quantified as the difference between potential and actual farm yields), impact of climatechange on future crop yields, and land-use change. However, there is a wide range in quality and spatialand temporal scale and resolution of climate and soil data underpinning these studies, as well as widelydiffering assumptions about cropping-system context and crop model calibration. Here we present anexplicit rationale and methodology for selecting data sources for simulating crop yields and estimatingyield gaps at specific locations that can be applied across widely different levels of data availability andquality. The method consists of a tiered approach that identifies the most scientifically robust require-ments for data availability and quality, as well as other, less rigorous options when data are not availableor are of poor quality. Examples are given using this approach to estimate maize yield gaps in the stateof Nebraska (USA), and at a national scale for Argentina and Kenya. These examples were selected torepresent contrasting scenarios of data availability and quality for the variables used to estimate yieldgaps. The goal of the proposed methods is to provide transparent, reproducible, and scientifically robustguidelines for estimating yield gaps; guidelines which are also relevant for simulating the impact of cli-mate change and land-use change at local to global spatial scales. Likewise, the improved understandingof data requirements and alternatives for simulating crop yields and estimating yield gaps as describedhere can help identify the most critical “data gaps” and focus global efforts to fill them. A related paper(Van Bussel et al., 2015) examines issues of site selection to minimize data requirements and up-scalingfrom location-specific estimates to regional and national spatial scales.

Yield gap analysis, which evaluates magnitude and variability of difference between crop yield potential (Yp) or water limited yield potential (Yw) and actual farm yields, provides a measure of untapped food production capacity. Reliable location-specific estimates of yield gaps, either derived from research plots or simulation models, are available only for a limited number of locations and crops due to cost and time required for field studies or for obtaining data on long-term weather, crop rotations and management practices, and soil properties. Given these constraints, we compare global agro-climatic zonation schemes for suitability to up-scale location-specific estimates of Yp and Yw, which are the basis for estimating yield gaps at regional, national, and global scales. Six global climate zonation schemes were evaluated for climatic homogeneity within delineated climate zones (CZs) and coverage of crop area. An efficient CZ scheme should strike an effective balance between zone size and number of zones required to cover a large portion of harvested area of major food crops. Climate heterogeneity was very large in CZ schemes with less than 100 zones. Of the other four schemes, the Global Yield Gap Atlas Extrapolation Domain (GYGA-ED) approach, based on a matrix of three categorical variables (growing degree days, aridity index, temperature seasonality) to delineate CZs for harvested area of all major food crops, achieved reasonable balance between number of CZs to cover 80% of global crop area and climate homogeneity within zones. While CZ schemes derived from two climate-related categorical variables require a similar number of zones to cover 80% of crop area, within-zone heterogeneity is substantially greater than for the GYGA-ED for most weather variables that are sensitive drivers of crop production. Some CZ schemes are crop-specific, which limits utility for up-scaling location-specific evaluation of yield gaps in regions with crop rotations rather than single crop species.

Advances in next-generation sequencing and genotyping technologies have enabled generation of large-scale genomic resources such as molecular markers, transcript reads and BAC-end sequences (BESs) in chickpea, pigeonpea and groundnut, three major legume crops of the semi-arid tropics. Comprehensive transcriptome assemblies and genome sequences have either been developed or underway in these crops. Based on these resources, dense genetic maps, QTL maps as well as physical maps for these legume species have also been developed. As a result, these crops have graduated from 'orphan' or 'less-studied' crops to 'genomic resources rich' crops. This article summarizes the above-mentioned advances in genomics and genomics-assisted breeding applications in the form of marker-assisted selection (MAS) for hybrid purity assessment in pigeonpea; marker-assisted backcrossing (MABC) for introgressing QTL region for drought-tolerance related traits, Fusarium wilt (FW) resistance and Ascochyta blight (AB) resistance in chickpea; late leaf spot (LLS), leaf rust and nematode resistance in groundnut. We critically present the case of use of other modern breeding approaches like marker-assisted recurrent selection (MARS) and genomic selection (GS) to utilize the full potential of genomics-assisted breeding for developing superior cultivars with enhanced tolerance to various environmental stresses. In addition, this article recommends the use of advanced-backcross (AB-backcross) breeding and development of specialized populations such as multi-parents advanced generation intercross (MAGIC) for creating new variations that will help in developing superior lines with broadened genetic base. In summary, we propose the use of integrated genomics and breeding approach in these legume crops to enhance crop productivity in marginal environments ensuring food security in developing countries.

One of the greatest challenges humanity faces is feeding the world's human population in a sustainable, nutritious, equitable and ethical way under a changing climate. Urgent transformations are needed that allow farmers to adapt and develop while also being climate resilient and contributing minimal emissions. This paper identifies several illustrative adaptation and development pathways, recognising the variety of starting points of different types of farmers and the ways their activities intersect with global trends, such as population growth, climate change, rapid urbanisation dietary changes, competing land uses and the emergence of new technologies. The feasibility of some pathways depends on factors such as farm size and land consolidation. For other pathways, particular infrastructure, technology, access to credit and market access or collective action are required. The most viable pathway for some farmers may be to exit agriculture altogether, which itself requires careful management and planning. While technology offers hope and opportunity, as a disruptor, it also risks maladaptations and can create tradeoffs and exacerbate inequalities, especially in the context of an uncertain future. For both the Sustainable Development Goals and the 2015 Paris Agreement to be achieved, a mix of levers that combine policy, technology, education and awareness-raising, dietary shifts and financial/economic mechanisms is required, attending to multiple time dimensions, to assist farmers along different pathways. Vulnerable groups such as women and the youth must not be left behind. Overall, strong good governance is needed at multiple levels, combining top-down and bottom-up processes.

To understand the genetic basis of tolerance to drought and heat stresses in chickpea, a comprehensive association mapping approach has been undertaken. Phenotypic data were generated on the reference set (300 accessions, including 211 mini-core collection accessions) for drought tolerance related root traits, heat tolerance, yield and yield component traits from 1-7 seasons and 1-3 locations in India (Patancheru, Kanpur, Bangalore) and three locations in Africa (Nairobi, Egerton in Kenya and Debre Zeit in Ethiopia). Diversity Array Technology (DArT) markers equally distributed across chickpea genome were used to determine population structure and three sub-populations were identified using admixture model in STRUCTURE. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations (r2; when r2<0.20) was found to decay rapidly with the genetic distance of 5 cM. For establishing marker-trait associations (MTAs), both genome-wide and candidate gene-sequencing based association mapping approaches were conducted using 1,872 markers (1,072 DArTs, 651 single nucleotide polymorphisms [SNPs], 113 gene-based SNPs and 36 simple sequence repeats [SSRs]) and phenotyping data mentioned above employing mixed linear model (MLM) analysis with optimum compression with P3D method and kinship matrix. As a result, 312 significant MTAs were identified and a maximum number of MTAs (70) was identified for 100-seed weight. A total of 18 SNPs from 5 genes (ERECTA, 11 SNPs; ASR, 4 SNPs; DREB, 1 SNP; CAP2 promoter, 1 SNP and AMDH, 1SNP) were significantly associated with different traits. This study provides significant MTAs for drought and heat tolerance in chickpea that can be used, after validation, in molecular breeding for developing superior varieties with enhanced drought and heat tolerance.

Accurate estimation of yield gaps is only possible for locations where high quality local data are available, which are, however, lacking in many regions of the world. The challenge is how yield gap estimates based on location-specific input data can be used to obtain yield gap estimates for larger spatial areas. Hence, insight about the minimum number of locations required to achieve robust estimates of yield gaps at larger spatial scales is essential because data collection at a large number of locations is expensive and time consuming. In this paper we describe an approach that consists of a climate zonation scheme supplemented by agronomical and locally relevant weather, soil and cropping system data. Two elements of this methodology are evaluated here: the effects on simulated national crop yield potentials attributable to missing and/or poor quality data and the error that might be introduced in scaled up yield gap estimates due to the selected climate zonation scheme. Variation in simulated yield potentials among weather stations located within the same climate zone, represented by the coefficient of variation, served as a measure of the performance of the climate zonation scheme for upscaling of yield potentials. We found that our approach was most appropriate for countries with homogeneous topography and large climate zones, and that local up-to-date knowledge of crop area distribution is required for selecting relevant locations for data collection. Estimated national water-limited yield potentials were found to be robust if data could be collected that are representative for approximately 50% of the national harvested area of a crop. In a sensitivity analysis for rainfed maize in four countries, assuming only 25% coverage of the national harvested crop area (to represent countries with poor data availability), national water-limited yield potentials were found to be over- or underestimated by 3 to 27% compared to estimates with the recommended crop area coverage of ≥50%. It was shown that the variation of simulated yield potentials within the same climate zone is small. Water-limited potentials in semi-arid areas are an exception, because the climate zones in these semi-arid areas represent aridity limits of crop production for the studied crops. We conclude that the developed approach is robust for scaling up yield gap estimates from field, i.e. weather station data supplemented by local soil and cropping system data, to regional and national levels. Possible errors occur in semi-arid areas with large variability in rainfall and in countries with more heterogeneous topography and climatic conditions in which data availability hindered full application of the approach.

Abstract Dry‐land legumes, well adapted to drought‐prone areas, have largely been neglected in the past despite the good opportunities they offer for income growth and food (and nutritional) security for the poor. This study evaluated the adoption and impact of two farmer and market‐preferred and disease‐resistant pigeonpea varieties that were developed and promoted in semi‐arid Tanzania. The new varieties were resistant to fusarium wilt, a fungal disease devastating the crop. However, farmers wanting to adopt new varieties did not adopt due to seed access constraints and under‐developed seed delivery systems. Adoption of new varieties is therefore analyzed using an augmented double hurdle model that allows estimating variety adoption conditional on seed access thresholds accounting for the additional information on sample separation. The study identifies the crucial role of seed access (local supply), extension, education, participatory decision making, capital, and household assets in determining adoption. The social economic benefits of the technology and policies for improved seed access were further analyzed using the extended economic surplus method (DREAM model). Even under restrictive assumptions, overall discounted benefits were found to be quite attractive, indicating the need for additional efforts to scale‐up the success story. Analysis of changes in research benefits from relaxing the seed access constraint showed that net gains would increase by up to 30% if farmer access to improved seeds can be assured. Smallholder farmers are the major beneficiaries along with consumers and rural net‐buyers who gain from productivity‐induced lower market prices.

Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community.

SUMMARY This study examines farmers’ perceptions of short- and long-term variability in climate, their ability to discern trends in climate and how the perceived trends converge with actual weather observations in five districts of Eastern Province in Kenya where the climate is semi-arid with high intra- and inter-annual variability in rainfall. Field surveys to elicit farmers’ perceptions about climate variability and change were conducted in Machakos, Makueni, Kitui, Mwingi and Mutomo districts. Long-term rainfall records from five meteorological stations within a 10 km radius from the survey locations were obtained from the Kenya Meteorological Department and were analysed to compare with farmers’ observations. Farmers’ responses indicate that they are well aware of the general climate in their location, its variability, the probabilistic nature of the variability and the impacts of this variability on crop production. However, their ability to synthesize the knowledge they have gained from their observations and discern long-term trends in the probabilistic distribution of seasonal conditions is more subjective, mainly due to the compounding interactions between climate and other factors such as soil fertility, soil water and land use change that determine the climate's overall influence on crop productivity. There is a general tendency among the farmers to give greater weight to negative impacts leading to higher risk perception. In relation to long-term changes in the climate, farmer observations in our study that rainfall patterns are changing corroborated well with reported perceptions from other places across the African continent but were not supported by the observed trends in rainfall data from the five study locations. The main implication of our findings is the need to be aware of and account for the risk during the development and promotion of technologies involving significant investments by smallholder farmers and exercise caution in interpreting farmers’ perceptions about long-term climate variability and change.

We analyse the impact of improved chickpea adoption on welfare in Ethiopia using three rounds of panel data. First, we estimate the determinants of improved chickpea adoption using a double hurdle model. We apply a control function approach with correlated random effects to control for possible endogeneity resulting from access to improved seed and technology transfer activities. To instrument for these variables we develop novel distance weighted measures of a household's neighbours' access to improved seed and technology transfer activities. Second, we estimate the impact of area under improved chickpea cultivation on household income and poverty. We apply a fixed effects instrumental variables approach where we use the predicted area under cultivation from the double hurdle model as an instrument for observed area under cultivation. We find that improved chickpea adoption significantly increases household income while also reducing household poverty. Finally, we disaggregate results by landholding to explore whether the impact of adoption has heterogeneous effects. Adoption favoured all but the largest landholders, for who the new technology did not have a significant impact on income. Overall, increasing access to improved chickpea appears a promising pathway for rural development in Ethiopia's chickpea growing regions.

Agriculture in Africa is expected to meet the dual objectives of providing food and helping people to escape poverty. African agriculture is dominated by smallholdings and donors generally target their agricultural support at the smallholder sector. The expectation is that if the gap between actual and potential yields can be closed, smallholders will grow sufficient crops to feed their families, with a surplus to sell, thus meeting food security needs and bringing in an income to move them out of poverty. In practice, this is often not possible. While technologies already exist that can raise smallholder farmers' yields 3 or 4 times, even under rainfed conditions, the small size of land available to them limits how much can be grown and the per capita income from agriculture is insufficient to allow people to move above the current World Bank-defined poverty line of US$1.90 per day. We link this finding with farmer typologies to further explain that there are large differences between individual farming households themselves in terms of their investment incentives and capability to benefit from field-level technologies that are aimed at increasing farm productivity. We argue for more differentiated policies for agricultural development in Africa and suggest that policymakers should be much more aware of the heterogeneity of farms and target interventions accordingly. It is important to understand where and for whom agriculture will have the main purpose of ensuring food and nutritional security and where and for whom there is the potential for significant increases in incomes and a contribution to wider economic growth. Let us recognize the distinctiveness of these targets and underlying target groups and work towards solutions that address the underlying needs.

Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.

Sole-cropped, unfertilized maize is the dominant cropping system throughout southern Africa. Yields have become stagnant and legumes are frequently advocated as an affordable option for resource poor farmers, to enhance productivity. Farmer participatory research was employed to test legume intensification as a means to improve maize-based systems in Malawi. A range of options were evaluated, from grain/legume intercrops of long-duration pigeonpea (Cajanus cajan) and groundnut (Arachis hypogaea) rotated with maize (Zea mays), to a relay green manure system of maize with Tephrosia vogelii (Fishbean). Two years of on-farm experimentation indicated that under on-farm conditions legume-intensified systems produced residues that contained about 50 kg N/ha per year, two-fold higher than sole-cropped maize residues. Grain yields from legume-intensified systems were comparable to yields from continuous sole maize, even in a dry lakeshore ecology. These preliminary findings were linked to farmer assessment, where farmers participating in the trials expressed strong interest in the technologies. Yet the probability of adoption remains uncertain. Associated surveys outlined constraints and trade-offs underlying technology choice, information that is not usually considered in conjunction with on-farm trials. Although the legumes were highly productive, farmers expressed worries about the marginal loss of maize production. While the trial performance was similar across regions, differences in market condition, farm resources and household composition appears to stimulate different technology choices. Farmers weigh the benefits of weed suppression and potential cash earnings, against the costs of seed, problems of seed access, labor requirements and problems of grain market access and price. Surveyed farmers commonly manage residues by burning. Promotion and experimentation with more efficient use of legume residues may offer higher short-term impacts than efforts to promote adoption of another cash crop. Ultimately, adoption and soil fertility benefits may depend on market returns to legume production. This study documents the value of researchers and farmers partnering in evaluation of technologies, adoption constraints and competing technology choices.

Abstract This article evaluates the impact of adoption of improved pigeonpea technologies on consumption expenditure and poverty status using cross-sectional data of 613 households from rural Tanzania. Using multiple econometric techniques, we found that adopting improved pigeonpea significantly increases consumption expenditure and reduces poverty. This confirms the potential role of technology adoption in improving household welfare as higher incomes translate into lower poverty. This study supports broader investment in agriculture research to address vital development challenges. Reaching the poor with better technologies however requires policy support for improving extension efforts, access to seeds and market outlets that stimulate adoption.

In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coherent and complete maps of the rootable depth and RZ-PAWHC of soil in SSA. We compiled geo-referenced data from 28,000 soil profiles from SSA, which were used as input for digital soil mapping (DSM) techniques to produce soil property maps of SSA. Based on these soil properties, we developed and parameterised (pedotransfer) functions, rules and criteria to evaluate soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootability (relative to threshold values) and rootable depth. Maps of these secondary soil properties were derived using the primary soil property maps as input for the evaluation rules and the results were aggregated over the rootable depth to obtain a map of RZ-PAWHC, with a spatial resolution of 1 km2. The mean RZ-PAWHC for SSA is 74 mm and the associated average root zone depth is 96 cm. Pearson correlation between the two is 0.95. RZ-PAWHC proves most limited by the rootable depth but is also highly sensitive to the definition of field capacity. The total soil volume of SSA potentially rootable by maize is reduced by one third (over 10,500 km3) due to soil conditions restricting root zone depth. Of these, 4800 km3 are due to limited depth of aeration, which is the factor most severely limiting in terms of extent (km2), and 2500 km3 due to sodicity which is most severely limiting in terms of degree (depth in cm). Depth of soil to bedrock reduces the rootable soil volume by 2500 km3, aluminium toxicity by 600 km3, porosity by 120 km3 and alkalinity by 20 km3. The accuracy of the map of rootable depth and thus of RZ-PAWHC could not be validated quantitatively due to absent data on rootability and rootable depth but is limited by the accuracy of the primary soil property maps. The methodological framework is robust and has been operationalised such that the maps can easily be updated as additional data become available.

European Union retailers are setting global benchmarks for the production of fresh food and are asking their suppliers for produce to be certified according to food safety and quality standards. Compliance with these standards for developing countries' small-scale producers entails costly investment in variable inputs and long- term structures. Limited empirical evidence exists either to refute or confirm the concern that the proliferation and enhanced stringency of these standards marginalize smallholders from the global market. This paper therefore explores the costs of compliance, factors explaining the smallholder decision to adopt EU private quality standards, and the impacts of the standards on farm financial performance. We develop a 2-stage standard treatment effect model to account for self-selection as a source of endogeneity. Analysis is based on a random cross section sample of 439 small-scale export vegetable producers in Kenya whose production was monitored in 2005–2006. We demonstrate that adopters and nonadopters are distinguishable by their asset holding and household wealth, access to services, labor endowment, and level of education. Once we control for the endogeneity problem, we find that small-scale producers can benefit substantially from adopting the standards at the farm level.

Peanut is an important and nutritious agricultural commodity and a livelihood of many small-holder farmers in the semi-arid tropics (SAT) of world which are facing serious production threats. Integration of genomics tools with on-going genetic improvement approaches is expected to facilitate accelerated development of improved cultivars. Therefore, high-resolution genotyping and multiple season phenotyping data for 50 important agronomic, disease and quality traits were generated on the 'reference set' of peanut. This study reports comprehensive analyses of allelic diversity, population structure, linkage disequilibrium (LD) decay and marker-trait association (MTA) in peanut. Distinctness of all the genotypes can be established by using either an unique allele detected by a single SSR or a combination of unique alleles by two or more than two SSR markers. As expected, DArT features (2.0 alleles/locus, 0.125 PIC) showed lower allele frequency and polymorphic information content (PIC) than SSRs (22.21 alleles /locus, 0.715 PIC). Both marker types clearly differentiated the genotypes of diploids from tetraploids. Multi-allelic SSRs identified three sub-groups (K = 3) while the LD simulation trend line based on squared-allele frequency correlations (r2) predicted LD decay of 15-20 cM in peanut genome. Detailed analysis identified a total of 524 highly significant MTAs (p value > 2.1 × 10-6) with wide phenotypic variance (PV) range (5.81-90.09%) for 36 traits. These MTAs after validation may be deployed in improving biotic resistance, oil/ seed/ nutritional quality, drought tolerance related traits, and yield/ yield components.

Grain legumes form an important component of the human diet, provide feed for livestock, and replenish soil fertility through biological nitrogen fixation. Globally, the demand for food legumes is increasing as they complement cereals in protein requirements and possess a high percentage of digestible protein. Climate change has enhanced the frequency and intensity of drought stress, posing serious production constraints, especially in rainfed regions where most legumes are produced. Genetic improvement of legumes, like other crops, is mostly based on pedigree and performance-based selection over the past half century. To achieve faster genetic gains in legumes in rainfed conditions, this review proposes the integration of modern genomics approaches, high throughput phenomics, and simulation modelling in support of crop improvement that leads to improved varieties that perform with appropriate agronomy. Selection intensity, generation interval, and improved operational efficiencies in breeding are expected to further enhance the genetic gain in experimental plots. Improved seed access to farmers, combined with appropriate agronomic packages in farmers' fields, will deliver higher genetic gains. Enhanced genetic gains, including not only productivity but also nutritional and market traits, will increase the profitability of farming and the availability of affordable nutritious food especially in developing countries.