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In recent decades, there have been substantial increases in crop production in sub-Saharan Africa (SSA) as a result of higher yields, increased cropping intensity, expansion of irrigated cropping systems, and rainfed cropland expansion. Yet, to date much of the research focus of the impact of climate change on crop production in the coming decades has been on crop yield responses. In this study, we analyse the impact of climate change on the potential for increasing rainfed cropping intensity through sequential cropping and irrigation expansion in central Benin. Our approach combines hydrological modelling and scenario analysis involving two Representative Concentration Pathways (RCPs), two water-use scenarios for the watershed based on the Shared Socioeconomic Pathways (SSPs), and environmental water requirements leading to sustained streamflow. Our analyses show that in Benin, warmer temperatures will severely limit crop production increases achieved through the expansion of sequential cropping. Depending on the climate change scenario, between 50% and 95% of cultivated areas that can currently support sequential cropping or will need to revert to single cropping. The results also show that the irrigation potential of the watershed will be at least halved by mid-century in all scenario combinations. Given the urgent need to increase crop production to meet the demands of a growing population in SSA, our study outlines challenges and the need for planned development that need to be overcome to improve food security in the coming decades.

Knowing where, when, and how much rice is planted and harvested is crucial information for understanding the effects of policy, trade, and global and technological change on food security. We developed RiceAtlas, a spatial database on the seasonal distribution of the world's rice production. It consists of data on rice planting and harvesting dates by growing season and estimates of monthly production for all rice-producing countries. Sources used for planting and harvesting dates include global and regional databases, national publications, online reports, and expert knowledge. Monthly production data were estimated based on annual or seasonal production statistics, and planting and harvesting dates. RiceAtlas has 2,725 spatial units. Compared with available global crop calendars, RiceAtlas is nearly ten times more spatially detailed and has nearly seven times more spatial units, with at least two seasons of calendar data, making RiceAtlas the most comprehensive and detailed spatial database on rice calendar and production.

Agricultural land suitability analysis (ALSA) for crop production is one of the key tools for ensuring sustainable agriculture and for attaining the current global food security goal in line with the Sustainability Development Goals (SDGs) of United Nations. Although some review studies addressed land suitability, few of them specifically focused on land suitability analysis for agriculture. Furthermore, previous reviews have not reflected on the impact of climate change on future land suitability and how this can be addressed or integrated into ALSA methods. In the context of global environmental changes and sustainable agriculture debate, we showed from the current review that ALSA is a worldwide land use planning approach. We reported from the reviewed articles 69 frequently used factors in ALSA. These factors were further categorized in climatic conditions (16), nutrients and favorable soils (34 of soil and landscape), water availability in the root zone (8 for hydrology and irrigation) and socio-economic and technical requirements (11). Also, in getting a complete view of crop's ecosystems and factors that can explain and improve yield, inherent local socio-economic factors should be considered. We showed that this aspect has been often omitted in most of the ALSA modeling with only 38% of the total reviewed article using socio-economic factors. Also, only 30% of the studies included uncertainty and sensitivity analysis in their modeling process. We found limited inclusions of climate change in the application of the ALSA. We emphasize that incorporating current and future climate change projections in ALSA is the way forward for sustainable or optimum agriculture and food security. To this end, qualitative and quantitative approaches must be integrated into a unique ALSA system (Hybrid Land Evaluation System - HLES) to improve the land evaluation approach. • Methods used in agricultural land suitability analysis (ALSA) strengths and limitations • Uncertainty and sensitivity analysis inclusion in ALSA modeling process • Climate change analysis inclusion in ALSA task • Frequently used predictors in ALSA modeling including biophysical, socioeconomic and management practices [ABSTRACT FROM AUTHOR]

Parasitic weeds pose increasing threats to rain-fed rice production in Africa. Most important species are Striga asiatica , S. aspera and S. hermonthica in rain-fed uplands, and Rhamphicarpa fistulosa in rain-fed lowlands. Information on the regional spread and economic importance of parasitic weeds in cereal production systems is scant. This article presents the first multi-species, multi-country, single-crop impact assessment of parasitic weeds in Africa. A systematic search of public international and national herbaria and the scientific literature was conducted to collect all available data on the regional distribution, incidences and related yield losses of the most important parasitic weeds in rice. Herbaria specimens were geo-referenced and these coordinates were overlapped with rain-fed rice areas. Probabilistic diffusion waves of parasitic weeds were generated to derive most likely incidence values. Estimates from this spatial analysis were then combined with secondary data from the literature into a stochastic impact assessment model to generate a confidence interval of the likely economic impact per country and for sub-Saharan Africa as a whole. Rhamphicarpa fistulosa occurs in at least 36 African countries, 28 of which produce rice in rain-fed lowlands where this species thrives. Striga hermonthica is found in at least 32 countries, Striga asiatica in at least 44 and S. aspera in at least 17. A total of 50 countries have at least one of these three species of Striga , 31 of which produce rice in the rain-fed uplands where these species can be encountered. An estimated 1.34 million ha of rain-fed rice is infested with at least one species of a parasitic weed in Africa. Our stochastic model estimates that annual economic losses inflicted by all parasitic weeds exceeds, with 95% certainty, a minimum value of US $111 million and most likely reaches roughly US $200 million and increases by US $30 million annually. To reverse this trend and support small-holder rice farmers in Africa with effective, sustainable and affordable solutions for control, targeted investments in research, development and capacity building are required. The top-10 priority countries where such investments would probably have the highest return are Nigeria, Guinea, Mali, Côte d’Ivoire, Cameroon, Tanzania, Madagascar, Uganda, Sierra Leone and Burkina Faso. [ABSTRACT FROM AUTHOR]

Water availability and high soil fertility make inland valley landscapes suitable for sustainable rice-based cropping. In this study, Random Forests statistical analysis was used on a database of 499 surveyed inland valleys in four study zones in three West African countries. The goal of the study was to assess parameters that indicate (are predictors for) high potential for development of rice-based systems in inland valleys. These parameters are related to the biophysical (hydrology, soil, climate, and topography) and socio-economic (demography, accessibility, and markets) environments. Farmer group surveys and secondary data from existing publicly available spatial data sets were used. The analysis revealed that, across the four research areas, the following parameters were relevant predictors for rice development: (1) distance from the inland valley to the nearest market; (2) distance from the inland valley to the nearest rice mill; (3) population density in the immediate environment of the inland valley; (4) total nitrogen in the top 20 cm of the soil profile; (5) land elevation; and (6) soil texture on the upper slope of the inland valley. Several predictors were highly important for specific research areas, but not for all, thus showing the diversity in the studied agricultural landscapes. These predictors included soil fertility management, source of irrigation water, and the percentage of female farmers in the inland valley. The identified relevant predictors will be used to map the potential rice production development of the inland valleys. This will help development agencies to assess their zones based on quantitative analysis for inland valley potential development. [ABSTRACT FROM AUTHOR]