Search

Two of the key limitations for sustainably increasing agricultural production are the scarcity of land and fresh water resources. Establishing land and water productivity gaps is, therefore, essential for measuring how efficiently these resources are being utilised and assessing the scope for increasing feed and food production. Monitoring the productivity gaps at large scales or over time using field data is challenging and expensive. Remote sensing offers an alternative data source to reveal spatial and temporal variations in productivity. This paper presents a framework that integrates remote sensing derived data and field data to assess (1) land and water productivity gaps, (2) bright spots – fields exhibiting land- and water productivity equal to or higher than the target, and (3) net irrigation water demand for increasing production. The framework is developed and applied to the Xinavane sugarcane estate in Mozambique, demonstrating its practical application through systematic evaluation on a 6637 ha section of the estate divided by different irrigation application methods. The results reveal that the productivity gap is the highest on fields irrigated by furrow (13.1 tonnes (ton) per ha), followed by sprinkler (12.6 ton/ha) and centre pivot (9.4 ton/ha). Bridging the productivity gap on the same cropland results in an increased sugarcane production of 12.5 % requiring 8.5 % additional irrigation water, whereas achieving the same production increase through irrigation expansion requires more blue water. The analyses show that remote sensing provides a viable source of information to diagnose the productivity constraints and how bright spots can provide insights into the best field management practices to overcome them. The framework demonstrates its usefulness for policy makers and stakeholders to make informed decisions on the scarce blue water allocation for enhancing agricultural production. [Display omitted] • Remote-sensing derived WaPOR is used to map productivity gap and irrigation demand for gap closure. • Distinguishing water consumption into green and blue supports net irrigation assessment. • Bright spots in a cropland reveal potential best management practices for productivity improvement. • Explore closing productivity gaps before expanding irrigation for increasing production. [ABSTRACT FROM AUTHOR]

Purpose: Accelerating the adoption of intensification methods is critical for improving African agricultural productivity growth. This study synthesizes the effect of determinants underlying the speed of adoption of agricultural intensification methods in Africa. Design/Methodology/Approach: A systematic literature search was conducted using online databases, covering 20 years of publication research. Eleven studies met the inclusion criteria and were included in the meta-analysis. Findings: After controlling for innovation type, a subgroup analysis reveals that gender and formal education influence the speed of adoption of improved staple crop varieties and other agricultural intensification methods. Practical implications: Continued investment in approaches that are gender sympathetic and in agricultural education and extension is implied. Theoretical implications: Accelerating agricultural productivity growth requires a broad understanding of the potential determinants. This extends beyond farmer characteristics and includes factors that describe the context of agricultural systems, policies, and technology. Originality/Value: This is the first study quantifying the effect size of factors that influence the pace of adoption of agricultural intensification methods in Africa. [ABSTRACT FROM AUTHOR]

Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross-correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re-analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of bias-adjustment methods. The results show that multivariate BA treatment should be preferred to univariate ones. The error obtained by comparing crop simulation obtained with AgMERRA with those obtained with multivariate bias-adjusted climate prediction is systematically lower. The error reduction varies as a function of the variable, the location, the crop model, and the climate model though the tendency is for smaller errors when multivariate methods are used to treat the latter. The results are attributed to the nature of crop models and the fact that multivariate methods consider more adequately the correlation existing between the meteorological variables. The study shows the importance of considering the nature of a model and the selection of input data that best suited to the former. In this case the improvements produced when using multivariate data appears to be significant especially in the light of the variety of crop models used and the similar response obtained and it is therefore recommended. [Display omitted] • The impact of climate data multivariate bias-adjustment methods versus univariate on crop model results was estimated. • Crop model results improved when input data was treated using multivariate methods compared to univariate methods. • Multivariate methods maintain the variables correlation as required by crop models. • This result is attributed to the parameterized nature of the participating crop-model formulations. • Climate, bias adjustment and the crop modelling communities joined efforts in this unprecedented collaboration. [ABSTRACT FROM AUTHOR]

Understanding the extent and adapting to the impacts of climate change in the agriculture sector in Africa requires robust data on which technical and policy decisions can be based. However, there are no publicly available comprehensive data of which crops are suitable where under current and projected climate conditions for impact assessments and targeted adaptation planning. We developed a dataset on crop suitability of 23 major food crops (eight cereals, six legumes & pulses, six root & tuber crops, and three in banana-related family) for rainfed agriculture in Africa in terms of area and produced quantity. This dataset is based on the EcoCrop model parameterized with temperature, precipitation and soil data and is available for the historical period and until mid-century. The scenarios used for future projections are SSP1:RCP2.6, SSP3:RCP7.0 and SSP5:RCP8.5. The dataset provides a quantitative assessment of the impacts of climate change on crop production potential and can enable applications and linkages of crop impact studies to other socioeconomic aspects, thereby facilitating more comprehensive understanding of climate change impacts and assessment of options for building resilience. [ABSTRACT FROM AUTHOR]