Using CERES-maize and ENSO as decision support tools to evaluate climate-sensitive farm management practices for maize production in the northern regions of Ghana

Open Access JournalMaize (Zea mays) has traditionally been a major cereal staple in southern Ghana. Through breeding and other crop improvement efforts, the zone of cultivation of maize has now extended to the northern regions of Ghana which, hitherto, were the home to sorghum and millet as the major cereals. Maize yield in the northern Ghana is hampered by three major biophysical constraints, namely, poor soil fertility, low soil water storage capacity and climate variability. In this study we used the DSSAT crop model to assess integrated water and soil management strategies that combined the pre-season El-Niño-Southern Oscillation (ENSO)-based weather forecasting in selecting optimal planting time, at four locations in the northern regions of Ghana. It could be shown that the optimum planting date for a given year was predictable based on February-to-April (FMA) Sea Surface Temperature (SST) anomaly for the locations with R2 ranging from 0.52 to 0.71. For three out of four locations, the ENSO-predicted optimum planting dates resulted in significantly higher maize yields than the conventional farmer selected planting dates. In Wa for instance, early optimum planting dates were associated with La Nina and El Niño (Julian Days 130-150; early May to late May) whereas late planting (mid June to early July) was associated with the Neutral ENSO phase. It was also observed that the addition of manure and fertilizer improved soil water and nitrogen use efficiency, respectively, and minimized yield variability, especially when combined with weather forecast. The use of ENSO-based targeted planting date choice together with modest fertilizer and manure application has the potential to improve maize yields and also ensure sustainable maize production in parts of northern Ghana

Artificial intelligence in digital pathology: a diagnostic test accuracy systematic review and meta-analysis

Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diagnostic accuracy of AI in digital pathology images from all areas of pathology. This systematic review and meta-analysis included diagnostic accuracy studies using any type of artificial intelligence applied to whole slide images (WSIs) in any disease type. The reference standard was diagnosis through histopathological assessment and / or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We identified 2976 studies, of which 100 were included in the review and 48 in the full meta-analysis. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model. 100 studies were identified for inclusion, equating to over 152,000 whole slide images (WSIs) and representing many disease types. Of these, 48 studies were included in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was substantial heterogeneity in study design and all 100 studies identified for inclusion had at least one area at high or unclear risk of bias. This review provides a broad overview of AI performance across applications in whole slide imaging. However, there is huge variability in study design and available performance data, with details around the conduct of the study and make up of the datasets frequently missing. Overall, AI offers good accuracy when applied to WSIs but requires more rigorous evaluation of its performance.Comment: 26 pages, 5 figures, 8 tables + Supplementary material

Evaluating maize yield variability and gaps in two agroecologies in northern Ghana using a crop simulation model

Article purchased; Published online: 19 Oct 2017The yield gap and variability in maize under smallholder systems in two agroecologies in northern Ghana were evaluated using a decision support system for agrotechnology transfer (DSSAT). The model was used to assess (1) the potential yield of maize (YPOT), (2) water-limited exploitable maize yield (YWEX), (3) nitrogen-limited yield (YNI), (4) farmer practice maize yield (YCFP) and (5) proposed enhanced nutrient use yield (enhanced farmer practice; YEFP). Effect of supplementary irrigation was also assessed on YCFP and YEFP conditions. Yield gaps were determined as the difference between YPOT and YCFP or YEFP on the one hand, and between YWEX and YCFP or YEFP on the other hand. The yield gap based on potential yield ranged from 59% to 75% under CFP and narrowed to between 29% and 59% under EFP. With water-limited exploitable yields, the yield gap ranged from 53% to 65% under CFP, reducing to between 22% and 42% under EFP. The use of supplementary irrigation further reduced the yield gap. Improved fertiliser use and supplementary irrigation have the potential to increase yield and hence reduce the yield gap if effective policies and institutional structures are in place to provide farmers with credit facilities and farm inputs

Productivity of Soybean under Projected Climate Change in a Semi-Arid Region of West Africa: Sensitivity of Current Production System

The production of soybean is gaining more attention in West Africa. In light of projected changes in climate, there is a need to assess the potential impacts on yield productivity and variability among farmers. An evaluated GROPGRO module of the Decision Support System for Agro-technological Transfer (DSSAT) was used to simulate soybean productivity under both historical (1980–2009) and projected climate scenarios from multiple general circulation models (GCMs) under two representative concentration pathways (RCPs): 4.5 and 8.5. Agronomic data from 90 farms, as well as multiple soil profile data, were also used for the impact assessment. Climate change leads to a reduction (3% to 13.5% across GCMs and RCPs) in the productivity of soybean in Northern Ghana. However, elevated atmospheric carbon dioxide has the potential to offset the negative impact, resulting in increased (14.8% to 31.3% across GCMs and RCPs) productivity. The impact of climate change on yield varied widely amongst farms (with relative standard deviation (RSD) ranging between 17% and 35%) and across years (RSD of between 10% and 15%). Diversity in management practices, as well as differences in soils, explained the heterogeneity in impact among farms. Variability among farms was higher than that among years. The strategic management of cultural practices provides an option to enhance the resilience of soybean productivity among smallholder

Climate Change Impact and Variability on Cereal Productivity among Smallholder Farmers under Future Production Systems in West Africa

Agriculture inWest Africa is constrained by several yield-limiting factors, such as poor soil fertility, erratic rainfall distributions and low input systems. Projected changes in climate, thus, pose a threat since crop production is mainly rain-fed. The impact of climate change and its variation on the productivity of cereals in smallholder settings under future production systems in Navrongo, Ghana and Nioro du Rip, Senegal was assessed in this study. Data on management practices obtained from household surveys and projected agricultural development pathways (through stakeholder engagements), soil data, weather data (historical: 1980–2009 and five General Circulation Models; mid-century time slice 2040–2069 for two Representative Concentration Pathways; 4.5 and 8.5) were used for the impact assessment, employing a crop simulation model. Ensemble maize yield changes under the sustainable agricultural development pathway (SDP) wer

Opportunity Crop Profiles for the Vision for Adapted Crops and Soils (VACS) in Africa

The Vision for Adapted Crops and Soils (or “VACS”) is a movement that brings together dedicated communities and individuals from research, advocacy, and policy to shine a light on opportunities that traditional and underutilized crops provide to build more resilient and competitive food systems. VACS was launched in February 2023 in partnership with the African Union (AU), the Food and Agriculture Organization of the United Nations (FAO), and the Office of the U.S. Special Envoy for Food Security at the State Department, with an initial focus on the African continent. This report is an in-depth crop analysis designed to provide a rigorous evidence base to the global community. By conducting a holistic assessment of a variety of well- researched crops (e.g., maize, cassava, soybean and tomato), compared to a non-exhaustive list of neglected and underutilized crops (e.g., opportunity crops, such as bambara groundnut and fonio), we aim to provide actionable insights into the crops that are best equipped to provide stable and nutritious diets in the face of climate variability and extreme weather events in geographies across the continent. This piece of research is not meant to be exhaustive or exclusionary towards considering a broader set of crops. We hope this process and the findings are a stepping-stone to provide an evidence-based assessment on behalf of a global agenda. This report was produced alongside the VACS: Research in Action Report, (also known as the Summary Report) which outlines the guiding concepts of the VACS approach, overviews research conducted to date to expand the evidence base, recommends areas of focus for the movement going forward, and ways to engage in VACS

Climate Change Impacts on West African Agriculture: An Integrated Regional Assessment (CIWARA)

The West African Sub-Saharan region (Fig. 1) is home to some 300 million people, with at least 60% engaged in agricultural activity. Climate change is now recognized as a major constraint to development worldwide. While climate change primarily relates to the future, historical trends give evidence of climate change already occurring. Temperature increases of 1 to 1.5◦C have been observed over the last 30 years in West Africa (EPA Ghana, 2001; IPCC, 2007) and there are projections of further warming of the West African region in the foreseeable future (2040–2069; Fig. 2a). The impact of climate change on West African rainfall is less clear. The analysis of historical data over the last 30 years shows that, whereas some zones experienced increased rainfall by as much as 20% to 40%, other locations experienced a decline in annual rainfall by about 15%. Future projections suggest a drier western Sahel (e.g., Senegal) but a wetter eastern Sahel (e.g., Mali, Niger; Fig. 2b). The southern locations of WestAfrica (e.g., Ghana) are projected to experience no change or slight increases in annual rainfall (Hulme et al., 2001). Irrespective of whether these zones will be dryer or not, there is historical evidence of shifts in rainfall patterns with extreme events (i.e., droughts and floods) becoming more frequent (Adiku and Stone, 1995) and it is probable that this trend may persist into the future..