Impacts of climate change on agro-climatic suitability of major food crops in Ghana

Current and future scenarios of suitability and expansion of cassava brown streak disease, Bemisia tabaci species complex, and cassava planting in Africa

Cassava (Manihot esculenta) is among the most important staple crops globally, with an imperative role in supporting the Sustainable Development Goal of 'Zero hunger'. In sub-Saharan Africa, it is cultivated mainly by millions of subsistence farmers who depend directly on it for their socio-economic welfare. However, its yield in some regions has been threatened by several diseases, especially the cassava brown streak disease (CBSD). Changes in climatic conditions enhance the risk of the disease spreading to other planting regions. Here, we characterise the current and future distribution of cassava, CBSD and whitefly Bemisia tabaci species complex in Africa, using an ensemble of four species distribution models (SDMs): boosted regression trees, maximum entropy, generalised additive model, and multivariate adaptive regression splines, together with 28 environmental covariates. We collected 1,422 and 1,169 occurrence records for cassava and Bemisia tabaci species complex from the Global Biodiversity Information Facility and 750 CBSD occurrence records from published literature and systematic surveys in East Africa. Our results identified isothermality as having the highest contribution to the current distribution of cassava, while elevation was the top predictor of the current distribution of Bemisia tabaci species complex. Cassava harvested area and precipitation of the driest month contributed the most to explain the current distribution of CBSD outbreaks. The geographic distributions of these target species are also expected to shift under climate projection scenarios for two mid-century periods (2041-2060 and 2061-2080). Our results indicate that major cassava producers, like Cameron, Ivory Coast, Ghana, and Nigeria, are at greater risk of invasion of CBSD. These results highlight the need for firmer agricultural management and climate-change mitigation actions in Africa to combat new outbreaks and to contain the spread of CBSD.

Use of modelling tools to assess climate change impacts on smallholder oil seed yields in South Africa

Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm Tool, a GHG calculator, was used to simulate yields and estimate GHG emissions from these crops, respectively. Four representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), three nitrogen (0, 75, and 150 kg/ha) and phosphorous (0, 30 and 60 P kg/ha) fertilizer rates at three sites in Limpopo, South Africa (Ofcolaco, Syferkuil and Punda Maria) were used in field trials for calibrating the models. The highest yield was achieved by sunflower across all crops, years and sites. Soybean yield is projected to decrease across all sites and scenarios by 2030 and 2050, except at Ofcolaco, where yield increases of at least 15.6% is projected under the RCP 4.5 scenario. Positive climate change impacts are predicted for groundnut at Ofcolaco and Syferkuil by 2030 and 2050, while negative impacts with losses of up to 50% are projected under RCP8.5 by 2050 at Punda Maria. Sunflower yield is projected to decrease across all sites and scenarios by 2030 and 2050. A comparison of the climate change impacts across sites shows that groundnut yield is projected to increase under climate change while notable yield losses are projected for sunflower and soybean. GHG emissions from the management of each crop showed that sunflower and groundnut production had the highest and lowest emissions across all sites respectively. With positive climate change impacts, a reduction of GHG emissions per ton per hectare was projected for groundnuts at Ofcolaco and Syferkuil and for sunflower in Ofcolaco in the future. However, the carbon footprint from groundnut is expected to increase by 40 to 107% in Punda Maria for the period up to 2030 and between 70-250% for 2050, with sunflower following a similar trend. We conclude that climate change will potentially reduce yield for oilseed crops while management will increase emissions. Therefore, in designing adaptation measures, there is a need to consider emission effects to gain a holistic understanding of how both climate change impacts on crops and mitigation efforts could be targeted.

Dataset of the suitability of major food crops in Africa under climate change

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.

Minimum tillage as climate-smart agriculture practice and its impact on food and nutrition security

Minimum tillage (MT) is a sustainable farming practice that limit soil disturbance only to planting stations while leaving the rest of the soil undisturbed. It is an important component of conservation agriculture, which aims to raise agricultural productivity, improve the livelihoods of farmers and build resilient farming systems. Despite the growing empirical literature on its adoption and benefits, there is a paucity of empirical evidence on the heterogeneous effect of length of MT adoption on household welfare. This study uses plot-level and household data combined with geo-referenced historical weather data to provide microeconomic evidence of the impact of MT on maize yields, food and nutrition security, and farm labor demand in Ghana. We account for potential selection bias and omitted variable problems by using an ordered probit selection model to estimate two transition-specific treatment effects: from conventional tillage systems to short-term MT adoption and from short-term to long-term MT adoption. The empirical results show that longer cropping seasons of MT adoption significantly increases maize yields and dietary diversity by about 4.33% and 14.22%, respectively, and decreases household food insecurity and labor demand by 42.31% and 11.09%, respectively. These findings highlight the necessity of developing and implementing programs that promote and help smallholder farmers to sustain its adoption for longer cropping season.

Observed changes in wet days and dry spells over the IGAD region of eastern Africa

Changes in wet and dry patterns have an impact on rain-fed agriculture, crop productivity, and food security in Eastern Africa. The purpose of this research is to look into the changes in wet days and dry periods within the Intergovernmental Authority on Development (IGAD) region. Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) and Multi Models Ensembles (MME) of 10 historical simulations and projections from Coupled Model Intercomparison Project (CMIP6) models were employed as the data source. Several statistical approaches, as well as wet and dry spell thresholds, were used to calculate patterns of change in wet and dry spells on a decadal (10-year), 20, 30, and 41-year time scale. The results show the region exhibits decrease a decrease in the number of wet days and protracted dry spells in the 1980s, followed by an extraordinary (exceptional) increase in wet days in the subsequent decades (2011-2020) during March-May (MAM), June-September (JJAS), and October-December (OND). In Kenya, Somalia, southeastern Ethiopia, Eritrea, and Djibouti, the probability of surpassing 7, 14, 21, 28 days (1, 2, 3, 4 spells) was less than 5%. Furthermore, floods in 1997, 2018, 2019, and 2020, as well as droughts in 1983, 1984, 1985, and 2021, were triggered by an increase or decrease in the number of wet days and dry spells over most of the region. The number of wet days is projected to decrease by 10-20% during the MAM season across Sudan, South Sudan, and central and northern Ethiopia, JJAS is projected to increase by 30-50% across central and northern Sudan. However, during the OND season, increases are projected over Uganda, Ethiopia, and Kenya under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) scenarios. These findings contributed to the advancement of scientific knowledge in the IGAD region, as well as decision-making, food security, and the development of adaptation and mitigation strategies. We encourage rain-fed agriculture, crop variety planning, and irrigation supplement.

A new regional cotton growth model based on reference crop evapotranspiration for predicting growth processes

Meteorological conditions and irrigation amounts are key factors that affect crop growth processes. Typically, crop growth and development are modeled as a function of time or growing degree days (GDD). Although the most important component of GDD is temperature, it can vary significantly year to year while also gradually shifting due to climate changes. However, cotton is highly sensitive to various meteorological factors, and reference crop evapotranspiration (ET_O) integrates the primary meteorological factors responsible for global dryland extension and aridity changes. This paper constructs a cotton growth model using ET_O, which improves the accuracy of crop growth simulation. Two cotton growth models based on the logistic model established using GDD or ET_O as independent factors are evaluated in this paper. Additionally, this paper examines mathematical models that relate irrigation amount and irrigation water utilization efficiency (IWUE) to the maximum leaf area index (LAI_max) and cotton yield, revealing some key findings. First, the model using cumulative reference crop evapotranspiration (CET_O) as the independent variable is more accurate than the one using cumulative growing degree days. To better reflect the effects of meteorological conditions on cotton growth, this paper recommends using CET_O as the independent variable to establish cotton growth models. Secondly, the maximum cotton yield is 7171.7 kg/ha when LAI_max is 6.043 cm²/cm², the corresponding required irrigation amount is 518.793 mm, and IWUE is 21.153 kg/(ha·mm). Future studies should consider multiple associated meteorological factors and use ET_O crop growth models to simulate and predict crop growth and yield.

Simultaneous multi-crop land suitability prediction from remote sensing data using semi-supervised learning

Land suitability models for Canada are currently based on single-crop inventories and expert opinion. We present a data-driven multi-layer perceptron that simultaneously predicts the land suitability of several crops in Canada, including barley, peas, spring wheat, canola, oats, and soy. Available crop yields from 2013-2020 are downscaled to the farm level by masking the district level crop yield data to focus only on areas where crops are cultivated and leveraging soil-climate-landscape variables obtained from Google Earth Engine for crop yield prediction. This new semi-supervised learning approach can accommodate data from different spatial resolutions and enables training with unlabelled data. The incorporation of a crop indicator function further allows for the training of a multi-crop model that can capture the interdependences and correlations between various crops, thereby leading to more accurate predictions. Through k-fold cross-validation, we show that compared to the single crop models, our multi-crop model could produce up to a 2.82 fold reduction in mean absolute error for any particular crop. We found that barley, oats, and mixed grains were more tolerant to soil-climate-landscape variations and could be grown in many regions of Canada, while non-grain crops were more sensitive to environmental factors. Predicted crop suitability was associated with a region's growing season length, which supports climate change projections that regions of northern Canada will become more suitable for agricultural use. The proposed multi-crop model could facilitate assessment of the suitability of northern lands for crop cultivation and be incorporated into cost-benefit analyses.

Interplays between changing biophysical and social dynamics under climate change: Implications for limits to sustainable adaptation in food systems

Climate change scenarios have significant implications for the livelihoods and food security of particular groups in society and will necessitate a range of adaptation actions. While there is a significant literature on the social as well as biophysical factors and limits to adaptation, less is known about the interactions between these, and what such interactions mean for the prospects of achieving sustainable and resilient food systems. This paper is an attempt at addressing this gap by examining changing biophysical and social factors, with specific consideration of vulnerable groups, across four case studies (Ghana, Malawi, Norway and Spain). In each case, future climate change scenarios and associated biophysical limits are mapped onto four key social factors that drive vulnerability and mediate adaptation, namely, scale, history, power and politics, and social differentiation. We then consider what the interaction between biophysical limits and socio-political dynamics means for the options for and limits to future adaptation, and how climate may interact with, and reshape, socio-political elements. We find that biophysical limits and socio-political factors do not operate in isolation, but interact, with dynamic relationships determining the 'space' or set of options for sustainable adaptation. By connecting the perspectives of biophysical and social factors, the study illuminates the risks of unanticipated outcomes that result from the disregard of local contexts in the implementation of adaptation measures. We conclude that a framework focusing on the space for sustainable adaptation conditioned by biophysical and social factors, and their interactions, can help provide evidence on what does and does not constitute sustainable adaptation, and help to counter unhelpful narratives of climate change as a sole or dominant cause of challenges in food systems.

Climate suitability predictions for the cultivation of macadamia (Macadamia integrifolia) in Malawi using climate change scenarios

Climate change is altering suitable areas of crop species worldwide, with cascading effects on people reliant upon those crop species as food sources and for income generation. Macadamia is one of Malawi’s most important and profitable crop species; however, climate change threatens its production. Thus, this study’s objective is to quantitatively examine the potential impacts of climate change on the climate suitability for macadamia in Malawi. We utilized an ensemble model approach to predict the current and future (2050s) suitability of macadamia under two Representative Concentration Pathways (RCPs). We achieved a good model fit in determining suitability classes for macadamia (AUC = 0.9). The climatic variables that strongly influence macadamia’s climatic suitability in Malawi are suggested to be the precipitation of the driest month (29.1%) and isothermality (17.3%). Under current climatic conditions, 57% (53,925 km²) of Malawi is climatically suitable for macadamia. Future projections suggest that climate change will decrease the suitable areas for macadamia by 18% (17,015 km²) and 21.6% (20,414 km²) based on RCP 4.5 and RCP 8.5, respectively, with the distribution of suitability shifting northwards in the 2050s. The southern and central regions of the country will suffer the greatest losses (≥ 8%), while the northern region will be the least impacted (4%). We conclude that our study provides critical evidence that climate change will reduce the suitable areas for macadamia production in Malawi, depending on climate drivers. Therefore area-specific adaptation strategies are required to build resilience among producers.

Climate Change, Flood Disaster Risk and Food Security Nexus in Northern Ghana

This research reviews climate change, flood disasters impacts and food security nexus in northern Ghana. The impacts of climate change include flood disasters which in turn affect food production with subsequent impact on food security. While climate change impact can be positive in some regions, it can be negative in other regions as it could lead to excess or lack of water, which negatively affects food production. Most especially, flood disasters have reportedly become frequent with devastating consequences on food production. Literature further suggests that the frequency of floods and their impacts have the potential to increase in the future. Floods inundate farms, pastures and livestock, which could subsequently reduce crop yields and animal production. Floods also destroys physical infrastructure and disrupts socio-economic activities which are linked to agriculture sector and could affect food production. This eventually decreases food availability, accessibility, utilization, and stability in the region. Northern Ghana has experienced flood disasters with increased frequency, which are related to climate change impacts. Although there is research on climate change, flood disasters, and food security issues in northern Ghana, the literature thus far indicates no clear focus of studies that focuses on the nexus of climate change, flood disasters, and food security of the study site. Thus, this research seeks to review the nexus of climate change, and flood disaster impacts on food security in northern Ghana with their implications on food security in the region. This study has two main research objectives. The first objective of this research is to identify and understand the potential impacts of climate change and flood disasters on food production in the study site. The second research objective is to explain the connection between climate change and flood disasters and the implications of this relationship on food security in the study site. This review study focuses on climate change, flood disasters, and food production to understand the critical impacts of climate change and flood disasters on food security in the northern part of Ghana. The aim of this research is to contribute to literature and discussion of the nexus of climate change, flood disaster impacts and food security sub-Saharan Africa.

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

Agriculture in West 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) were −13 and −16%, while under the unsustainable development pathway (USDP), yield changes were −19 and −20% in Navrongo and Nioro du Rip, respectively. The impact on sorghum and millet were lower than that on maize. Variations in climate change impact among smallholders were high with relative standard deviations (RSD) of between 14% and 60% across the cereals with variability being higher under the USDP, except for millet. Agricultural production systems with higher intensification but with less emphasis on soil conservation (USDP) will be more negatively impacted by climate change compared to relatively sustainable ones (SDP).

Climate change and specialty coffee potential in Ethiopia

Current climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience.

Evidence of Climate Change Coping and Adaptation Practices by Smallholder Farmers in Northern Ghana

Evidence on how coping practices for immediate climate variations can transform into long-term adaptive capacity are relatively limited. This study addressed this gap by identifying the coping practices for short-term climate variations and the adaptation measures used by smallholder farmers to address future climate change in northeast Ghana. The paper used a mixed-methods approach, including household surveys, focus group discussions and key informant interviews. Data were collected from 555 households located in six communities across three districts in northeast Ghana. Results indicated that smallholder farmers were employing a host of practices to address the threats posed by climate change. Key adaptation practices included the planting of drought-tolerant crop varieties, the use of indigenous knowledge, intensification of irrigation, migration, adjusting the planting calendar, crop diversification, mixed farming, and sustainable land management practices. On the contrary, short-term coping practices reported by the study participants included the sale of non-farm assets, complementing agriculture with non-farm jobs, selling livestock, engaging in wage labor, charcoal burning and reliance on social networks. The results further revealed that barriers to climate change adaptation and coping practices differed by gender. The paper recommends that capacities of smallholder farmers in vulnerability hotspots should be enhanced to address immediate climate variations, as well as future climate changes. Ghana’s climate change and agricultural policies should prioritize adaptations by smallholder farmers in addressing threats posed by climate change.

Agriculture for Sustainable Development: A SWOT-AHP Assessment of Ghana’s Planting for Food and Jobs Initiative

The exponential increase in the population of Ghana and the need to meet the population’s food security needs while creating job opportunities have necessitated the implementation of the Planting for Food and Jobs (PFJ) initiative by the Government of Ghana (GoG). Using the strengths, weaknesses, opportunities, and threats (SWOT) analytical tool, we SWOT of the PFJ initiative. We further complemented the study with the analytic hierarchy process (AHP) tool to rank the various criteria (factors) identified under the SWOT analysis. The study identified favorable environmental conditions as the highest strength, which recorded 59.3%, followed by agricultural lands availability (21.8%). Inadequate of financial services was identified as the highest weakness (55.8%), followed by over-reliance on climatic conditions (25.9%). High export potential relative to agricultural products in the country was identified as the highest opportunity, which recorded a weight of 50.3%. The One District One Factory (1D1F) initiative came up as the second highest opportunity. Negative ramification of climate change was identified as the main threat to Ghana’s agricultural sector (57.9%), followed by the importation of basic food products (25%).