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Climate change and variability challenge crop productivity and resource use efficiency in West Africa. Despite abundant research on climate change impact on crop yields and food security, little is known about climate change effects on the resource use efficiencies of the main staple crops in the dry savanna agro-ecological zone of northern Benin, West Africa. This study assessed the impact of climate change on water- and N-use efficiencies, and yields of maize and sorghum in the dry savanna of northern Benin considering three soil fertility management options (return of crop residues, mineral NPK fertilizer application, and combinations of both) and three bias-corrected ensemble mean predictions (BNU-ESM, CanESM2, and MPI-ESM-MR models) of future climate (2080–2099) under Representative Concentration Pathways (RCPs) of 2.6, 4.5, and 8.5. Seasonal rainfall is projected to decrease by 2% under RCP 2.6 and by 4% under RCP 4.5, and to increase by 1% under RCP 8.5 relative to the baseline mean (1986–2005). Increasing trends in minimum temperature of +1.0 °C (RCP 2.6), +2.0 °C (RCP 4.5), +4.7 °C (RCP 8.5) and maximum temperature of +1.1 °C (RCP 2.6), +2.0 °C (RCP 4.5), +4.6 °C (RCP 8.5) are also predicted. Solar radiation was projected to decrease by about 0.4 MJ m−2 d-1. Under these projected climate scenarios, both CERES-Maize and CERES-Sorghum simulated positive responses in aboveground biomass accumulation during the vegetative growth stages. The predicted increase in aboveground biomass growth will be largest under RCP8.5 and smallest under RCP 2.6. This impact can be enhanced by improved soil fertility management, albeit with a crop-specific magnitude. Across the soil fertility management options, CERES-Maize predicted decreases in water-use efficiency by 17–53%, partial factor productivity of nitrogen (N) by 10–47%, and internal N-use efficiency by 5–33% for maize. Similarly, CERES-Sorghum simulated decreases in water-use efficiency (23–51%), partial factor productivity of N (22–49%), and internal N-use efficiency (13–47%) for sorghum. The largest overall loss in resource efficiency and yield were predicted for the RCP 8.5 scenario. The projected climate change for the dry savanna in northern Benin will likely reduce water- and N-use efficiencies as well as grain yields of maize and sorghum considerably but these results should be treated with caution due to shortcomings in the models structure for dealing with effects of enhanced CO2. For reliable assessments of climate change impact on WUE, it is critically important to update parameterization and code of the CERES crop models in DSSAT to have a sufficiently strong effect of CO2 on stomatal conductance and on transpiration.

Agriculture is a critical element of the West African Sudan Savanna’s economy and the main livelihood strategy for many people, where food insecurity and poverty are widespread. Low soil fertility, high intra and inter-annual rainfall variability together with limited ability to invest in new technologies are among the major constraints to the predominantly smallholder, rainfed crop production in the region’s mixed crop-livestock systems. We evaluated the effect of tillage practices (contour and reduced tillage), nitrogen fertilizer rates (no nitrogen -N0, recommended nitrogen -NREC and high nitrogen-N2REC) and residue management (improved and standard) on the yield of maize, cotton and sorghum for two landscape positions (upslope and footslope) for four growing seasons in three locations in the Sudan Savanna region of Burkina-Faso (Dano), Ghana (Vea) and Republic of Benin (Dassari). The studies aimed at assessing the potential of residue retention, tillage practices and nitrogen fertilization to (1) increase average yields and (2) stabilize yields under sub-humid conditions. Over the 4 year study period, across the 3 locations recommended N produced 17% higher crop yields than the N0 treatment. It was not worthwhile to double the recommended N as there was no yield benefit of applying more fertilizer. The results revealed no consistent interactions across sites except crop type and nitrogen fertilization. Stability analysis revealed that contour ridging was superior in stabilizing yield. Evaluation of the relative maize yield stability on different levels of nitrogen depicts variation and there is a tendency for an inverse relationship between mean yield of nitrogen levels and its relative yield stability since nitrogen application did not increase yield stability. The outcome of cost benefit analysis revealed that, return per cash invested favored non-degraded sites with cotton production and in years with favorable rainfall conditions. Thus, in the Sudan savanna of West Africa, it is economically risky to invest in mineral N fertilizers when cropping on degraded soils or when rainfall is expected to be erratic.

Cotton is an important cash crop in many West African countries. Hence, its sustainable production will support the national economies as well as the livelihoods of the farming population alike and in turn help easing wide-spread poverty. However, future climate change may affect the productivity of cotton in West Africa. Therefore, the objectives of this study were to (i) parameterize the Cropping System Model- CROPGRO-Cotton to simulate growth, seed cotton yield, and in-season soil water dynamics and nitrogen (N) uptake, and (ii) apply the model to estimate potential climate change impacts on cotton growth, yields, and water- and N- productivity under different soil-fertility management practices. The CROPGRO-Cotton model was first parameterized and evaluated using datasets collected in three field experiments conducted in 2014 and 2015 in the Dry Savanna of northern Benin, West Africa. The model was next applied to determine long-term responses of cotton to historical (1986–2015) and projected climate (2080–2099) for three Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5). CROPGRO-Cotton accurately simulated in-season soil water dynamics (nRMSE of 12–27%, d-values of 0.79–0.88), N uptake (nRMSE of 31–44%, d-values of 0.89–0.96), and biomass accrual (nRMSE of 31–46% and d-values of 0.91–0.97), as well as seed cotton yield at harvest (nRMSE of 24–39% and d-value of about 0.81). The model predicted higher seed cotton yield with planting dates in June compared to July. Under the climate change scenario of RCP2.6, CROPGRO-Cotton predicted a decrease in water use efficiency (WUE) by 20% without any soil amendment, and by 4% with an integrated soil-crop management compared to the historical run, but an increase of 2% with a high use of mineral fertilizer. With the RCP4.5 and 8.5 scenarios, the predicted changes in WUE varied between −1% and 17% across the soil fertility management options. CROPGRO-Cotton predicted increases in N-partial factor productivity by 7 to 31%. The N uptake varied between −7% and 46% whilst seed cotton yield varied between −7 and 41%. The findings underlined that the predicted future increases in water and N productivity in cotton will be driven by CO2 fertilization, increases in temperatures as well as rainfall variability, but at the expense of soil fertility leading to soil mining. Yet, even if cotton, unlike some other crops in the region, will likely respond positively to climate change, adequate soil fertility management practices are essential to ensure efficient and sustainable water- and N- use in the expected future.

Crop models are useful tools to investigate climate change impacts and suitable adaptations strategies on crops. In order to evaluate the impact of climate change on peanut yield in Senegal, a solution of the SIMPLACE crop modelling framework using the Lintul5 crop model together with a Tc model and FAO-56 based approach to simulate evapotranspiration was used with consideration of Tc versus Ta in driving heat stress with output from four regional climate models (RCMs) and two climate change scenarios (RCP4.5 and RCP8.5). Results from six field experiments at two sites (Bambey and Nioro) in Senegal in the dry seasons of 2014 and 2015 and the rainy season of 2014, were used for calibration and evaluation for two peanut varieties. Our calibration and evaluation exercise revealed that simulation skill was markedly improved when Tc was considered under irrigated, dry season conditions during which time the plants were subject to periodic heat stress. Under future climatic conditions, positive changes of up to 2.4% for RCP4.5 and 8.3% for RCP8.5 for seed yield were found when increasing [CO2] is taken into account for the period 2016–2045 in dry season. While, in rainy season seed yield increased by 11.0% for RCP4.5 and 19.0% for RCP8.5. The effect of climate change on seed yield was negative in the dry season where maximum Ta is often higher than 38 °C compared to the rainy season in particular when Ta is used for simulating heat stress effects. It is concluded that climate change could have limited negative impacts on peanut yield in Senegal due to the effect of elevated [CO2]. However, simulated Tc should be used instead of Ta to accurately account for heat stress impact on peanut especially during the dry season.

Sustainable production practices are needed to enhance water and nutrient use efficiencies in the cropping systems of West Africa, especially in face of predicted climate change for the region. Crop simulation models may assist in exploring alternative management systems for optimizing water and nutrient use efficiencies, increasing crop production, and easing food insecurity. The twofold objectives were: (i) to access the ability of the CERES-Maize and CERES-Sorghum models for predicting yields, nitrogen (N) and phosphorus (P) uptake, and in-season soil water and N dynamics during maize and sorghum growth; (ii) to use the models to assess the effects of different nutrient management strategies on soil C and N, and crop water and N use efficiencies considering 30 years of historical weather variability in the dry savanna agro-ecological zone of northern Benin, West Africa. Both models were calibrated with data from a researcher-managed field trial (2014) and validated with independent findings (2015). The models were next evaluated with datasets collected from researcher- and farmer-managed field trials conducted under rainfed and supplementary irrigation conditions (2014–2015). The parameterized CERES-Maize and CERES-Sorghum models accurately predicted biomass accumulation with a normalized root mean square error (nRMSE) of 18% and 17%, respectively, and an index of agreement (d) of 0.98 for both crops in non-nutrient- and non-water-stressed conditions. Under rainfed and rainfed with supplementary irrigation conditions, both with and without mineral fertilizer application, CERES-Maize predicted biomass accumulation with nRMSE (d) of 10–22% (0.96–0.99), while CERES-Sorghum reached accuracies of 13–29% (0.97–0.99). Grain yield was simulated with nRMSE (d) of 19% (0.93) and 15% (0.89) by CERES-Maize, and 16% (0.91) and 15% (0.80) by CERES-Sorghum in researcher-managed and farmer-managed fields, respectively. CERES-Maize predicted reasonably well nutrient uptake with nRMSE (d) ranging from 14 to 40% (0.89–98) for N and between 24 and 49% (0.90–0.98) for P. Similarly, CERES-Sorghum simulated N uptake with nRMSE of 17–49% and d-values of 0.87–0.98, and P uptake with nRMSE of 42–62% and d-values of 0.82–0.93. Values of nRMSE (d) between predicted and measured in-season soil water and N dynamics were 6–13% (0.61–0.87) and 46–58% (0.61–0.76) with CERES-Maize, and 14–20% (0.75–0.89) and 35–56% (0.62–0.82) with CERES-Sorghum, respectively. The models simulated improved soil organic C and inorganic N, water and N use efficiencies, and grain yields with the integrated soil-crop management system compared to un-amended soil and high mineral fertilizer use options under 30 years of weather variability. Both models are thus appropriate tools not only to explore potential impacts of predicted climate change on soil water and nutrient use efficiencies, but also to frame more sustainable intensification measures in the maize- and sorghum-based production systems of West Africa.

In the Sudano-Sahelian zone of West Africa, where deforestation and cropland soil degradation occur at alarming rates, the (re-)introduction of trees on degraded lands can improve and sustain farming systems and landscapes. The shoot and root morphological traits of five woody species introduced to degraded cropland were assessed with a particular focus on the survival, early establishment and growth of saplings subjected to manuring (1 kg per plant) and drip irrigation (0.5 L of water per plant per day). Functional traits of the woody above- and belowground organs of field-grown plants were monitored for the first 15 months after planting, covering two growing (rainy) seasons and one dry season. The high survival rate (>60%), combined with a very low incidence rate ( Jatropha curcas L., Leucaena leucocephala Lam. and Moringa oleifera Lam. The mortality rate of the most drought-sensitive species Parkia biglobosa Jacq. could be reduced ten-fold through supplemental irrigation during the dry season. The fast-growing L. leucocephala , M. oleifera and J. curcas had higher values for shoot-level traits than did the slow-growing Anacardium occidentale L. and P. biglobosa . Fertilization and irrigation enhanced the shoot growth of both the fast and slow growers during the dry and growing seasons. In contrast, belowground development was either increased or reduced by fertilization and irrigation during the growing and dry seasons, but the slow growers demonstrated a more plastic response to these treatments than did the fast growers. Among the studied traits, the relative growth rate of plant as a whole (i.e. shoots and roots combined) exhibited the greatest plastic response to resource availability, thus suggesting its application in screening candidate species for afforestation efforts. Overall, the five studied species were considered suitable for the afforestation of degraded croplands, while the early growth and establishment of saplings could be boosted by irrigation and a supply of manure.

The early growth stage is critical in the response of trees to climate change and variability. It is not clear, however, what climate metrics are best to define the early-growth sensitivity in assessing adaptation strategies of young forests to climate change. Using a combination of field experiments and modelling, we assessed the climate sensitivity of two promising afforestation species, Jatropha curcas L. and Moringa oleifera Lam., by analyzing their predicted climate&ndash, growth relationships in the initial two years after planting on degraded cropland in the semi-arid zone of Benin. The process-based WaNuLCAS model (version 4.3, World Agroforestry Centre, Bogor, Indonesia) was used to simulate aboveground biomass growth for each year in the climate record (1981&ndash, 2016), either as the first or as the second year of tree growth. Linear mixed models related the annual biomass growth to climate indicators, and climate sensitivity indices quantified climate&ndash, growth relationships. In the first year, the length of dry spells had the strongest effect on tree growth. In the following year, the annual water deficit and length of dry season became the strongest predictors. Simulated rooting depths greater than those observed in the experiments enhanced biomass growth under extreme dry conditions and reduced sapling sensitivity to drought. Projected increases in aridity implied significant growth reduction, but a multi-species approach to afforestation using species that are able to develop deep-penetrating roots should increase the resilience of young forests to climate change. The results illustrate that process-based modelling, combined with field experiments, can be effective in assessing the climate&ndash, growth relationships of tree species.

Phosphorus (P) deficiency is a major constraint to crop production in many agricultural systems globally. Application of phosphorus fertilizer is essential for optimal crop yields when soils are P limiting. Crop simulation models can provide an alternative, less time consuming and inexpensive means of determining the optimum crop P requirements under varied soil and climatic conditions. The CROPGRO-peanut model is capable of simulating the growth and yield of peanut in response to weather, soil, water, nitrogen and management practices but its capability in predicting crop responses to soil and fertilizer P needs to be established. Our objective was to adapt and evaluate the CROPGRO-peanut model within the DSSAT system to simulate the growth and yield of peanut (Arachis hypogaea) in response to soil and fertilizer P. Data from four P fertilizer treatments (0, 13, 26 and 39 kg P ha−1) and two cropping seasons (2002 and 2003) experiments on an Alfisol were used to calibrate the model. The model was tested using two data sets from a P fertilizer × cultivar trial conducted on-station in 1997 and 1998 and P fertilizer × fungicide trials on-farm in 2002. The limited testing showed that the P module accurately simulated the seasonal patterns of aboveground biomass and pod yield in the on-farm trials. Averaged across sampling dates RMSEs in the on-farm trials ranged from 100 to 398 kg ha−1 (d ≥ 0.99) for total biomass and from 97 to 263 kg ha−1 (d values ≥ 0.98) for pod yield. At final harvest, the variability of simulated biomass and pod yield was about 5.4 and 10.0% of the observed biomass and pod yield respectively. In the P fertilizer × cultivar trial, the variability of simulated biomass and pod yield were 22 and 13% for cv. F-Mix and 19 and 5% for cv. Chinese respectively. The model simulated the seasonal patterns of vegetative P content in the four farmers’ fields fairly well with RMSEs ranging from 0.28 to 1.29 kg ha−1 when averaged across measurements dates. The model outputs were sensitive to soil P test values, the method of P fertilizer application and to plant P uptake factors, such as root P extraction radius and root length density. There were differences in the sensitivity of the simulations to changes in target tissue P concentrations. Increasing or decreasing the optimum P concentration of seed and minimum P concentrations of leaf and stem had the greatest effects on pod yield compared to other plant parts. We conclude that the generic soil and plant P model in DSSAT 4.5 is capable of simulating peanut growth and yield in response to soil P levels or fertilizer application on an Alfisol.

Increased climate variability and higher mean temperatures are expected across many world regions, both of which will contribute to more frequent extreme high temperatures events. Empirical evidence increasingly shows that short episodes of high temperature experienced around flowering can have large negative impacts on cereal grain yields, a phenomenon increasingly referred to as heat stress. Crop models are currently the best tools available to investigate how crops will grow under future climatic conditions, though the need to include heat stress effects has been recognized only relatively recently. We reviewed literature on both how key crop physiological processes and the observed yields under production conditions are impacted by high temperatures occurring particularly in the flowering and grain filling phases for wheat, maize and rice. This state of the art in crop response to heat stress was then contrasted with generic approaches to simulate the impacts of high temperatures in crop growth models. We found that the observed impacts of heat stress on crop yield are the end result of the integration of many processes, not all of which will be affected by a “high temperature” regime. This complexity confirms an important role for crop models in systematizing the effects of high temperatures on many processes under a range of environments and realizations of crop phenology. Four generic approaches to simulate high temperature impacts on yield were identified: (1) empirical reduction of final yield, (2) empirical reduction in daily increment in harvest index, (3) empirical reduction in grain number, and (4) semi-deterministic models of sink and source limitation. Consideration of canopy temperature is suggested as a promising approach to concurrently account for heat and drought stress, which are likely to occur simultaneously. Improving crop models’ response to high temperature impacts on cereal yields will require experimental data representative of field production and should be designed to connect what is already known about physiological responses and observed yield impacts.

Sustainable management practices are needed to enhance soil organic carbon (SOC) in degraded soils in semi-arid West Africa. We studied the effects of three amounts of nitrogen (N) (0, 60 and 120 kg N ha−1) and three amounts of phosphorus (P) fertilizer (0, 26 and 39 kg P ha−1) application over four seasons on maize residue production, residue C, N, and P concentrations, and their impacts on SOC, total soil nitrogen (TSN), and total soil phosphorus (TSP) in the 0–20 cm soil layer. Combined application of N and P fertilizers substantially increased maize grain yield on average by 294 % and biomass produced and returned to the soil by about 60–70 % compared with no fertilization. Annual C, N, and P inputs from crop residue were significantly higher with combined application of N and P fertilizer. The increased amount of crop residue and consequent increased residue C, N and P returned to the soil significantly increased SOC, TSN and TSP in the 0–20 cm soil layer after four seasons. There was a significant correlation between the amount of crop residues returned to the soil over four seasons and SOC (r = 0.82; P = 0.007), TSN (r = 0.75; P = 0.020) and TSP (r = 0.69; P = 0.039). We concluded from these experiments that returning crop residues, application of inorganic fertilizer improves SOC, TSN and TSP concentrations and enhances crop productivity. The farmers who traditionally remove crop residues for fodder and fuel will require demonstration of the relative benefits of residues return to soil for sustainable crop productivity.

Six promiscuous soybean genotypes were assessed for their ability to nodulate with indigenous root-nodule bacteria in Ghana, with Bradyrhizobium japonicum WB74 serving as positive control. Although the results revealed free nodulation of all six genotypes in both inoculated and uninoculated plots, there was a marked effect of inoculation on photosynthetic rates and whole-plant C. Inoculation also increased stomatal conductance in TGx1485-1D, TGx1448-2E, TGx1740-2F and TGx1445-3E, leading to significantly elevated transpiration rates in the last two genotypes, and a decrease in TGx1485-1D, TGx1440-1E and Salintuya-1, resulting in reduced leaf transpiration and decreased C accumulation. Nodulation, total plant biomass, plant N concentration and content also increased and ∂(15)N of the six genotypes, except for TGx1448-2E decreased. Significantly higher %Ndfa resulted in all the soybean genotypes tested (except for TGx1485-1D), and the symbiotic N yield in TGx1740-2F and TGx1448-2E doubled. PCR-RFLP revealed 18 distinct IGS types present in root nodules of the six promiscuous soybean genotypes, with IGS type II being isolated from all six genotypes, followed by IGS types X and XI from five out of the six genotypes. Marked differences in strain IGS type symbiotic efficiency were revealed. For example, as sole nodule occupant, IGS type XI produced high symbiotic N in TGx1445-3E, but low amounts in TGx1448-2E. Inoculated Salintuya-1, which trapped nine strain IGS types in its root nodules, was the most promiscuous genotype, but produced less symbiotic N compared to genotypes with fewer strains in their root nodules.

The crop models in the Decision Support System for Agrotechnology Transfer (DSSAT) have served worldwide as a research tool for improving predictions of relationships between soil and plant nitrogen (N) and crop yield. However, without a phosphorus (P) simulation option, the applicability of the DSSAT crop models in P-deficient environments is limited. In this study, a soil–plant P model integrated to DSSAT was described, and results showing the ability of the model to mimic wide differences in maize responses to P in Ghana are presented as preliminary attempts to testing the model on highly weathered soils. The model simulates P transformations between soil inorganic labile, active and stable pools and soil organic microbial and stable pools. Plant growth is limited by P between two concentration thresholds that are species-specific optimum and minimum concentrations of P defined at different stages of plant growth. Phosphorus stress factors are computed to reduce photosynthesis, dry matter accumulation and dry matter partitioning. Testing on two highly weathered soils from Ghana over a wide range of N and P fertilizer application rates indicated that the P model achieved good predictability skill at one site (Kpeve) with a final grain yield root mean squared error (RMSE) of 535 kg ha−1and a final biomass RMSE of 507 kg ha−1. At the other site (Wa), the RMSE was 474 kg ha−1 for final grain yield and 1675 kg ha−1 for final biomass. A local sensitivity analysis indicated that under P-limiting conditions and no P fertilizer application, crop biomass, grain yield, and P uptake could be increased by over 0.10% due to organic P mineralization resulting from a 1% increase in organic carbon. It was also shown that the modeling philosophy that makes P in a root-free zone unavailable to plants resulted in a better agreement of simulated crop biomass and grain yield with field measurements. Because the complex soil P chemistry makes the availability of P to plants extremely variable, testing under a wider range of agro-ecological conditions is needed to complement the initial evaluation presented here, and extend the use of the DSSAT-P model to other P-deficient environments.

SUMMARYPrior on-station research showed that sowing dates, sowing density and applications of fungicide and phosphorus (P) increased groundnut (Arachis hypogaea) pod yield by 60–80%. Farmer-managed trials were conducted in the Wa district of the Upper West Region of Ghana from 2004 to 2007 to test the yield response to sowing density, fungicide and P and to assess economic returns of these technologies to farmers. Treatments included: an early maturing groundnut cultivar, Chinese, sown at farmers' density (5–8 plant m−2) without fungicide and without P application (T1, control), with fungicide sprays alone (T2), or with fungicide and P application (T3), cultivar Chinese sown at recommended (higher) density (20 plant m−2) with fungicide and P application (T4), and a full season cultivar, Manipinter, with fungicide and P application (T5). Soil fertility, sowing density, days from sowing to first weeding, incidence and severity of leaf-spot disease and plant population at final harvest were recorded. Relative to farmers' practice, pod yield of cultivar Chinese was significantly increased by 80% with fungicide sprays alone, 108% with fungicide and P application, and 113% with fungicide and P application at higher sowing density. Cultivar Manipinter treated with fungicide and P gave 107% increase in pod yield relative to farmers' practice. Correlation and stepwise regression analyses suggested that major determinants of groundnut pod yield in farmers' fields were plant density, leaf-spot disease and P availability. The increase in yield with fungicide and P application translated into a 4–5-fold increase in gross margin for farmers in the region. Returns to labour and labour productivity were doubled with combined use of fungicide and P fertilizer.

Few studies have assessed the levels of symbiotic N nutrition in legumes grown by farmers in Africa. In this study, the shoots of cowpea plants were sampled from 63 farms in 12 villages within 5 districts of the Upper West Region of Ghana, and assessed for growth and symbiotic N nutrition. The data revealed considerable differences in cowpea plant density per m2, plant growth,15N natural abundance (δ15N), %Ndfa, and N-fixed among different farms under one village, and between villages under the same district, and between districts in the Upper West Region. In farms where there were fewer cowpea plants per m2, plant growth was better and dry matter yield per plant significantly greater, leading to strong variations in δ15N values. Except for four farms at Bamahu which had cowpea shoot Ndfa values of 12.1%, 30.0%, 36.5% and 46.6%, one farm at Babile with Ndfa value of 58.1%, and three farms at Silbelle with Ndfa values of 56.8%, 57.9% and 68.7%, the remaining 55 out of the 63 farms studied showed high shoot Ndfa values, ranging from 70.6% to 99.7%, which clearly indicates that cowpea cultivated by farmers in the Upper West Region of Ghana meet a large proportion of their N requirements from symbiotic fixation. At the district level, isotopic analysis showed that, on average, the15N natural abundance values (%0) of cowpea shoots were −0.496±0.04 for Jirapa, −0.083±0.06 for Nadowli, 0.368±0.08 for Lawra, J.333±0.29 for Wa and 0.365±0.09 for Sissala district. Estimates of the legume’s N derived from fixation were 66.3% for Wa district, 89.9% for Nadowli, 79.4% for Lawra, 78.9% for Sissala and 80.9% for Jirapa district. The amount of N-fixed ranged from 402.3 mg.plant−1 for Nadowli, 176.5 mg.plant−1 for Wa, 235.4 mg.plant−1 for Sissala, 179.0 mg.plant−1 for Lawra to 249.2 mg.plane−1 for the Jirapa district. Expressed on per-hectare basis using cowpea density per m2, the total amount of N-fixed was around 16.6 kg ha−1 in the Nadowli district, 19.1 kg ha−1 in Wa, 23.0 kg ha−1 in Sissala, 2J.1 kg ha−1 in Lawra and 17.6 kg ha−1 in the Jirapa district. Averaged across all 5 districts, N-fixed by cowpea was about 19.5 kg ha−1 in the Upper West Region of Ghana. These data suggest that, increasing N2 fixation in fanners’ fields in Ghana would require optimization of cowpea plant density rather than biological manipulation of the symbiotic process (as %Ndfa values were generally very high).

The interest in agricultural soils as global storage of carbon has increased in recent years, along with the prospect of farmers' participation in payment schemes under the Clean Development Mechanism of the Kyoto protocol. Thus, a better understanding of agricultural practices that can increase soil carbon and enhance the livelihoods of farmers is necessary, particularly in smallholder farming systems of West Africa. This study evaluates different crop management strategies both by their capacity to sequester carbon in agricultural soils and by their contribution to household income. A case study in Wa, Upper West Region of Ghana is used to test 48 different cropping strategies by means of a crop simulation model and a household-level multiple-criteria optimisation model. Each cropping strategy is evaluated after a 20-year simulation period by its capacity to accrue carbon in the soil, by its economic performance at the plot-level, and by its contribution to the farm income with and without carbon payments. A set of best management practices that concomitantly increase soil carbon and farm income are identified and classified by their cost of investment.

Participation in carbon (C) markets could provide farmers in developing countries incentives for improving soil fertility. However carbon traders need assurances that contract levels of C are being achieved. Thus, methods are needed to monitor and verify soil C changes over time and space to determine whether target levels of C storage are being met. Because direct measurement over the large areas needed to sequester contract amounts of C in soil is not practical, other approaches are necessary. An integrated approach is described in which an Ensemble Kalman Filter (EnKF) is used to assimilate in situ soil carbon measurements into a stochastic soil C model to estimate soil C changes over time and space. The approach takes into account errors in in situ measurements and uncertainties in the model to estimate mean and variance of soil C for each land unit within a larger land area. The approach requires initial estimates of soil C over space along with uncertainties in these estimates. Model predictions are made to estimate soil C for the next year, in situ soil C measurements update these predictions using maximum likelihood methods, and the spatial pattern of soil C mean, variance, and covariance thus evolve over time. This approach can also be used to provide yearly estimates of the changes in soil C over multiple fields, the variance in those estimates, and aggregate soil carbon mean and variance values each year. In this paper, the use of the EnKF is shown for an area in Ghana with 12 fields, comparing numbers of fields sampled each year and ways of selecting which fields to sample each year. The model predicts soil C changes over time using first order decomposition of existing soil C and addition of C from plant residues. The lowest intensity sampling method (sampling only 1/4 of the fields per year) resulted in the highest level of uncertainty in aggregate soil C estimate. Rotating sample fields each year improved the performance of the EnKF. These results demonstrated a quantifiable tradeoff between field sampling intensity and uncertainty in aggregate soil C estimates. The framework could be modified to use more complex biophysical models and to assimilate remote sensing data

Among various ways to sequester CO2 from the atmosphere, increasing soil carbon is an option that could also lead to increased agricultural productivity, especially in developing countries. In order to accept this option as a mechanism for reducing atmospheric CO2 levels, a reliable soil carbon accounting system needs to be developed. Soil carbon can be estimated from direct measurements, but measurements often have higher variability than annual soil carbon changes due to the uncertain nature of in-situ sampling methods and laboratory analysis techniques. To reduce such variability in soil carbon estimates, the estimation accuracy of an approach that assimilates measurements with simulated outputs from a complex crop model was studied. The DSSAT-CENTURY model and the ensemble Kalman filtering (EnKF) method were used to test if this approach can provide more reliable estimates of SOC over time than measurements alone. The EnKF was developed to estimate soil carbon, crop biomass, and a model parameter that specifies the rate of soil organic matter decomposition. A base-case simulation study was set up with a continuous maize farming system in Ghana for 20 years. Based on an identical twin test that used model-generated truth and measurement datasets, the EnKF estimates of soil carbon showed superior estimation accuracy to measurements with less uncertainty. In time, the uncertainty in soil carbon estimates based on annual sampling was reduced by about 60% using the EnKF method. The method was not effective in estimating the model carbon decomposition rate parameter, mainly because of the low correlations of that parameter with either soil carbon or crop biomass. However, even when uncertainties in measurements and model predictions were relatively high, estimates of soil carbon converged to the true value, although uncertainties in those estimates varied with measurement and model uncertainties. We conclude that the use of the EnKF method with the complex DSSAT-CENTURY model would provide more reliable soil monitoring system, but more research is needed to compare complex versus simple models in this approach.

Late leaf spot is one of the important factors limiting peanut productivity in Ghana. It is essential to demonstrate the extent of yield losses caused by leaf spot and to develop suitable crop management practices. The objectives of this research were to study the effects of time of sowing, cultivar lifecycle, and fungicide sprays on disease incidence, biomass and pod yield of peanut crops grown under rainfed conditions in northern Ghana. Two peanut cultivars, Chinese (90 days duration) and F-mix (120 days duration) were grown for three seasons (1999–2001) at Nyankpala and for two seasons (2000, 2001) at Wa with three sowing dates (early, mid and late, relative to start of rainy season) with (+F) and without (−F) fungicide application. Disease rating, main-stem defoliation, total biomass, pod yield and seed yield were recorded at harvest maturity. Early sowings soon after onset of rains resulted in greater biomass and pod yields compared to late sowing for both cultivars. The long duration cultivar F-mix produced greater yields than short duration cultivar Chinese under both with and without fungicide treated environments. However, incidence and severity of disease, as measured by disease scores and main-stem defoliation at the end of season, were similar in both cultivars. This suggests that the greater yield obtained by the long duration cultivar was not due to disease tolerance or escape but due to its longer growing season. Application of foliar sprays of fungicide was effective in controlling leaf spot and improved peanut biomass and pod yields by 39% and 75%, respectively, when averaged across cultivars and years. Long duration cultivar F-mix, sown early and treated with fungicide under optimum and timely crop management practices, produced 4500–5000 kg ha −1 pod yield over five site years, i.e. more than three to four-fold increase over present average yields in Ghana.