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Numerous biological and chemical methods have been proposed over the years for estimating the nitrogen (N) mineralization capacity of soils; however, none of them has found general use in soil fertility testing. The efficacy of a recently proposed alkaline hydrolysis method for assessing N availability in soils compared with the standard long-term incubation technique for determining potentially available N was evaluated. The nitrogen mineralization of 12 surface soils incubated under aerobic conditions at 25 °C for 26 weeks was determined. Field-moist soils were direct-steam distilled with 1 M KOH or 1 M NaOH; the NH3 released was trapped in boric acid, and its concentration was determined successively every 5 min for 40 min. The cumulative N mineralized or hydrolyzed was fitted to the first-order exponential equation to determine the potentially mineralizable N (No) and an analogous “potentially hydrolyzable N (Nmax)” for the soils. The flush of CO2 (fCO2) following the rewetting and incubation of air-dried soils under aerobic conditions for 3 days was also determined. The results showed that the Nmax values differed considerably among the soils, indicating differences in the chemical nature and reactivity of the organic N content of the soils, and were significantly correlated with No and fCO2 values. The estimated Nmax and No values ranged from 105 to 371 mg N kg−1 and 121 to 292 mg kg−1, respectively. Based on the simple and inexpensive nature of the alkaline hydrolysis procedure, the reduction in the incubation time required to obtain No (months to minutes), and the strong association between Nmax and No, we concluded that Nmax is a good predictor of the biologically discrete and quantifiable labile pool of mineralizable soil organic N (ON), and the use of the alkaline hydrolyzable ON as a predictor of No merits consideration for routine use in soil testing laboratories for estimating the N-supplying capacity of soils.

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 smallholders.

Climate change is a major environmental stressor that would adversely affect tropical agriculture, which is largely rain-fed. Associated with climate change is an increasing trend in temperature and decline in rainfall, leading to prolonged and repeated droughts. The purpose of this study was to determine the effect of climate variables such as temperature, relative humidity, vapor pressure deficit (VPD), and soil water on the phenology, biomass, and grain yield of soybean crops. A greenhouse experiment was set in a split plot design with three average environmental conditions as the main plots: E1 (36 °C, RH = 55%), E2 (34 °C, RH = 57%) and E3 (33 °C, RH = 44%). Additionally, there were three water treatments: W1 (near saturation), W2 (Field capacity), and W3 (soil water deficit) and two soybean varieties (Afayak and Jenguma). These treatments were replicated nine times. The results showed that high temperatures (E1) accelerated the crop development, particularly at flowering. Additionally, increased atmospheric demand for water under a high temperature environment resulted in high evapotranspiration, leading to high transpiration which probably reduced photosynthetic activity of the plants and thereby contributing to biomass and grain yield loss. Biomass and yield were drastically reduced for the combined effect of high temperature (E1) and drought (W3) as compared to combined effect of ambient temperature (E3) and well-watered condition (W1). Increasing temperatures and erratic rainfall distributions associated with climate change poses a potential threat to the soybean production in Ghana.

The efficiency of mineral fertilizer use in most soils in Sub-saharan Africa is low. Prominent among the reasons for this is low soil carbon stock. In this study, we hypothesized that in the short term, combined use of biochar and inorganic fertilizer in irrigated rice (Oryza sativa var KRC Baika) cropping systems will increase soil organic carbon storage, N recovery and agronomic efficiency of N use (above world average of 55% and 20 kg grain·kg−1·N respectively) and improved economic returns compared to the sole use of inorganic fertilizer. A two-year (4‒cropping cycles) field trial was, thus, conducted on a Vertisol. The experiments were designed as split–plot with two (0 and 10 t·ha−1) biochar and four (0, 45, 90, 120 kg·ha−1·N) nitrogen application rates. Additionally, the effect of biochar on the chemical properties of the soil was investigated using standard protocols. Biochar application improved the soil organic carbon storage in the topsoil. There were significant interactions between the application of biochar and nitrogen fertilizer on yield parameters. Introducing biochar significantly increased root volume and nutrient (N, P and K) uptake, resulting in increased grain and straw yield. Grain yields under biochar amended plots were higher than sole fertilizer amended plots in 14 out of 16 instances (cropping cycles × N rates). The increase in grain yield was between 12 to 29% across N rates. Biochar amendment also enhanced agronomic N use and apparent N recovery efficiencies in 3 out of the 4 cropping cycles. Gross margin indicated that biochar application under irrigated rice cropping systems is economically feasible in all cropping cycles and N rates. However, the value cost ratio of biochar application was higher than for sole inorganic fertilizer in three out of the four cropping cycles (each cropping cycle has three N rates). The soil organic carbon storage of biochar amended soil increased by 17% under unfertilized condition and by 32% under fertilized condition. To enable the promotion and efficient use of the biochar technology in enhancing productivity and profitability in irrigated rice, extension officers and farmers will need to be trained on how to char the rice husk to reduce emissions prior to upscaling the technology to farmers.

Climate change is estimated to exacerbate existing challenges faced by smallholder farmers in Sub-Sahara Africa. However, limited studies quantify the extent of variation in climate change impact under these systems at the local scale. The Decision Support System for Agro-technological Transfer (DSSAT) was used to quantify variation in climate change impacts on maize yield under current agricultural practices in semi-arid regions of Senegal (Nioro du Rip) and Ghana (Navrongo and Tamale). Multi-benchmark climate models (Mid-Century, 2040–2069 for two Representative Concentration Pathways, RCP4.5 and RCP8.5), and multiple soil and management information from agronomic surveys were used as input for DSSAT. The average impact of climate scenarios on grain yield among farms ranged between −9% and −39% across sites. Substantial variation in climate response exists across farms in the same farming zone with relative standard deviations from 8% to 117% at Nioro du Rip, 13% to 64% in Navrongo and 9% to 37% in Tamale across climate models. Variations in fertilizer application, planting dates and soil types explained the variation in the impact among farms. This study provides insight into the complexities of the impact of climate scenarios on maize yield and the need for better representation of heterogeneous farming systems for optimized outcomes in adaptation and resilience planning in smallholder systems.