Context: Maize is a key staple crop in Ghana, yet yields remain low (20-40 % of potential). Although fertilizer is promoted to enhance productivity, adoption is limited by highly variable yield responses. Objective: This study analyzed spatial and environmental drivers of fertilizer effect heterogeneity using 2854 yield observations from randomized controlled trials and 10,916 pairwise absolute yield response to fertilizer (AR) estimates. Methods: Causal forest (CF) and boosted random forest (BRF) models to estimated fertilizer effects, with BRF performance evaluated via a 10 x 10 nested cross-validation and grid search. SHapley Additive exPlanations and Accumulated Local Effects analyses identified key drivers of fertilizer effect heterogeneity and quantified the magnitude of their influence on fertilizer yield effect. Results and conclusions: Fertilizer effect varied widely (-4.7-8.9 t ha(-1)), with the Sudan Savannah showing the highest median AR (2.8 t ha(-1)) and the Forest-Savannah Transition the lowest (0.9 t ha(-1)). BRF outperformed CF in predicting fertilizer effects (ME: -0.06-0.05 t ha(-1) vs. -0.19 t ha(-1), RMSE: 1.17-1.23 t ha(-1) vs. 1.3 t ha(- 1), MEC: 0.32-0.38 vs. 0.24 and CCC: 0.46-0.54 vs. 0.34). Key determinants of fertilizer effect heterogeneity included both climatic variables (Palmer Drought Severity Index [PDSI], vapor pressure deficit, rainfall) and soil properties (silt content, exchangeable aluminum). PDSI emerged as the dominant driver of fertilizer effect heterogeneity in the entire data set. However, the relative importance of soil versus climate varied spatially: soil properties were the main drivers of fertilizer effect in the Semi-Deciduous Forest and the Forest-Savannah Transition, whereas climatic variables played a stronger role in northern zones. Fertilizer yield effect increased by 0.4-1.6 t ha(-1) with increasing PDSI, indicating that improved moisture availability enhances fertilizer use efficiency. Overall, optimal moisture conditions (PDSI > -2.0), the use of hybrid seeds, and the application of briquette fertilizer all contributed to higher fertilizer effects, whereas drought conditions substantially reduced them. Furthermore, fertilizer effect decreased by 0.2-1.4 t ha(-1) as silt increased from 9 % to 30 %, and by 0.3-0.6 t ha(-1) as exchangeable aluminum increased from 36 to 221 mg kg(-1). Significance: This study presents the first large-scale, data-driven assessment of fertilizer yield effects heterogeneity in Ghana, integrating causal and predictive machine learning with explainable AI. Findings support tailored fertilizer strategies by agro-ecological zones to reduce farmer risk and promote sustainable intensification.

Efficient fertilizer application is vital for enhancing maize production and profitability in Sub-Saharan Africa, where soil fertility varies widely across regions. This study aimed to develop a machine learning approach for generating site-specific fertilizer recommendations for maize production in Ghana and to evaluate its performance against conventional and semi-mechanistic approaches. A random forest machine learning model was trained on 482 maize yield experiments, consisting of 3136 yield observations collected from 1991 to 2020, to predict maize yield response to different fertilizer rates. The model incorporated multiple explanatory variables, including soil properties, climate conditions, and management practices, to generate fertilizer response curves from which fertilizer recommendations were derived for 14 sites across three agro-ecological zones in Ghana where field validation experiments were conducted. On these sites, the recommendations were compared with recommendations derived from the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS), Conventional Fertilizer Dose Response (CFDR), and Updated Conventional Fertilizer Dose Response (UCFDR) approaches and validated through field experiments. The machine learning approach generally recommended lower rates of phosphorus and potassium than the other approaches, while nitrogen recommendations were comparable. In the Guinea Savanna zone, the recommendations from the machine learning approach outperformed those from the other approaches, producing higher mean yields for three out of the four sites in the zone. In the Forest-Savanna Transition (FST) zone, the machine learning model recommendations led to higher mean yields at four sites, while the approaches based on QUEFTS and UCFDR performed best at two other sites. In the Semi-deciduous Forest zone, the recommendations of the QUEFTS approach resulted in the highest mean yields at three sites, and CFDR at one site. Despite high input prices during the period of experimentation, the machine learning approach-based recommendations demonstrated higher net profit margins in the FST zone, suggesting cost-effectiveness in this zone. These findings indicate that site-specific fertilizer recommendations are more efficient than blanket recommendations and that machine learning approaches offer a promising and innovative approach for generating cost-effective, site-specific fertilizer recommendations in tropical climates.

Rice, consumed by half of the world’s population, is inherently low in zinc (Zn), iron (Fe), and protein. We compiled data from 245 studies across 34 countries to evaluate the impact of genetic, agronomic, and processing interventions on rice grain Zn, Fe, and protein concentrations. Zn-biofortified cultivars had 9.8% higher grain Zn concentrations than non-biofortified cultivars, while Fe-biofortified cultivars did not exhibit a significant improvement in Fe content. In the absence of Zn and Fe fertilization, the probability of achieving the breeding target concentrations of Zn (28 mg kg−1) and Fe (15 mg kg−1) was only 4.0% and 10.5%, respectively in white (polished) rice. On the other hand, Zn and Fe fertilization increased the probability of achieving the same targets in white rice by 41.3% and 67.7% for Zn and Fe, respectively. Milling and polishing of brown rice grains reduced Zn, Fe, and protein concentrations by 21%, 70%, and 6.5%, respectively. Our findings emphasize the need for the combined use of genetic and agronomic fortification and the consumption of parboiled rice to attain desired health impacts.

Rice, consumed by half of the world’s population, is inherently low in zinc (Zn), iron (Fe), and protein. We synthesized data from 245 studies across 34 countries to evaluate the impact of genetic, agronomic, and processing interventions on rice grain Zn, Fe, and protein concentrations. Zn-biofortified cultivars had 9.8% higher grain Zn concentrations than non-biofortified cultivars, while Fe-biofortified cultivars did not exhibit a significant improvement in Fe content. The probabilities of achieving the breeding target concentrations of Zn (28 mg kg –1 ) and Fe (15 mg kg –1 ) in polished rice were only 4.0% and 10.5%, while Zn and Fe fertilization increased the probability to 41.3% and 67.7% for Zn and Fe, respectively. Milling and polishing of brown rice grains reduced Zn, Fe, and protein concentrations by 21%, 70%, and 6.5%, respectively. Our findings emphasize the need for combined use of genetic and agronomic fortification, and consumption of parboiled rice to attain desired health impacts.

The application of crop models in sub-Saharan Africa is often limited by the scarcity of reliable weather data. This study evaluated the performance of the LINTUL-2 maize model in simulating water-limited yields using three weather datasets: observed station data from the Ghana Meteorological Agency (GMet) and two gridded sources—ERA5-Land and NASA POWER. Model performance was assessed by quantifying and comparing the uncertainties associated with each dataset’s yield predictions. Calibration was conducted using 2020 field data on phenology and yield, yielding root mean square error (RMSE) of 0.82–1.44 t ha−1, mean error (Bias) of 0.34–1.2 t ha−1, and ratio of performance to interquartile distance (RPIQ) values ranging from 0.88 to 1.53 across the datasets, with GMet consistently producing the most accurate results. Observed 2020 yields ranged from 3.4 to 6 t ha−1, while simulated yields ranged from 4.4 to 6.9 t ha−1. Validation with 2021 data produced RMSE values of 1.2–1.8 t ha−1, Bias ranging from −0.6 to 0.9 t ha−1, and RPIQ scores of 0.8–1.2. Further evaluation using 60 legacy yield observations revealed that ERA5-Land showed better water-limited yield variability but still overestimated yields (Bias = 2.51 t ha−1), slightly outperforming NASA POWER (Bias = 2.85 t ha−1). Drastic underestimation at some locations were linked to poor rainfall distribution and low soil water-holding capacity, despite sufficient seasonal rainfall totals (> 400 mm), resulting in prolonged crop stress. Overall, the model tended to overestimate yields, likely due to field conditions not fully aligning with the model’s assumptions of water limitation and nutrient sufficiency. The findings suggest that while gridded weather datasets, when used with crop growth models, can provide reasonably accurate yield estimates in data-scarce regions, aligning field conditions with model assumptions–particularly regarding nutrient availability–is critical for improving simulation reliability.

Maize as staple crop is essential for food security in Ghana, yet its yields remain low and highly variable despite increased fertilizer use. Understanding yield variability is crucial for effective agronomic strategies and reducing the risks of inappropriate fertilization strategies. This study quantifies maize yield variability, evaluates predictive model accuracy, and identifies key yield-influencing factors using machine learning (ML) models. A dataset of 5213 maize yield observations from 2000 to 2022 was analyzed using random forest (RF), gradient boosting (GB), and cubist models. Model performance was assessed through nested 20 x 10-fold cross-validation and grid search, using model error, concordance correlation coefficient, model efficiency coefficient (MEC), and mean absolute error. SHapley Additive exPlanations value and H-statistics determined variable importance and first- and second-order effects. Yield coefficient of variation was 55 %, with fertilizer use reducing variability on average by 15 %. ML models demonstrated high predictive accuracy, with RF, GB, and cubist achieving similar mean MCEs of 0.69. All three models identified nitrogen fertilizer (NF) and exchangeable soil magnesium (Mg) as the key drivers of maize yield. NF increased maize yields by an average of 181 kg ha(-1) (RF), 287 kg ha(-1) (GB), and 293 kg ha(-1) (Cubist) across a range of 0-240 kg NF ha(-1). Similarly, Mg contributed to yield increases on average of 161 kg ha(-1) (RF), 198 kg ha(-1) (GB), and 287 kg ha(-1) (cubist) over a range of 0.5-2.4 mEq 100 g(-1). Additionally, interactions between NF, soil moisture, and Mg were found to significantly influence maize yield. Our findings underscore the pivotal role of NF and Mg (a relatively understudied soil nutrient in Ghana) in influencing maize yield. The study advocates for Mg- contain fertilization and highlights the need for further research to deepen Mg impact on maize production in Ghana.

Soil fertility is a critical factor in sustaining crop productivity and meeting the food demands of the world's growing population. Assessing the fertility status of soil allows for the adoption of targeted agricultural practices to enhance crop yields. However, national-scale soil property maps in Ghana remain scarce. This study entailed the development of 250 m resolution maps of Ghana showing 9 soil properties at a depth of 0-30 cm, including total nitrogen (TN); available phosphorus (avail. P); exchangeable potassium (exch. K); soil clay, sand, and silt content; soil organic carbon (SOC); pH; and cation exchange capacity (CEC). This involved two profile data harmonization methods-equal-area spline and thickness-weighted average-with a quantile random forest model. On average, the thickness-weighted approach (coefficient of determination [R2]: 0.008-0.687, concordance correlation coefficient [CCC]: 0.103-0.793) outperformed the spline approach (R2: 0.180-0.567, CCC: 0.175-0.688) for eight of the 9 soil properties. Ghanaian soils are deficient in nitrogen (within or below the maize critical range of 400-1500 ppm) and phosphorus (below the maize critical threshold of 11 ppm), while potassium levels are generally within the maize critical range of 17-74 ppm, with concentrations exceeding 74 ppm in the upper northern area of the country. Ghana's soils are predominantly sandy, and agroecological zones, including the Western Evergreen, Moist Evergreen, lower-west Deciduous Forest, and lower-west Coastal Savannah, show pH values below 5.5, indicating acidity levels that could limit crop productivity. The study shows that regional or continental soil maps do not adequately reflect the needed spatial details of soil properties, as provided in national-level maps, for effective targeting of fertilization strategies and other agronomic measures to enhance agricultural productivity.

Fertilizer prices play a major role in farmers’ decision making. With the recent price hikes due to COVID-19 and the Russia-Ukraine war, an analysis of the crop-switching decisions of farmers and their willingness to pay (WTP) for mineral fertilizers is conducted. A total of 420 maize farmers were individually interviewed in 2022 and focus group discussions (FGDs) were conducted in 2023. Evidently, 93.4

Machine learning (ML) is increasingly being used to enhance yield predictions and optimize agronomic practices in sub-Saharan Africa. Yet, understanding how these models generalize across heterogenous ecological context remains unresolved. This study, conducted in Ghana, evaluates the predictive performance of four ML models, namely, random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost) for predicting maize yield and agronomic efficiency-defined as the increase in yield per unit of nutrient applied. It also compares variable importances identified by these models and how they influence yield and agronomic efficiency. The analysis used 4496 georeferenced maize trial datasets from various agroecological zones across Ghana, incorporating 35 variables related to soil properties, climate, topography, crop management, and fertilizer application. Model performance was assessed using three cross-validation techniques: leave-one-out, leave-site-out, and leave-agroecological-zone-out. Accuracy was measured using mean error, root mean square error (RMSE), and model efficiency coefficient. When evaluated under leave-one-out cross-validation, XGBoost consistently achieved the highest predictive accuracy with the lowest RMSE for yield (639.5 kg ha-1) and for agronomic efficiency of nitrogen (11.6 kg kg-1), which is moderate given the high variability in on-farm nutrient response. RF also performed well, while KNN and SVM showed poor extrapolation under stringent validation. Nitrogen application rate, rainfall, and crop genotype were consistently identified as the most influential explanatory variables across all models, providing insight into key drivers of productivity. These findings demonstrate the power of ML techniques in supporting agricultural planning and improving maize production in sub-Saharan Africa.

Phosphatic fertilizers are indispensable for sustainable agriculture, but phosphorus (P) scarcity has drawn global attention with respect to research and policy discussions. Soil conditions (pH, organic matter, metal oxides), P-fertilizer form and its application methods, and plant growth mechanisms influence plant P availability. Given the nonrenewable nature and low use efficiency of P, the development of speciality P-fertilizers and improved application methods are essential for reducing environmental P losses and increasing plant P uptake, thereby improving P use efficiency (PUE). This paper explores strategies for using innovative P-fertilizers targeting plant physiological processes instead of conventional bulk field applications to enhance PUE.

The continuously rising atmospheric CO2 concentration potentially increase plant growth through stimulating C metabolism; however, plant C:N:P stoichiometry in response to elevated CO2 (eCO2) under low P stress remains largely unknown. We investigated the combined effect of eCO2 and low phosphorus on growth, yield, C:N:P stoichiometry, and remobilization in rice cv. Kasalath (aus type), IR64 (a mega rice variety), and IR64-Pup1 (Pup1 QTL introgressed IR64). In response to eCO2 and low P, the C accumulation increased significantly (particularly at anthesis stage) while N and P concentration decreased leading to higher C:N and C:P ratios in all plant components (leaf, sheath, stem, and grain) than ambient CO2. The remobilization efficiencies of N and P were also reduced under low P with eCO2 as compared to control conditions. Among cultivars, the combined effect of eCO2 and low P was greater in IR64-Pup1 and produced higher biomass and grain yield as compared to IR64. However, IR64-Pup1 exhibited a lower N but higher P concentration than IR64, indicating that the Pup1 QTL improved P uptake but did not influence N uptake. Our study suggests that the P availability along with eCO2 would alter the C:N:P ratios due to their differential partitioning, thereby affecting growth and yield.

Soybean (Glycine max L.) is an important crop for Ghana. However, the variability of yields throughout the season and in space limits its potential to improve the lives of farmers. A two-year (2020-2021) study was conducted in eight regions of Ghana from 2020 to 2021 to evaluate the effect of ten fertilization strategies that combined nitrogen (N), phosphorus (P), potassium (K), sulphur (S) and zinc (Zn) at two rates on soybean yield. Treatments were all randomly laid out in the eight regions. The study also characterized the factors driving the spatial and temporal variability of yield. The effects of the fertiliser treatments were analysed using a linear mixed effects model to determine the magnitude of their responses. Additionally, a random forest model was employed to predict yield spatially and seasonally, with the objective of characterising the effect sizes of biophysical variables on yield. In 2020, the highest average yield was achieved through the additive effect of NPS. This resulted in an increase in yield of 446 kg ha−1 over the control yield and 249 kg ha−1 over the NPK treatment. Similarly, in 2021, NPS significantly increased yield by 275 kg ha−1, although the highest average yield of 485 kg ha−1 was achieved through the additive effect of NPKZn relative to the control yield. The yield's coefficient of variation was significant in both years, at approximately 50%. However, the random forest model only accounted for 20-24% of the yield variability. During the two-year of experimentation, random forest model identified three common factors among the ten most important variables that influenced soybean yield: the predicted yield was increased by 30 kg ha−1 with S (10 kg ha−1) and from 78 to 130 kg ha−1 with P (20 kg ha−1) nutrient rates. Additionally, cumulative solar radiation greater than 657-680 kWh m−2 reduced the predicted yield from 125 to 140 kg ha−1. The results imply that the use of S in combination with P is an effective strategy for increasing soybean yields in Ghana. Spatial maps indicated that yield gain can increase significantly with fertiliser in northern regions, but rainfall and root zone depth are also key factors to consider in boosting soybean production.

The continuously rising atmospheric CO2 concentration potentially increase plant growth through stimulating C metabolism; however, plant C:N:P stoichiometry in response to elevated CO2 (eCO(2)) under low P stress remains largely unknown. We investigated the combined effect of eCO(2) and low phosphorus on growth, yield, C:N:P stoichiometry, and remobilization in rice cv. Kasalath (aus type), IR64 (a mega rice variety), and IR64-Pup1 (Pup1 QTL introgressed IR64). In response to eCO(2) and low P, the C accumulation increased significantly (particularly at anthesis stage) while N and P concentration decreased leading to higher C:N and C:P ratios in all plant components (leaf, sheath, stem, and grain) than ambient CO2. The remobilization efficiencies of N and P were also reduced under low P with eCO(2) as compared to control conditions. Among cultivars, the combined effect of eCO(2) and low P was greater in IR64-Pup1 and produced higher biomass and grain yield as compared to IR64. However, IR64-Pup1 exhibited a lower N but higher P concentration than IR64, indicating that the Pup1 QTL improved P uptake but did not influence N uptake. Our study suggests that the P availability along with eCO(2) would alter the C:N:P ratios due to their differential partitioning, thereby affecting growth and yield.

Increasing fertilizer use is highly justified for sustainable agricultural intensification if yield response, fertilizer use efficiency (FUE), and economic viability of fertilizer application are high. Despite the increasing fertilizer application rates in Ghana, yields only marginally increased. Also, the recent fertilizer price hikes post COVID-19 revived concern for economic analysis of fertilizers. This study analyzed the FUE and economic viability of fertilizer use in maize production in Guinea/Sudan Savannah and Transitional/Deciduous zones of Ghana. Survey data from 2,673 farmers in the 2019, 2020, and 2021 production seasons were used. The average agronomic efficiency (AE), partial factor productivity (PFP), and value-cost ratio (VCR) of fertilizer use were 2.2 kg of grains per kilogram of fertilizer, 18.3 kg grains per kilogram of fertilizer, and 1.8 Ghana cedis of marginal yield per Ghana cedi spent on fertilizer, respectively. Fertilizer use was economically viable for only 28.1% of farmers with a VCR of 2 or higher, while 52.5% reached the break-even point with a VCR of at least 1. Various fertilizer formulations, including NPK plus sulfur, and adoption of integrated soil fertility management (ISFM) practices, particularly improved seeds, organic fertilizers, and minimum tillage, improved maize yield response to fertilizer and thus the FUE. These low efficiency and economic viability of fertilizer use are prevailing conditions in other sub-Saharan Africa (SSA) countries and these do not guarantee sustainable food security and improved livelihood of the farmers in the region. Ghana’s Ministry of Food and Agriculture (MoFA), together with relevant stakeholders, should provide guidance on ISFM and intensify farmer education through farmer associations to increase the adoption of ISFM. The local government should work with other relevant stakeholders to improve the market conditions within the agriculture sector, for instance, by linking farmers to city markets for favorable output prices.

The primary objective of this multilocational study was to investigate the impact of NPKS granule and briquette fertilizers, on selected soil chemical properties and yield of maize. The treatments were made up of different rates of NPKS granules and briquette fertilizers namely: T1 (Control), T2 (Granule NPK 10- 20-20 (200 kg ha-1) + Granule Urea 217.2 kg ha-1), T3 (Granule NPKS 10- 20-20-3 (600 kg ha-1) + Granule Urea 87 kg ha-1 GrU), T4 (Granule NPKS 10- 20-20-3 (400 kg ha-1) + Granule Urea 87 kg ha-1 GrU), T5 (Granule NPKS 10- 20-20-3 (400 kg ha-1) + No Urea), T6 (Briquette NPKS 10- 20-20-3 (3 briquettes/hill) + Briquette Urea (2 briquettes/hill) and T7 (Briquette NPKS 10- 20-20-3 (3 briquettes/hill) + Briquette Urea (1 briquette/hill), were deployed in a randomized complete block design with four replications. Some soil chemical properties were assessed; pH, available phosphorus (P), exchangeable potassium (K), calcium (Ca), magnesium (Mg), cation exchange capacity (CEC), total nitrogen (N), and organic matter content. Findings revealed stable pH levels, low available P, and suboptimal exchangeable K levels in the soil, indicating that the treatment did not have any significant impact on the chemical properties of the soil. The application of Granular NPKS 10- 20-20-3 (400 kg ha-1) + Granular Urea 87 kg ha-1 GrU) to maize produced significantly higher cob length and cob diameter compared with the control at only Atebubu. Total grain yield exhibited no significant differences (P ≥ 0.05) among treatments at Atebubu and Nsapor respectively. Significant differences occurred in the 100-seed with T3 and T4 producing higher weights at Nsapor and Atebubu respectively. Although the differences between treatments were not statistically significant, the result indicate a potential positive effect of T3 and T5 on grain yield (t/ha). The study highlights the influence of NPKS fertilizers in granule and briquette forms on soil chemical properties and maize yield, with the granules performing better than the briquette hence recommended.

Maize (Zea mays) is an important staple crop for food security in Sub-Saharan Africa. However, there is need to increase production to feed a growing population. In Ghana, this is mainly done by increasing acreage with adverse environmental consequences, rather than yield increment per unit area. Accurate prediction of maize yields and nutrient use efficiency in production is critical to making informed decisions toward economic and ecological sustainability. We trained the random forest machine learning algorithm to predict maize yield and agronomic efficiency in Ghana using soil, climate, environment, and management factors, including fertilizer application. We calibrated and evaluated the performance of the random forest machine learning algorithm using a 5 × 10-fold nested cross-validation approach. Data from 482 maize field trials consisting of 3136 georeferenced treatment plots conducted in Ghana from 1991 to 2020 were used to train the algorithm, identify important predictor variables, and quantify the uncertainties associated with the random forest predictions. The mean error, root mean squared error, model efficiency coefficient and 90% prediction interval coverage probability were calculated. The results obtained on test data demonstrate good prediction performance for yield (MEC = 0.81) and moderate performance for agronomic efficiency (MEC = 0.63, 0.55 and 0.54 for AE-N, AE-P and AE-K, respectively). We found that climatic variables were less important predictors than soil variables for yield prediction, but temperature was of key importance to yield prediction and rainfall to agronomic efficiency. The developed random forest models provided a better understanding of the drivers of maize yield and agronomic efficiency in a tropical climate and an insight towards improving fertilizer recommendations for sustainable maize production and food security in Sub-Saharan Africa.

The continuously rising atmospheric CO2 concentration potentially increase plant growth through stimulating C metabolism; however, plant C:N:P stoichiometry in response to elevated CO2 (eCO2) under low P stress remains largely unknown. We investigated the combined effect of eCO2 and low phosphorus on growth, yield, C:N:P stoichiometry, and remobilization in rice cv. Kasalath (aus type), IR64 (a mega rice variety), and IR64-Pup1 (Pup1 QTL introgressed IR64). In response to eCO2 and low P, the C accumulation increased significantly (particularly at anthesis stage) while N and P concentration decreased leading to higher C:N and C:P ratios in all plant components (leaf, sheath, stem, and grain) than ambient CO2. The remobilization efficiencies of N and P were also reduced under low P with eCO2 as compared to control conditions. Among cultivars, the combined effect of eCO2 and low P was greater in IR64-Pup1 and produced higher biomass and grain yield as compared to IR64. However, IR64-Pup1 exhibited a lower N but higher P concentration than IR64, indicating that the Pup1 QTL improved P uptake but did not influence N uptake. Our study suggests that the P availability along with eCO2 would alter the C:N:P ratios due to their differential partitioning, thereby affecting growth and yield.

Foliar application could improve grain iron (Fe) concentration (GFeC) by following 4Rs, i.e., the right Fe compound with right concentration sprayed at the right growth stage with right number of sprays. We studied the Fe mobilisation towards grain and its use efficiency using chelated-Fe and nano-Fe compounds in rice. Various Fe formulations [Fe-citrate, Fe-EDTA, FePO4, nano-Fe oxide, and humic acid with FeCl3 (HA + Fe)] were evaluated for their effect on growth, yield, and Fe mobilisation in rice. Single spray was done at tillering (set 1), anthesis (set 2), and grain-filling (set 3) stages, or sprayed twice at anthesis and grain-filling (set 4) and thrice at all stages (set 5). In all sets, shoot Fe at harvest (SFeH) correlated significantly with grain yield whereas SFeH and GFeC were negatively correlated, indicating that higher Fe in foliage promotes growth but would not necessarily increase grain Fe. A significant correlation between GFe uptake (GFeU) with Fe mobilisation efficiency index revealed that Fe mobilisation from shoot rather than root was the primary contributor to GFeU. Among Fe compounds, HA + Fe application enhanced grain yield and GFeU (> 70%) relative to control in all sets whereas nano-Fe (4 mM) resulted in highest GFeC in sets 4 and 5. Improved yield and Fe mobilisation from shoot towards grain was obtained with a single spray of HA + Fe either at anthesis or grain-filling stage. Thus, foliar Fe regimen has potential to enhance grain mineral quality and alleviate Fe deficiency that have implications for human health.

CONTEXT: Maize is the main cereal crop in Ghana, but its production is adversely affected by various biotic and abiotic factors.OBJECTIVE: This study aimed to highlight the factors related to maize yield variability. To this end, yields from 978 data points within 3 agro-ecological zones (AEZs) were used in crop-based and statistical modelling.METHODS: The QUantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model, the Linear Mixed Effects Model (LMM), and the Random Forest (RF) model were used to evaluate multiple effect sizes.RESULTS AND CONCLUSIONS: Analyzing an entire set of yield data points with QUEFTS, and LMM explained 19%, and 26% of yield variability, respectively. Considering all data points in the RF model, nitrogen fertilizer (NF) rate, temperature, root zone depth, rainfall, and variety accounted for 27%, 15%, 13%, 10%, and 9% of yield variation, respectively. In Guinea Savanna (GS), Transition Zone (TZ), and Deciduous Forest (DF), QUEFTS explained 30%, 20%, and 4% of yield variability, respectively. LMM, however, explained 47%, 51%, and 79% of yield variability in those AEZs. LMM showed that the phosphorus fertilizer (PF) rate was very important and exceeded the importance of the NF rate in GS. LMM showed also that yield variability was significantly related to maize variety at the AEZ scale. In DF, soil chemistry (marginal R2 = R2m = 0.48) and environmental variables (R2m = 0.43) contributed more to explaining yield variability, whereas in GS and TZ, fertilizer rates (R2m = 0.35 in GS

Sub-Saharan Africa (SSA) faces chronic food insecurity associated with soil degradation and the peculiar aftermath of climate change and exacerbated by rising population and historically poor agricultural practices. Notably, use of mineral fertilizers has the potential to counteract soil degradation in SSA; it drives an increased agricultural production required to feed the rising population while sustaining the quality and health of soils. However, limited financial resources deprive SSA of the promise of fertilizers, wherein application rates are historically low, and regimes are characterized by unbalanced nutrient composition and poor fertilizer quality. Although current global fertilizer use is generally characterized by low efficiency, SSA is most affected due to the already low usage and the quality of available fertilizer products. About 70% of fertilizer-nitrogen is lost through unregulated transformation to ammonia, nitrous oxide, and nitrate that are either volatilized or emitted into the atmosphere or leached into water bodies. Similarly, the preponderance of fertilizer-phosphorus is lost via run-off and leaching, unavailing it to plants while overloading streams and rivers and, together with nitrate, causing eutrophication. These environmental problems are accentuated in SSA where fertilizer quantity and quality issues are already a limiting factor. Notably, recent advances happening outside of SSA indicate that nutrients, when strategically formulated, such as by nano packaging, (bio)polymer encapsulation, and tunable to respond to environmental cues, can provide multiple outcomes, particularly, healthy soils with higher productivity. Therefore, presumably, a proper synthesis of the gamut of soil properties influencing plant nutrient release and availability, options for plant exposure and uptake is critical for realizing these benefits in SSA. Despite these possibilities, there is a lack of deeper context on fertilizer-related issues as they affect food and nutrition security and the health of soils in SSA. This paper provides an overview of the fertilizer-nutrient and associated agronomic, food insecurity and soil environmental challenges and opportunities, which though not exclusive to SSA per se, can be reasoned with the peculiarity of the region. This provides the impetus to increase fertilizer use efficiency, improve soil and environmental health, sustainable crop production, and food and nutrition security in SSA.