Climate change is projected to exacerbate food insecurity in sub-Saharan Africa (SSA) by reducing crop yields and soil fertility. Many climate change impact studies in SSA have overlooked long-term effects of soil fertility on crop yield. We evaluated maize yields under different scenarios of soil fertility (using soil organic carbon as a proxy) and climate change (considering changes in temperature, rainfall, and CO2) at four sites in SSA. Using an ensemble of 15 calibrated soil-crop models, we found a strong consensus that, without fertilization, soil fertility declines over time, impacting maize yields more strongly than changes in temperature, rainfall, or CO2. The model ensemble indicated that when accounting for soil fertility changes, the yield benefits of combined application of organic and mineral inputs increase over time, even under climate change. These findings highlight the importance of considering long-term change in soil fertility when assessing impacts of climate change and integrated nutrient management on crop production in SSA.

Accurate simulation of evapotranspiration (ET) with crop models is essential for improving agricultural water management and yield forecasting. Few studies have evaluated multiple soybean [Glycine max (L.) Merr.] models for simulating ET under conditions of low evaporative demand that is characteristic for a warm-summer humid continental climate. Six soybean crop models, encompassing 15 different modeling approaches, were evaluated for ET simulation and compared against eddy covariance data collected over five growing seasons in Ottawa, Canada. Models were first calibrated with phenology, in-season growth, and yield data, followed by calibration with measured ET and soil water content (SWC) data during the second step. After initial calibration, simulated daily ET was higher on average than measured ET, particularly during full canopy cover (normalized bias, nBias = 17.1 to 49.2% depending on the model). Following the second calibration, simulated daily ET was closer to measured values, but bias remained (nBias = 5.9 to 52.1% during full canopy). The ensemble median reduced uncertainty in the simulation of daily ET compared to most models, but DNDC remained the top-ranking model (nRMSE = 0.7 mm d- 1, nBias = 11.2%). The MONICA model was most accurate simulating cumulative ET (RMSE = 39.9 mm, nBias = 11.3%), whereas the CROPGRO models excelled simulating SWC (RMSE= 0.04 to 0.05 m3 m- 3, nBias = 0.10 to 0.9% depending on soil depth). This study was instrumental in evaluating the best ET methodologies and parameters in soybean models. However, there was bias across the models compared to measured eddy covariance ET in a humid environment. The results reveal the need to further investigate possible biases in ET estimates by eddy covariance over soybean canopies, and to review the role of night-time dew contributions to ET in process-based models.

The Cropping System CROPGRO-Perennial Forage Model (CROPGRO-PFM) within the Decision Support System for Agrotechnology Transfer (DSSAT) framework is among the few models that simulate and evaluate perennial forages. However, its application to systems in East Africa remains limited. To address this gap, this study aimed to assess the capability of the CROPGRO-PFM model to predict herbage yield and soil organic carbon (SOC) dynamics under Urochloa hybrid cv. Cayman and to evaluate herbage and SOC responses to varying manure application rates in Tanzania. Model calibration involved adjusting parameters related to soil water content and the fraction of SOC in the stable pool. The simulated herbage yield showed a good agreement with observed data, with the D-statistic ranging from 0.58 to 0.85, with no calibration required from previous genotype coefficients used for Urochloa's. The model captured seasonal variations in herbage production, showing peak yields during the wet season and reduced yields during the dry season. However, accurately capturing SOC variability requires long-term data, while our study was limited to just three years.Model application for 30 years across six sites revealed that a manure application rate of 10 t ha-1 led to SOC gains up to 0.7 Mg C ha-1 yr-1 and a 135% increase in herbage production. The results show the model's potential application for simulating herbage yield and SOC under irrigation and manure management in East Africa.

Industrial hemp ( Cannabis sativa L.) is a re‐emerging crop in the United States with unique agronomic challenges that require location‐specific studies and guidance. Digital farming tools, such as crop growth models, can facilitate this process by enabling a better understanding of the farming system. Crop growth models predict the growth and development of crops over time using weather, soil, management, and physiological parameters as inputs. The goal of this study was to develop a new hemp model in the Cropping System Model (CSM)‐CROPGRO module in the Decision Support System for Agrotechnology Transfer (DSSAT). Experimental data spanning two cultivars, both grown over two seasons and two sites in Florida, were used for model calibration and evaluation. Model adaptations were made in (1) tissue composition and assimilate partitioning, (2) cardinal temperatures for different growth and development processes, and (3) leaf photosynthesis and senescence. The results show a good simulation of aboveground biomass ( d = 0.91, root mean square error [RMSE] = 482 kg ha −1 ), stem weight ( d = 0.83, RMSE = 430 kg ha −1 ), and time to flowering (+4 to −5 days), capturing the differences among cultivars and planting dates. A seasonal analysis using the adapted model showed the impact of variable planting dates on hemp phenology, biomass, and grain production. Future work should include a more detailed observation and mechanistic simulation of self‐thinning and evaluation with data representing different production environments and cultivars. The CROPGRO‐Hemp model will provide a basis for growers, researchers, and other stakeholders to systematically analyze hemp production systems in Florida and internationally.

Rice ( Oryza sativa L.) is a staple food and plays a crucial role in the food security of many countries. However, rice cultivation is associated with significant methane (CH4) emissions, contributing to overall greenhouse gas emissions and, thus, climate change. In this context, process-based crop models are useful tools for understanding and predicting the complex interactions between crop production, environmental factors, and sustainability. The objective of this study was to evaluate the performance of the Cropping System Model (CSM)-CERES-Rice model and DSSAT-GHG module to predict daily methane emissions and rice grain yield for different irrigation practices in a subtropical environment. The study employed a comprehensive approach, including measurements of daily CH4 emissions, phenological stages, final aboveground biomass, and grain yield for rice cultivars BRS Pampa, BRS Pampeira, A705, and XP113 conducted over four consecutive crop seasons (2019-2023) and two irrigation systems: continuous flooding (CF) or alternate wetting and drying (AWD) in Capao do Leao, RS, Brazil. We followed a four-step methodology involving initial calibration of cultivar parameters, sensitivity analysis (soil- related parameters associated with CH4 emissions), final cultivar parameters calibration, and long-term simulation analysis. Based on the sensitivity analysis and comparison to observed emissions, modifications were made to soil-related parameters such as soil buffer regeneration after drainage events (BRAD) and the fraction of soil water-filled porosity above which methane production occurs (WFPSthresh) to enhance the accuracy of methane production. Optimal parameter combinations (WFPS thresh = 70 %, BRAD = 0.070 d- 1 ) were selected based on a comparative analysis, enabling CH4 simulations under non-flooded conditions. The predictive capability of the CERES-Rice model exhibited an average bias for grain yield of 485 kg ha- 1 under CF and 592 kg ha- 1 under AWD conditions. The results showed that the GHG module of DSSAT, after BRAD and WFPS thresh parameter adjustments, was able to simulate daily CH4 emissions in paddy rice with a very good agreement (average index of agreement (D-Statistic) of 0.87 for CF and 0.70 for AWD). Following the model evaluation, long-term simulations for different irrigation practices revealed the impact on grain yield, cumulative methane emissions, and seasonal applied irrigation. The highest crop water-methane productivity (CWMP = 52 %) was observed under sprinkler irrigation at 50 % soil water depletion, identifying it as the most sustainable option in this subtropical environment. Thus, the CSM-CERES-Rice model combined with the DSSAT-GHG module proved to be a potential tool for agricultural and environmental management of rice fields under subtropical conditions.

CONTEXT: There is an increase in global demand to reduce fossil fuel use and to create sustainable intensification of animal production systems. Alternative sources of mineral nitrogen fertilizers have been shown to increase herbage accumulation. The environment in which plants thrive is dynamic and complex due to the nature of the soil-plant-atmosphere interactions. Modeling can evaluate and explain these aspects. OBJECTIVE: The objective of this study was to adapt the CROPGRO-Perennial Forage Model (PFM) for its ability to simulate asymbiotic N-fixation and growth of brachiariagrass (Brachiaria hybrid 'BRS RB331' Ipypora) under four N sources: 0 N (0 N), 80 kg ha-1 yr-1 of mineral nitrogen fertilizer (80 N), 0 N + Azospirillum brasilense (Az), and 80 kg N ha-1 yr-1 + A. brasilense (80 N + Az). METHODS: The study was conducted from November 2014 to March 2017 in Sinop, Brazil. The parameterization of the CROPGRO-PFM started with published values for 'Marandu' palisadegrass but required minor modifications for Brachiaria hybrid 'BRS RB331' Ipypora. A limited number of parameter values were re-calibrated to include root senescence, dormancy sensitivity to daylength, and dry matter partitioning to leaf and stem. To mimic asymbiotic N fixation by A. brasilense, the biological nitrogen fixation (BNF) module from CROPGRO-PFMAlfalfa was used, along with a modified additional cost for BNF and modified specific activity. RESULTS: Important model modifications were to increase partitioning to leaf while decreasing partitioning to stem, along with modifying the daylength (winter) effect on partitioning to shoot. After modifying these model parameters, the simulation of herbage accumulation was improved, with a RMSE of 459 kg DM ha-1, and dstatistic of 0.70. The model was successfully adapted to simulate BNF by A. brasilense which resulted in equivalent of 61.8 kg N fixed ha-1 yr-1. CONCLUSIONS: Model adaptation for BNF by A. brasilense can effectively mimic the physiology of asymbiotic biological nitrogen fixation in the CROPGRO-PFM for simulating herbage accumulation of Ipypora brachiariagrass. IMPLICATIONS AND SIGNIFICANCE: Replacing mineral fertilizers with a sustainable N source can assist with sustainable food production, mitigation of greenhouse gas emissions and environmental pollution. However, the simulations indicate that asymbiotic BNF is costly (nearly 10 times more than BNF of soybean nodules) and it can be overcome/saturated by higher fertilization levels that provide N at a lower energy cost, although there is the fossil fuel cost of mineral N fertilization but not for BNF.

Combining multi-model simulations can reduce the uncertainty in model structure and increase the accuracy of agricultural systems modeling results. This improvement is essential for supporting better decision making in irrigation planning and climate change adaptation strategies. Besides the commonly used arithmetic mean and median, many multi-model averaging approaches (MAA), widely examined in groundwater and hydrological modeling, but these additional MAA have not been examined in agricultural system modeling to improve the simulation accuracy. Therefore, the objective of this study is to evaluate the performance of seven MAA: two equal weighted approaches (Simple Model Averaging (SMA) and Median) and five weighted approaches (Inverse Ranking (IR), Bates and Granger Averaging (BGA), and Granger Ramanathan A, B, and C (GRA, GRB, and GRC)) in combining results of multiple agricultural system models. The Granger Ramanathan methods differ in their constraints: GRA employs conventional least squares, GRB requires non-negative weights that total to one, and GRC reduces absolute errors for robustness against outliers. The evaluation was conducted using maize yield and daily ETa simulations for both blind (uncalibrated) and calibrated phases of data from two groups of maize sites (Group A and Group B) across North America. The modeling results from the blind and calibrated phases were combined for all maize models and group maize models. Overall, all MAA performed better than individual crop models for blind and calibration phases. Specifically, the GRB model averaging method provided the closest match to measured values for daily ETa, while GRA was the most accurate for maize yield in most cases across all sites and phases. GRB improved daily ETa estimation over the median by an average of 4 % and 8.5 % in terms of RRMSE, while GRA enhanced maize yield estimation over the median by 7.5 % and 10.9 % for Group A and Group B sites, respectively. Notably, the improvement was greater in the blind phase for both groups of maize sites. An ensemble of group maize models with varied structures performed nearly as well as an ensemble of all maize models in simulating daily ETa and yield for Group A and Group B sites. Based on the results, we recommend GRA for crop yield and GRB for ETa simulations for maize, but both methods require observed yield and ETa data for their application; however, in the absence of observed data, we recommend the SMA method as it performs better than the median. However, the performance of these MAA methods may differ for other crops (e.g., soybean, wheat, canola, potato, alfalfa) or regions, and it should be evaluated in future studies.

In a previous Arabidopsis investigation, three ovule-specific cell-wall peroxidases decreased seed abortion rates. These peroxidases were expressed in soybean plants. Because cell wall peroxidases alter extensibility, possible effects on seed size and plant yield were evaluated. Since the effects of these peroxidases in Arabidopsis were dependent on environmental stress, soybean plants were grown in controlled environment greenhouse rooms under four temperature treatments; the daily temperature averages were 26, 30, 34, and 38 °C. In this experiment in vivo oxygen levels during seed growth were 25-fold below ambient, which could affect peroxidase activities. Consequently, soybeans were grown at atmospheric (21%) and elevated (32%) O2 to evaluate peroxidase activities at higher O2. Chambers were maintained at 700 ppm CO2 in an attempt to minimize photorespiration in elevated O2. Individual seed weight decreased with increasing temperature to zero at 38 °C. In elevated O2 rooms, the oxygen concentration in developing seeds increased, but, due to leaf photorespiration, plant biomass and seed yield decreased. Seed size and shelling percentage declined equally with temperature at both O2 concentrations. Expression of all three cell-wall peroxidases reduced seed abortion; however, that did not increase yields at ambient or elevated O2. While O2 concentration is less than 1% in developing seeds, increased O2 levels in seeds were not beneficial for soybean reproduction.

This LETTER discusses metrics that can be used to quantify plant response to fluctuating elevated CO2 such as in Free-Air CO2 Enrichment (FACE) compared to that at constant elevated CO2, both with the same average elevated CO2 concentration. The concept of Reduction in CO2-stimulated uptake rate in oscillating elevated CO2 (Holtum and Winter, 2003), abbreviated as RCS, value = 0.33, is the FIRST METRIC of plant response in oscillating elevated CO2 compared to constant elevated CO2. RCS, which includes ambient CO2 in its calculation, inadequately describes the actual response of plants grown in fluctuating elevated CO2. Furthermore, the SECOND METRIC, Relative Response Ratio of Allen et al. (2020b), abbreviated as RRR, where RRR = 1.0 - RCS with a value of 0.67, also inadequately describes the response of plants grown in fluctuating elevated CO2. A THIRD METRIC of plant response to CO2 enrichment, "Plant response in fluctuating elevated CO2 / Plant response in constant elevated CO2", Fel/Cel, average value of 0.85, represents the response to fluctuating CO2 in FACE. For completeness, a FOURTH METRIC (Fel/Amb) and FIFTH METRIC (Cel/Amb) are defined. A variation of the FOURTH METRIC, [(Fel -Amb)/Amb] X 100], has been widely used to report yield responses to FACE.

Heat tolerance is an important trait in cowpea, a crop that constitutes the primary protein source for a large portion of the human population in sub-Saharan Africa. Cowpea grows across this region, with cultivated, landrace, semi-wild, and wild cowpeas germplasm growing across diverse climatic conditions. This study used environmental association (envGWAS) and allele frequency outlier approaches in a panel of 580 gene bank accessions to identify genomic regions associated with heat and limited precipitation. Because allele frequency outliers are detected independent of potential selection factors driving differentiation, we used a ranking-based approach to identify the climate variables most associated with variants among outliers. Precipitation-related variables dominated the signals we identified for envGWAS and allele frequency outliers. We found variants on all eleven chromosomes putatively associated with the adaptation of cowpea to higher-temperature environments. The considerable overlap between variants associated with low precipitation and high temperature suggests that these traits may be inextricably linked in cowpea. The Sahel region is the source of many accessions with derived variants associated with high temperature, suggesting the potential for accessions from this region to contribute to heat tolerance alleles for cowpea improvement. ### Competing Interest Statement The authors have declared no competing interest.

Sub-Saharan Africa (SSA) faces significant food security risks, primarily due to low soil fertility leading to low crop yields. Climate change is expected to worsen food security issues in SSA due to a combined negative impact on crop yield and soil fertility. A common omission from climate change impact studies in SSA is the interaction between change in soil fertility and crop yield. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, is expected to increase crop yield but it is uncertain how this advantage is maintained with climate change. We explored the impact of scenarios of change in soil fertility and climate variables (temperature, rainfall, and CO2) on rainfed maize yield in four representative sites in SSA with no input and ISFM management. To do so, we used an ensemble of 15 calibrated soil-crop models. Reset and continuous simulations were performed to assess the impact of soil fertility vs climate change on crop yield. In reset simulations, SOC, soil N and soil water were reinitialized each year with the same initial conditions. In continuous simulations, SOC, soil N and soil water values of a given year were obtained from the simulation of the previous year, allowing cumulative effects on SOC and crop yields.Most models agreed that with current baseline (no input) management, yield changed by a much larger order of magnitude when considering declining soil fertility with baseline climate (-39%), compared with considering constant soil fertility but changes in temperature, rainfall and CO2 (from -12% to +5% depending on the climate variable considered). The interaction between change in soil fertility and climate variables only marginally influenced maize yield (high agreement between models). The model ensemble indicated that when accounting for soil fertility change, the benefits of ISFM systems over no-input systems increased over time (+190%). This increase in ISFM benefits was greater in sites with low initial soil fertility. We advocate for the urgent need to account for soil-crop long-term feedback in climate change studies to avoid large underestimations of climate change and ISFM impact on food production in SSA.

Lack of robustness and potential bias are growing concerns for research, including for sustainable agriculture. Research confirmation requires independent duplication of field experiments, modeling and other analyses. Key concepts include “repeatability” (consistency within an experiment), “replicability” (same team, different environments), and “reproducibility” (independent team, different environments). Researchers must improve workflow descriptions, especially regarding crop environments and management. A useful metric is how well research could be reproduced in ten years.

Peanut is a nutritious and highly-valued crop across the world. Achieving competitive yield and quality is challenged by biotic and abiotic stresses and the relatively high production requirements compared with other crops. To successfully address these challenges, it is important to understand peanut physiology and its essential requirements for growth and development. This article reviews the requirements for growth including soil characteristics, water, nutrients, and environmental conditions. The physiological effects of stresses during seed storage and germination and during plant growth, development, and reproduction are discussed. Phenotyping based on physiological responses are also discussed.

Food insecurity in sub-Saharan Africa is partly due to low staple crop yields, resulting from poor soil fertility and low nutrient inputs. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, can contribute to increasing yields and sustaining soil organic carbon (SOC) in the long term. Soil-crop simulation models can help assess the performance and trade-offs of a range of crop management practices including ISFM, under current and future climate. Yet, uncertainty in model simulations can be high, resulting from poor model calibration and/or inadequate model structure. Multi-model simulations have been shown to be more robust than those with single models and help understand and reduce modelling uncertainty. In this study, we aim to perform the first multi-model comparison for long-term simulations of crop yield and SOC and their feedbacks in SSA. We evaluated the performance of 16 soil-crop models using data from four long-term maize experiments at sites in SSA with contrasting climates and soils. Each experiment had four treatments: i) no exogenous inputs, ii) addition of mineral nitrogen (N) fertilizer, iii) use of organic amendments, and iv) combined use of mineral and organic inputs. We assessed model performance in two steps: through blind calibration involving a minimum level of experimental data provided to the modeling teams, and subsequently through full calibration, which included a more extensive set of observational data. Model ensemble accuracy was greater with full calibration than blind calibration. Improvement in model accuracy was larger for maize yields (nRMSE 48 vs 18%) than for topsoil SOC (nRMSE 22 vs 14%). Model ensemble uncertainty (defined as the coefficient of variation across the 16 models) increased over the duration of the long-term experiments. Uncertainty of SOC simulations increased when organic amendments were used, whilst uncertainty of yield predictions was largest when no inputs were applied. Our study revealed large discrepancies among the models in simulating i) crop-to-soil feedbacks due to uncertainties in simulated carbon coming from roots, and ii) soil-to-crop feedbacks due to large uncertainties in simulated crop N supply from soil organic matter decomposition. These discrepancies were largest when organic amendments were applied. The results highlight the need for long-term experiments in which root and soil N dynamics are monitored. This will provide the corresponding data to improve and calibrate soil-crop models, which will lead to more robust and reliable simulations of SOC and crop productivity, and their interactions.

Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predictions of soil processes like seed germination, decomposition, nitrification, evaporation, and carbon sequestration. To assess how well such models can simulate soil temperature, herein we present results of an inter -comparison study of 33 maize ( Zea mays L.) growth models. Among the 33 models, four of the modeling groups contributed results using differing algorithms or "flavors" to simulate evapotranspiration within the same overall model family. The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured continually at several depths. The range of simulated soil temperatures was large (about 10-15 degrees C) from the coolest to warmest models across whole growing seasons from bare soil to full canopy and at both shallow and deeper depths. Within model families, there were no significant differences among their simulations of soil temperature due to their differing evapotranspiration method "flavors", so root -mean -square -errors (RMSE) were averaged within families, which reduced the number of soil temperature model families to 13. The model family RMSEs averaged over all 20 treatment -years and 2 depths ranged from about 1.5 to 5.1 degrees C. The six models with the lowest RMSEs were APSIM, ecosys, JULES, Expert -N, SLFT, and MaizSim. Five of these best models used a numerical iterative approach to simulate soil temperature, which entailed using an energy balance on each soil layer. whereby the change in heat storage during a time step equals the difference between the heat flow into and that out of the layer. Further improvements in the best models for simulating soil temperature might be possible with the incorporation of more recently improved routines for simulating soil thermal conductivity than the older routines now in use by the models.

The study was conducted to evaluate the yield performance of improved forage and food crops and to estimate the profitability of fodder and food crops in Holetta and Ejere areas, central highlands of Ethiopia during 2019 and 2020 cropping seasons. The experiment was laid out in randomized complete block design with three replications and evaluated two annual forage crops, two perennial forage crops, and two cereal food crops. Data on herbage dry matter (DM) yield was collected for forage crops while straw and grain yields were measured for food crops. Partial budget analysis was made to evaluate the economic feasibility of forage and food crops production. The result revealed that the herbage DM yield varied (P<0.05) at each location during each production year and combined over years and between the two locations. The straw and grain yields of food crops also varied (P<0.05) between the two production years and locations. The nutritive values of herbage and straw yields of forage and food crops differed (P<0.05) for all measured parameters. The crude protein and in-vitro dry matter digestibility of perennial forage crops were relatively higher than annual forage crops and straw of food crops. The partial budget analysis result indicated that the gross revenue (GR) and net return (NR) obtained from food crops were the highest followed by annual forage crops while the least was recorded from perennial forage crops during the first year of production. However, perennial forage crops produced the higher GR and NR than food crops and annual forage crops in the second year of production. In the second year of production, among the forage crops, Desho grass generated 308, 293, 287, and 232% while Rhodes grass generated 99, 92, 90, and 62% more NR than wheat, sole oat, barley, and oat/vetch mixtures, respectively. The benefit-cost ratio (BCR) of perennial forage crops was the lowest (3.0 for Desho and 1.6 for Rhodes) in the first year of production but the ratio was the highest (17.2 for Desho and 8.6 for Rhodes) in the second year of production. This confirms the better economic feasibility of perennial forage crops as they can be maintained using minimal management cost once they have been established.

Technology development has progressed in recent decades in terms of breeding and agronomy of major crops such as wheat, rice, and maize. Nevertheless, production of these major crops is under pressure from climate change and threats of marginal soil nutrients in degraded agricultural lands. Underutilized crops are identified as resilient to local environments that can cope with climatic variations. Bambara groundnut, an African legume that is grown in low input subsistence farming systems in sub-Saharan Africa and Asia has been identified as a potential future crop under climate change. Improving its productivity and suitability under future climates and future locations, requires comprehensive analysis of growth and development of Bambara groundnut under different growing conditions. Process-based crop simulation models facilitate the evaluation of management options for variable growing environments and soil conditions. Therefore, the current study was aimed to evaluate growth and development of Bambara groundnut and to adapt the generic legume model CROPGRO for explaining crop response to management and environment. The parameters used for modelling growth and development of peanut (Arachis hypogea) served as the initial reference values for adapting CROPGRO model. Crop information from the published sources were applied to the functions and parameters of the model. Crop phenology, canopy development, growth and yield were calibrated for two contrasting Bambara groundnut landraces using experimental data from controlled-environment experiments conducted in 2003 and 2006 at Tropical Crops Research Unit (TCRU), Sutton Bonington Campus, University of Nottingham, UK. Overall, the model calibration results of Leaf Area Index (LAI), total biomass and yield were well predicted with d-statistics greater than 0.89. The cardinal base temperatures for leaf photosynthesis of Bambara groundnut had to be set higher than those for peanut, in order to simulate the reduced growth observed during relatively low temperatures. The value for the specific leaf weight (SLWREF) at which light-saturated leaf photosynthesis is defined, was increased from 0.0043 to 0.0063 g cm(-2) to reduce the productivity of Bambara groundnut compared to peanut. The new model will be incorporated in the DSSAT version 4.8 software and will provide capability for assessing management practices in matching environments and suggesting potential regions for future expansion of Bambara groundnut cultivation into resilient cropping systems.

IntroductionMaralfalfa grass (Pennisetum spp.) is known for its high dry matter productivity and nutritive value. However, information on agronomic management practices to improve yield and nutritive value in Ethiopia is sparse.Materials and methodThe experiment consisted of 7 plant densities (33,333 [75 cm × 40 cm]; 26,667[75 cm × 50 cm]; 25,000 [100 cm × 40 cm]; 22,222 [75 cm × 60 cm]; 20,000 [100 cm × 50 cm]; 16,667 [100 cm × 60 cm]; and 13,333 [125 cm × 60 cm] plants per hectare, and arranged in randomized complete block design with three replications.ResultsThe results indicated that the year had a significant (P<0.001) effect on measured traits. Plant densities had no significant (P>0.05) effect on the number of nodes and leaves per plant, leaf length, and basal diameters. The number of tillers per plant varied significantly among plant densities only in the first year at the 1st harvest. Plant height in the first year was significantly (P<0.01) greater than in the second year.DiscussionAnnual dry matter production and annual crude protein production of Maralfalfa grass were not significantly affected by plant density. Nutritive value parameters (CP, Ash, NDF, ADF, ADL, and IVDMD) were not significantly (P > 0.05) influenced by plant density. Using a lower plant density could reduce the amount of planting material, transport, and labor costs.ConclusionHowever, further studies on Maralfalfa grass should be conducted in multi-locations of Ethiopia both under rain-fed and irrigated conditions with various agronomic practices.

A BSTRACT . Soybean [Glycine max (L.)] is one of the main sources of food protein in the world. To achieve a high yield and grain protein concentration, soybean requires a large amount of nitrogen (N). Many studies have evaluated N fertilization in soybean, with the hypothesis that symbiotic N fixation may not be sufficient to supply N for high-yielding soybean. This study aimed to evaluate the performance of the CSM-CROPGRO-Soybean model with field data to determine the impact of N fertilizer on soybean and explore long-term scenarios. The experiments consisted of a combination of available water management and N fertilizer levels over eight crop seasons. After calibration and evaluation, the CSM-CROPGRO-Soybean model was used to explore the soil and plant N balance. We also applied the model to explore different N fertilization rates in long-term scenarios. The results showed that the model was able to simulate soybean growth with a very good agreement for all sites (D-statistic ranging from 0.63 to 0.99). The observed and simulated crop yield gains from N fertilization were small and not proportional to the amount of N fertilizer applied. The model underestimated the effect of N fertilizer on grain protein concentration increment, but there was a consistent trend towards an increase in protein concentration observed in measured data. Simulations of soil inorganic N balance suggested very high losses of N by volatilization for urea-N fertilization treatments. The long-term scenarios showed that using N rates above 600 kg ha-1 is potentially inappropriate, and a higher crop N use efficiency was obtained with no N fertilizer.

Crop models are useful tools for simulating agricultural systems that require continued model development and testing to increase their robustness and improve how they describe our current understanding of processes. Coordinated and "blind" evaluation of multiple models using same protocols and experimental datasets provides unique opportunities to further improve models and enhance their reliability. For soybean [Glycine max (L.) Merr.], there has been limited coordinated multi-model evaluations for the simulation of in-season plant growth dynamics. We evaluated ten dynamic soybean crop models for their simulation of in-season plant growth using data from five experiments conducted in Argentina, Brazil, France, and USA. We evaluated models after a Blind (using only phenology data) and a Full calibration (with in-season and end-of-season variables). Calibration reduced model uncertainty by reducing standard bias for the simulation of in-season variables (biomass, leaf, pod, and stem weights, and leaf area index, LAI). However, we found that most models had difficulty in reproducing leaf growth dynamics, with normalized root mean squared error (nRMSE) of 56% for leaf weight and 43% for LAI (across locations and models after Full calibration). Models with different levels of complexity and experience were capable of simulating final seed yield at maturity with reasonable accuracy (nRMSE of 8-31% after Full calibration). However, the nRMSE for pod weight (of 17-64% after Full calibration) was two-fold larger than that of seed yield. Moreover, the models differed in how they simulated timing from sowing to beginning seed growth (47-93 days) and effective seed filling period (18-54 days), owing to model structural differences in defining the reproductive developmental stages. Overall, we identified the following processes that can benefit from further model improvement: leaf expansion and senescence, reproductive phenology, and partitioning to reproductive growth. Simulation of pod wall tissue and individual seed cohorts is another aspect that many models currently lack. Model improvement can benefit from high-temporal resolution experimental datasets that concurrently account for phenology, plant growth, and partitioning. Further, we recommend collecting reproductive phenology in the field consistent with actual dry matter allocation to organs in the models and collecting multiple observations of seed and pod weight to aid model improvement for simulation of seed growth and yield formation.