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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 Capão do Leão, 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 (WFPSthresh = 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 WFPSthresh 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.

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 (P0.05) 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 0.05) 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.

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

Livestock feeding in Burkina Faso is characterized by a recurrent deficit in both the quality and the quantity of fodder during the dry season, which affects animal performance. To overcome this, quality fodder/forage production is an alternative. Therefore, this study evaluated food- and feed-improved cultivars of maize and cowpea in intercropping trials using the “mother and baby trials” approach with crop–livestock farmers. The mother trial comprised a randomized block design with eight treatments and four replicates: two cowpea (KVx745-11P and Tiligré) and two maize cultivars (Barka and Espoir), and grown under two cropping systems (monoculture and intercropping). Baby trials were established on-farm and involved 30 farmers during two seasons (2019 and 2020) in four villages in the South Sudan zone of Burkina Faso. Data were collected on (1) weed density and biomass, (2) grain yield and fodder biomass, (3) intercropping efficiency, and (4) fodder nutritive value. Data were analyzed using ANOVA and the least significant difference (LSD) means separation at a 5% threshold. The results revealed that maize and cowpea intercropping significantly reduced weed biomass (p ≤ 0.05). In monoculture, the maize cultivar Barka produced a greater grain yield (4980 kg/ha) and fodder biomass [6259 kg dry matter (DM)/ha] than the cultivar Espoir, which produced a grain yield of 2581 kg/ha and fodder biomass of 4952 kg DM/ha. The cowpea cultivars, KVx745-11P and Tiligré, were similar (p ≥ 0.05) in terms of fodder biomass (2435–2820 kg DM/ha) and grain yield (1152–1163 kg/ha). For the intercropping system, land equivalent ratios for fodder biomass (1.18:1.41) and grain yield (1.02:1.44) were greater than 1; intercropping also had better productivity system indexes than the monoculture cropping system. The crude protein concentration of fodder was greater for Barka maize (9.5%–9.8%) than for Espoir maize (8.5%–8.7%). The crude protein concentration was greater for cowpea KVx745-11P (19%–21.8%) than for cowpea Tiligré (15%–17%). Intercropping both Barka maize and cowpea KVx745-11P was the most productive cropping system for maximizing grain and fodder production for crop–livestock farmers in the South Sudan zone of Burkina Faso.

Crop insurance has been an important mechanism used by farmers to protect the economic and yield losses produced by extreme weather events. Alternatively, index-based insurance (IBI) has been evaluated in many parts of the world as a mechanism to avoid moral hazard and adverse selection from traditional crop insurance products. However, there is a lack of knowledge on how the sowing date impacts the IBI triggers and tick size. In addition, few studies have evaluated how the frequency of payouts is affected by climate phenomena such as the El Niño-Southern Oscillation (ENSO). In this study, we aimed to develop a drought index based-insurance for soybean (Glycine max (L.) Merr) in the Southeastern US, where the triggers and payouts were quantified for sowing dates and ENSO phases. Simulated soybean life cycle and grain yield for multiple sites, sowing dates, and maturity groups were used to calibrate the index triggers. The index triggers were assessed based on the period of measurement, sowing time, and ENSO phase. Our results show that the accumulation of water stress between flowering and physiological maturity using quantile regression increased the accuracy of payments. The results also highlighted a higher frequency of payments triggered when soybean seasons in the Southeastern US were under La Niña influence. The results also presented larger payments generated when the soybean was sown during April and May in comparison to later sowing dates. These results could be used by insurance companies and public administration to improve the efficiency of IBI products offered.

Abstract Brassica carinata (carinata), a non‐food oilseed feedstock mainly used for biofuel, is a relatively new alternative winter crop in the southeastern (SE) United States (US). However, there are limited N rate and N application timing data available at the regional scale. These data are needed to expand production in the SE US. An N rate study was conducted during the winter–spring growing seasons during 2017–2018 and 2018–2019 in Florida, US, and at three locations during 2018–2019 in Georgia, US, to quantify the effects of N rate (0, 45, 90, 134, and 179 kg N ha−1) on carinata nutrient uptake, biomass, seed yield, and seed chemical composition. Seed yield showed a linear response up to 134 kg N ha−1. Seed protein and glucosinolate concentrations decreased from 0 to 90 kg N ha−1, then increased from 90 to 179 kg N ha−1. Seed oil concentration was inversely related to seed protein concentration. A two‐factor N application timing study (4 N application timing: at‐plant, pre‐bolting, at‐plant + pre‐bolting, at‐plant + pre‐bolting + bolting × 4 N rates: 0, 45, 90, and 134 kg N ha−1) was conducted in Georgia, US, over three site‐years to quantify the effect of N application timing on yield and agronomic and economic optimum N rates (AONR and EONR, respectively). All split applications increased AONR by at least 10 kg N ha−1 compared to a single at‐plant application. A two‐split N application was more profitable than either a single N application or a three‐split N application based on marginal return. A two‐way split application (at‐plant + pre‐bolting) at 134 kg N ha−1 is recommended to optimize yield and economical production. Based on uncertainty analyses, the 50% credible interval of EONR occurred between 116 and 152 kg N ha−1, with a median estimate at 130 kg N ha−1.

Fruit quality is of increasing importance for consumers but is a complex trait for growers, as it is affected by environment, genotype, and crop management interactions. Decision support tools, such as computer models that simulate crop growth and development can help optimize production but require further improvement to simulate quality aspects. The goal of this study was to apply the newly developed CROPGRO-Strawberry model in the Decision Support System for Agrotechnology Transfer (DSSAT) model framework and develop a module for the dynamic prediction of quality traits for strawberry. Experimental data from Florida with quality measurements from multiple harvests were correlated with indices based on preharvest weather conditions (temperature, radiation, rainfall) and simulated model parameters (evapotranspiration) during fruit development. Two quality relationships based on linear equations were identified and integrated into the model to simulate strawberry fruit soluble solids content (r2 = 0.89, d = 0.97) and titratable acidity (r2 = 0.55, d = 0.85) based on preharvest temperature. A strategic analysis with historical weather data for a subtropical growing region over a 10-year period showed the importance of seasonal climate variability for simulated strawberry yield and fruit quality across different harvest months. The improved CROPGRO-Strawberry model is the first process-based crop model to predict selected quality traits across multiple harvests throughout the season and can be extended to other crop models for which quality traits are important.

Abstract Brassica carinata is a non‐food, low‐carbon, none‐to‐low indirect land‐use change impact oilseed feedstock grown for sustainable biofuels, bioproducts, and high protein seed meal. Understanding carinata growth and development is critical to the development of best management practices for maximum productivity and profitability of double‐cropped farming systems. Field experiments were conducted during 2017–2018 (Year 1) and 2018–2019 (Year 2) winter–spring growing seasons in Quincy, Florida, to quantify the total aboveground dry matter accumulation (TDM), allocation, growth, nutrient uptake, and seed quality. The two carinata cultivars Avanza 641 and AX17012 accumulated 10826 and 9343 kg TDM ha−1 in Year 1 and 9655 and 10,642 kg TDM ha−1 in Year 2, respectively, at harvest maturity. The proportion of DM in the vegetative parts such as leaves and stems decreased, and the DM in reproductive structures such as silique walls and seeds increased with maturity. Seed yield (SY) was similar between cultivars but differed between years with Year 1 (2732 kg ha−1), producing 29% greater SY than Year 2 (1929 kg ha−1). Carinata primary stem has 17–20 leaf nodes, with 75%–88% of the axillary meristems producing primary and secondary branches. Crop growth rate (CGR) increased from vegetative to pod development for both cultivars in Year 1, while in Year 2, AX17012 and Avanza 641 attained maximum CGR at the pod development and bolting/flowering stages, respectively. Maximum seasonal nutrient uptake in leaves, stems, and siliques generally occurred in the early, mid, and late season, respectively, in both years. Seed weight, oil, monounsaturated fatty acids, C18:3, C20:1, and C22:1 concentrations increased with plant age, whereas protein, glucosinolate, polyunsaturated fatty acids, saturated fatty acids, C18:1, and C18:2 concentrations decreased with plant maturity. These results can be used for crop modeling to predict growth, development, and yield and aid in developing in‐season decision support tools.

Abstract Carinata (Brassica carinata) is an oilseed crop which, because of its non‐edible oil composition and favorable fatty acid profile, is proposed as a “green” sustainable aviation fuel. It can be grown as a winter crop in the southeastern USA or as a summer annual crop in northern latitudes. No crop models exist for carinata because it is a relatively new crop. The CROPGRO model is a mechanistic crop simulation of daily crop growth and development as a function of daily weather, soil properties, crop management, and species parameters. We adapted the CROPGRO model to simulate carinata based on growth analysis data collected over two seasons at three sites: Quincy, FL, Jay, FL, and Shorter, AL. The adaptation process required literature knowledge as well as optimization against field observations. The parameterization of model sensitivities to climatic factors is presented. The adapted model gave good simulations of carinata growth dynamics compared to observed growth during different seasons and locations and in response to N fertilization. While additional testing is appropriate, the model is sufficiently ready to be used for various applications. An example application is presented for the effect of sowing date on carinata yield and maturity over long‐term weather in the Southeastern USA.

Abstract The environmental consequences of using nonrenewable fossil fuels have motivated a global quest for sustainable alternatives from renewable sources. Carinata has been developed as a low carbon intensity, non‐food oilseed biomolecular platform to produce advanced drop‐in renewable fuels, meal, and co‐products. The crop is widely adaptable to grow in the humid subtropical and humid continental climatic regions of Asia, Africa, North America, South America, Europe, and Australia as a spring or winter crop. Carinata is heat tolerant, resistant to diseases and seed shattering with lower water‐use requirements than other oilseed brassicas. Adopting carinata in double‐cropping systems would require continuing research to integrate crop biology with agronomy, to understand growth and development and its interaction with agricultural inputs and management. Site‐specific best management agronomic practices and crop improvement research to develop frost‐tolerant, early‐maturing, nutrient use‐efficient, and high yielding varieties with desirable oil content and fatty acid profile will enhance the crop's adaptability and economic viability. The exploitation of intra‐ and interspecific and intra‐ and intergeneric diversity will further enhance carinata productivity and resistance to biotic and abiotic stresses. This review attempts to present a comprehensive description of carinata's biology, beginning with its origin and current state of distribution, availability of genetic and genomic resources, and a discussion of its morphology, phenology, and reproduction. A detailed analysis of the agronomy of the crop, including planting and germination and management practices, is presented in the context of crop growth and development. This will facilitate global adoption, sustainable production, and commercialization of carinata as a dedicated biofuel oilseed crop in diverse cropping systems and growing regions of the world, including the Southeast United States.

Sustainable aviation fuel (SAF) production from lipids is a technologically mature approach for replacing conventional fossil fuel use in the aviation sector, and there is increasing demand for such feedstocks. The oilseed Brassica carinata (known as Ethiopian mustard or simply carinata) is a promising SAF feedstock that can be grown as a supplemental cash crop over the winter fallow season of various annual crop rotations in the Southeast US, avoiding land use changes and potentially achieving some of the soil carbon sequestration and ecosystem service benefits of winter cover crops. However, carinata may require more intensive management than traditional cover crops, potentially leading to additional soil greenhouse gas (GHG) emissions through increased carbon losses from soil tillage and nitrous oxide (N2O) emissions from nitrogen fertilizer application. In this work, the 2017 version of the process-based DayCent ecosystem model was used to establish initial expectations for the total regional SAF production potential and associated soil GHG emissions when carinata is integrated as a winter crop into the existing crop rotations across its current suitability range in southern Alabama, southern Georgia, and northern Florida. Using data from academic and industry carinata field trials in the region, DayCent was calibrated to reproduce carinata yield, nitrogen response, harvest index, and biomass carbon-to-nitrogen ratio. The resulting model was then used to simulate the integration of carinata every third winter across all 2.1 Mha of actively cultivated cropland in the study area. The model predicted regional average yields of 2.9–3.0 Mg carinata seed per hectare depending on crop management assumptions. That results in the production of more than two million Mg of carinata seed annually across the study area, enough to supply approximately one billion liters of SAF. Conventional management of carinata led to only modest increases in soil carbon storage that were largely offset by additional N2O emissions. Climate-smart management via adopting no-till carinata establishment or using poultry litter as a nitrogen source resulted in a substantial net soil GHG sink (0.23–0.31 Mg CO2e ha−1 y−1, or 0.24–0.32 Mg CO2e per Mg of seed produced) at the farms where carinata is cultivated.

A study was conducted in four compartments of a polycarbonate greenhouse at Gainesville, FL, USA to investigate how a soybean (Glycine max L. Merr.) cultivar, Maverick (maturity group III, indeterminate), responded to three elevated temperatures, ELT, (day/night of 34/26 °C, 38/30 °C, and 42/34 °C) in comparison to a control growth temperature (30/22 °C). Carbon dioxide (CO2) concentration was maintained at 700 μmol mol−1 in each compartment by a processor controlled air-sampling and CO2-injection system. Three sequential experiments were conducted at different times of year (summer, autumn, and early spring) to investigate the effect of intensity, timing, and duration of ELT on soybean node number, internode elongation, mainstem length, and number of pods set per plant. At the control temperature, the soybean plants grown in the polycarbonate greenhouse were taller than field-grown plants. When plants were grown under continuous ELT applied soon after sowing or at initial flowering, the number of nodes increased with increasing ELT intensity, whereas the length of individual internodes decreased. When ELT treatment was applied during the beginning of flowering stage (R1–R2) or earlier, more nodes were produced and the length of affected internodes was decreased. When the ELT was imposed later at reproductive stage R5+ just before the beginning of seed filling, effects on node numbers and internode lengths were negligible. Short-term (10-day) duration of ELT applied at four stages from V3 to R5+ did not significantly affect final mean numbers of nodes or mean mainstem lengths. Possible mechanisms of elevated temperature effects on soybean internode elongation and node number (internode number) are discussed. Total pod numbers per plant increased linearly with mainstem node numbers and mainstem length. Furthermore, total pod numbers per plant were greatest at 34/26 °C rather than at the control temperature of 30/22 °C (and remained high at 38/30 °C). Mild increases in temperature might not threaten, but actually increase, yields of soybean in northerly zones where this crop is currently grown at slightly suboptimal temperatures. However, a sustained increase in ambient temperature would likely threaten soybean yields.

Climate change and population growth pose great challenges to the food security of the millions of people who grow maize in the already fragile agricultural systems in tropical environments. There is an urgent need for maize varieties that are both drought and heat tolerant given the already prevailing drought and heat stress levels in many tropical environments, which are set to exacerbate with climate change. In this study, the crop growth simulation model for maize (CERES-Maize) was used to quantify the impact of climate change on maize and the potential benefits of incorporating drought and heat tolerance into the commonly grown (benchmark) maize varieties at six sites in Eastern and Southern Africa and one site in South Asia. Simulation results indicate that climate change will have a negative impact on maize yield at all the sites studied but the degree of the impact varies with location, level of warming and rainfall changes. Combined hotter and drier climate change scenarios (involving increases in warming with a reduction in rainfall) resulted in greater average simulated maize yield reduction (21, 33 and 50% under 1, 2 and 4 °C warming, respectively) than hotter only climate change scenarios (11, 21 and 41%, respectively). Incorporating drought, heat and combined drought & heat tolerance into benchmark varieties increased simulated maize yield under both the baseline and future climates. The average simulated benefit from combined drought & heat tolerance was at least twice that of heat or drought tolerance and it increased with the increase in warming levels. The magnitude of the simulated benefits from drought tolerance, heat tolerance and combined drought & heat tolerance and potential acceptability of the varieties by farmers varied across sites and climate scenarios indicating the need for proper targeting of varieties where they fit best and benefit most. It is concluded that incorporating drought and heat tolerance into maize germplasm has the potential to offset predicted yield losses and sustain maize productivity under climate change in vulnerable sites. Keywords: Climate change, Maize, Drought tolerance, Heat tolerance, Tropical environments

The common bean is a tropical facultative short-day legume that is now grown in tropical and temperate zones. This observation underscores how domestication and modern breeding can change the adaptive phenology of a species. A key adaptive trait is the optimal timing of the transition from the vegetative to the reproductive stage. This trait is responsive to genetically controlled signal transduction pathways and local climatic cues. A comprehensive characterization of this trait can be started by assessing the quantitative contribution of the genetic and environmental factors, and their interactions. This study aimed to locate significant QTL (G) and environmental (E) factors controlling time-to-flower in the common bean, and to identify and measure G × E interactions. Phenotypic data were collected from a biparental [Andean × Mesoamerican] recombinant inbred population (F11:14, 188 genotypes) grown at five environmentally distinct sites. QTL analysis using a dense linkage map revealed 12 QTL, five of which showed significant interactions with the environment. Dissection of G × E interactions using a linear mixed-effect model revealed that temperature, solar radiation, and photoperiod play major roles in controlling common bean flowering time directly, and indirectly by modifying the effect of certain QTL. The model predicts flowering time across five sites with an adjusted r-square of 0.89 and root-mean square error of 2.52 d. The model provides the means to disentangle the environmental dependencies of complex traits, and presents an opportunity to identify in silico QTL allele combinations that could yield desired phenotypes under different climatic conditions.

Forage production is primarily limited by weather conditions under dryland production systems in Brazilian semi-arid regions, therefore sowing at the appropriate time is critical. The objectives of this study were to evaluate the CSM-CERES-Pearl Millet model from the DSSAT software suite for its ability to simulate growth, development, and forage accumulation of pearl millet [Pennisetum glaucum (L.) R.] at three Brazilian semi-arid locations, and to use the model to study the impact of different sowing dates on pearl millet performance for forage. Four pearl millet cultivars were grown during the 2011 rainy season in field experiments conducted at three Brazilian semi-arid locations, under rainfed conditions. The genetic coefficients of the four pearl millet cultivars were calibrated for the model, and the model performance was evaluated with experimental data. The model was run for 14 sowing dates using long-term historical weather data from three locations, to determine the optimum sowing window. Results showed that performance of the model was satisfactory as indicated by accurate simulation of crop phenology and forage accumulation against measured data. The optimum sowing window varied among locations depending on rainfall patterns, although showing the same trend for cultivars within the site. The best sowing windows were from 15 April to 15 May for the Bom Conselho location; 12 April to 02 May for Nossa Senhora da Gloria; and 17 April to 25 May for Sao Bento do Una. The model can be used as a tool to evaluate the effect of sowing date on forage pearl millet performance in Brazilian semi-arid conditions.

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.