Sorghum is the world’s fifth major cereal in terms of production and acreage. It is expected that its growth will be affected by the increase in air temperature, an important component of global climate change. Our objective was to use the Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model to (a) evaluate the impact of climate warming on forage and grain sorghum production in Argentina and (b) to analyze to what extent yield changes were associated with changes in water or nitrogen stress days. For model calibration, we used previous information related to the morpho-physiological characteristics of both sorghum types and several soil parameters. We then used multiyear field data of sorghum yields for model validation. Yield simulations were conducted under three possible climate change scenarios: 1, 2, and 4 °C increase in mean annual temperature. ALMANAC successfully simulated mean yields of forage and grain sorghum: root mean square error (RMSE): 2.6 and 1.0 Mg ha⁻¹, respectively. Forage yield increased 0.53 Mg ha⁻¹, and grain yield decreased 0.27 Mg ha⁻¹ for each degree of increase in mean annual temperature. Yields of forage sorghum tended to be negatively associated with nitrogen stress (r = −0.94), while grain sorghum yield was negatively associated with water stress (r = −0.99). The information generated allows anticipating future changes in crop management and genetic improvement programs in order to reduce the yield vulnerability.

Crop models with well-tested parameters may help improve sugarcane productivity for food and biofuel generation, especially in rainfed areas where studies are scarce. This study aimed to calibrate crop parameters for the sugarcane cultivar CP 72-2086, an early-maturing cultivar widely grown in Mexico and other countries, and evaluate their adequacy in simulating sugarcane in a diverse range of rainfed conditions. For the calibration and evaluation of parameters, the ALMANAC model was used with climate, soil, management, and yield for two growing seasons from 30 farms in three regions (Northeastern Mexico, Gulf of Mexico, and Pacific Mexico). Statistical analyses were made using regression analysis and mean squared deviation and its three components, i.e., the squared bias, the lack of correlation weighted by the standard deviations, and the squared difference between standard deviations. Model simulations with a light extinction coefficient (k) of 0.69, maximum leaf area index of 7.5, leaf area index decline rate of 0.3, optimal and minimum temperature for plant growth of 32 °C and 11 °C, respectively, potential heat units of 6000 to 7400 degree days (base 11 °C), harvest index of 0.9; maximum crop height of 4.0 m, and root depth of 2.0 m showed highest accuracy and captured best the magnitude of yield fluctuations with a root mean squared deviation of 7.8 Mg ha⁻¹. The parameters were found to be reasonable to use in simulating sugarcane in diverse regions under rainfed conditions. Using a dynamic value of k (varying during the growing season) deserves further study as it may help improve crop model precision.

Corn (Zea mays L.) stover removal thresholds are subject to many site-specific factors. Current data do not provide practical stover removal guidelines for given site-specific conditions. We used the Agricultural Policy Environmental Extender (APEX) model to assess the effects of site-specific factors and corn stover removal from 3,703 farm fields within the Upper Mississippi River Basin (UMRB). From among the many management and resource attribute factors across these farm fields, we chose the site-specific factors of two land types (highly erodible land [HEL] and non-HEL), three soil textures (clayey, loamy, and sandy), four hydrologic groups (A, B, C, and D), and two management scenarios (Baseline and Enhanced Conservation Treatment) to characterize the variability of stover production potential. The Baseline management reflects farmer management practices reported in the Conservation Effects Assessment Project National Cropland Assessment database, while the Enhanced Conservation Treatment reflects additional conservation practices and management scenarios needed to mitigate sediment, nutrient, and soil organic carbon (SOC) losses. For evaluation purposes, we used the Conservation Effects Assessment Project National Cropland Assessment “acceptable planning criteria” but with more stringent SOC criteria, which does not allow stover removal on sites that lose SOC. Overall, grain and stover yields and N and P losses decreased, while sediment and SOC losses increased with increasing stover removal. On average, Baseline stover yields on HEL were slightly lower than on non-HEL, while yields were higher on loamy, followed by clayey, and then sandy soils. Hydrologic group D soils had the highest Baseline stover yields, followed by those in group B, then group C, and then group A with the lowest yields. The Enhanced Conservation Treatment management drastically mitigated sediment and nutrient losses by 69% and 57%, respectively. Differential impacts of stover removal were most pronounced among the soil textural classes, followed by soil hydrologic groups, and then land type. Overall, the findings of this research underscore the importance of site-specific factors in determining corn stover removal thresholds. For the Upper Mississippi River Basin and for sites meeting the “acceptable planning criteria,” sufficiently “safe” corn stover removals are possible such that only a portion of the available corn acreage would be required to produce enough ethanol to exceed the National 2012 Energy Independence and Security Act goal. However, we do not evaluate issues associated with ethanol plant economic feasibility, such as spatial concentration of stover production.

Considerations for using corn (Zea mays L.) residues for biofuel should include impacts on the subsequent soybean [Glycine max (L.) Merr.] crop. We used the Agricultural Policy Environmental Extender (APEX) model to study the effects of: four rates of corn residue removals, 0%, 40%, 60%, and 80%, across site-specific conditions of two land types, highly erodible land (HEL) and non-HEL; three soil textural classes, clayey, loamy, and sandy; and the four soil hydrologic groups, A, B, C, and D, on soybean yield and nitrogen (N) dynamics, for 3703 farm fields within the Upper Mississippi River Basin. Overall, residue removal reduced soybean yield, N fixation, uptake, losses, and soil storage by 8%, 6%, 7%, and 5%, respectively. There is a positive tradeoff of residue removal, that is, it reduces the pool of nutrients available for loss, which resulted in a 9% decline in N losses when compared to no residue removal. Corn residue removal-induced reduction in soil organic N storage could affect the magnitude of the soybean N credit, implying that increased fertilization rates could be needed to sustain corn productivity in corn/soybean rotation systems. Responses were however subject to site-specific conditions, key considerations when determining residue removal thresholds.

Mexico holds the largest single bean production area in the world that is vulnerable to drought. Using field data and two future climate scenarios (RCP4.5 and RCP8.5) for the period 2020–2039, this study evaluated three common bean (Phaseolus vulgaris L.) cultivars planted under rainfed conditions at different densities in two locations in the north-central Mexican semi-arid temperate highlands. The sowing densities were 90,000, 145,000, and 260,000 plants ha⁻¹ established in single rows (SR), three rows (3R), and six rows (6R), respectively. The climate change scenarios were derived from an assembly model integrating 11 general circulation models (GCM) selected for Mexico with a 30” arc resolution. The baseline climate was for the period 1961–2010. The ALMANACMEX model (USDA-ARS-INIFAP, Temple, USA) was parameterized and evaluated and then re-run using the climate scenarios. Beans planted at 6R showed the highest increase in seed yield in both climate scenarios, although the response varied by cultivar and time periods. For the growth habit III cultivars, Flor de Mayo Bajio showed no difference in yield, while Pinto Saltillo, a drought-resistant cultivar, showed increases of 13% to 16% at 6R only until 2033. Growth habit I cultivar Azufrado 2 showed more than 60% increases at 6R in both climate scenarios for the full period 2020–2039. These results suggest that considering the projected climate conditions, high sowing densities may be a viable agronomic option for common bean production under rainfed conditions in semi-arid temperate regions, such as the highlands of Mexico, in the near future; however, the selection of the cultivar is a key element to consider in this regard.

Given a rising demand for quality assurance, rather than solely yield, supplemental irrigation plays an important role to ensure the viability and profitability of vegetable crops from unpredictable changes in weather. However, under drought conditions, agricultural irrigation is often given low priority for water allocation. This reduced water availability for agriculture calls for techniques with greater irrigation efficiency, that do not compromise crop quality and yield, and that provide economic benefit for producers. This study developed vegetable growing models for eight different vegetable crops (bush bean, green bean, cabbage, peppermint, spearmint, yellow straight neck squash, zucchini, and bell pepper) based on data from several years of field research. The ALMANAC model accurately simulated yields and water use efficiency (WUE) of all eight vegetables. The developed vegetable models were used to evaluate the effects of various irrigation regimes on vegetable growth and production in several locations in the Winter Garden Region of Texas, under variable weather conditions. Based on our simulation results from 960 scenarios, optimal irrigation amounts that produce high yield as well as reasonable economic profit to producers were determined for each vegetable crop. Overall, yields for all vegetables increased as irrigation amounts increased. However, irrigation amounts did not have a sustainable impact on vegetable yield at high irrigation treatments, and the WUEs of most vegetables were not significantly different among various irrigation regimes. When vegetable yields were compared with water cost, the rate decreased as irrigation amounts increased. Thus, producers will not receive economic benefits when vegetable irrigation water demand is too high.