Explore the vast range of titles available.

The impact of climate change on agricultural systems is a major concern as it can have a significant effect on the world food supply. The objective of this study was to evaluate climate change impacts on crop production and nitrate leaching in two distinct climatic zones in Canada. Spring wheat (Triticum aestivum L.) was selected for the semiarid regions of Western Canada (Swift Current, SK) and maize (Zea mays L.) was chosen for the more humid regions of central Canada (Woodslee, ON). Climate scenarios were based upon simulations from a Canadian Regional Climate Model (CanRCM4) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) and crop simulations were conducted using the Decision Support System for Agrotechnology Transfer (DSSAT) model. Compared to the baseline climate scenario, wheat yields increased by 8, 8, 11, 15%, whereas maize yields decreased by 15, 25, 22, 41% under RCP4.5 2050s (2041-2070), RCP4.5 2080s (2071-2100), RCP8.5 2050s and RCP8.5 2080s scenarios, respectively. Annual nitrate leaching increased by 19, 57, 73, 129% at Swift Current and by 84, 117, 208, 317% at Woodslee under the four scenarios, respectively. Adaptation measures suggested that fertilizer N rate for spring wheat should be increased to 80-100 kg N ha-1 to obtain optimal yields although this will result in an additional risk of 5-8 kg N ha-1 nitrate leaching at Swift Current. The fertilizer N rate of 150 kg N ha-1 was found to be suitable for high maize yields at Woodslee. New wheat and maize cultivars with long growing seasons would enable crop growth to match the phenological stage and hence maintain high crop yields to adapt to increased temperatures in the future.

The objective of this research was to assess the adoption of circular agricultural practices as a tool to improve the recovery use efficiency of phosphorus (P) applied to tropical soils. Two Brazilian farms (1 and 2) that are under long-term no-till and cropped year-round with cover and/or cash crops were used in this study. Soybean, maize and common bean were grown during the summer season (October–February), followed by wheat, common bean and maize during the winter season (February–August). Brachiaria ruziziensis was intercropped with off-season maize. Farm 1 also grew sweet potatoes in rotation with grains. In the integrated crop–livestock system, the leftovers from the silos and crop residues were used to feed beef cattle, while the residues not used in the confinement were turned into compost and applied in the production fields. During the last 3 years, 80 (farm 1) and 71 (farm 2) kg/ha/year of P-fertilizer was applied to meet the demand of the different crops and 56% (farm 1) and 58% (farm 2) of P-fertilizer was exported through the crops and livestock. P-recovery represented more than 50% on both farms. Around 60% of the P consumed by animals was excreted in the form of faeces and urine and the animal manure was used to produce organic compost. Therefore, most of the P consumed by the livestock was returned back to the field to serve as organic fertilizer. This study showed that circular agricultural practices can enhance P-recovery.

In silico plant modelling is the use of dynamic crop simulation models to evaluate hypothetical plant traits (phenology, processes and plant architecture) that will enhance crop growth and yield for a defined target environment and crop management (weather, soils, limited resource). To be useful for genetic improvement, crop models must realistically simulate the principles of crop physiology responses to the environment and the principles by which genetic variation affects the dynamic crop carbon, water and nutrient processes. Ideally, crop models should have sufficient physiological detail of processes to incorporate the genetic effects on these processes to allow for robust simulations of response outcomes in different environments. Yield, biomass, harvest index, flowering date and maturity are emergent outcomes of many interacting genes and processes rather than being primary traits directly driven by singular genetics. Examples will be given for several grain legumes, using the CSM-CROPGRO model, to illustrate emergent outcomes simulated as a result of single and multiple combinations of genotype-specific parameters and to illustrate genotype by environment interactions that may occur in different target environments. Specific genetically influenced traits can result in G × E interactions on crop growth and yield outcomes as affected by available water, CO2 concentration, temperature, and other factors. An emergent outcome from a given genetic trait may increase yield in one environment but have little or negative effect in another environment. Much work is needed to link genetic effects to the physiological processes for in silico modelling applications, especially for plant breeding under future climate change.

Thermal time models have been used to predict the development of many different species, including grapevine (Vitis vinifera L.). These models normally assume that there is a linear relationship between temperature and plant development. The goal of this study was to estimate the base temperature and duration in terms of thermal time for predicting veraison for four grapevine cultivars. Historical phenological data for four cultivars that were collected in the Pacific Northwest were used to develop the thermal time model. Base temperatures (T b) of 0 and 10 °C and the best estimated T b using three different methods were evaluated for predicting veraison in grapevine. Thermal time requirements for each individual cultivar were evaluated through analysis of variance, and means were compared using the Fisher’s test. The methods that were applied to estimate T b for the development of wine grapes included the least standard deviation in heat units, the regression coefficient, and the development rate method. The estimated T b varied among methods and cultivars. The development rate method provided the lowest T b values for all cultivars. For the three methods, Chardonnay had the lowest T b ranging from 8.7 to 10.7 °C, while the highest T b values were obtained for Riesling and Cabernet Sauvignon with 11.8 and 12.8 °C, respectively. Thermal time also differed among cultivars, when either the fixed or estimated T b was used. Predictions of the beginning of ripening with the estimated temperature resulted in the lowest variation in real days when compared with predictions using T b = 0 or 10 °C, regardless of the method that was used to estimate the T b.

This study presents results of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments (CGRA) of +1.5° and +2.0°C global warming above pre-industrial conditions. This first CGRA application provides multi-discipline, multi-scale, and multi-model perspectives to elucidate major challenges for the agricultural sector caused by direct biophysical impacts of climate changes as well as ramifications of associated mitigation strategies. Agriculture in both target climate stabilizations is characterized by differential impacts across regions and farming systems, with tropical maize Zea mays experiencing the largest losses, while soy Glycine max mostly benefits. The result is upward pressure on prices and area expansion for maize and wheat Triticum aestivum, while soy prices and area decline (results for rice Oryza sativa are mixed). An example global mitigation strategy encouraging bioenergy expansion is more disruptive to land use and crop prices than the climate change impacts alone, even in the +2.0°C scenario which has a larger climate signal and lower mitigation requirement than the +1.5°C scenario. Coordinated assessments reveal that direct biophysical and economic impacts can be substantially larger for regional farming systems than global production changes. Regional farmers can buffer negative effects or take advantage of new opportunities via mitigation incentives and farm management technologies. Primary uncertainties in the CGRA framework include the extent of CO₂ benefits for diverse agricultural systems in crop models, as simulations without CO₂ benefits show widespread production losses that raise prices and expand agricultural area.

Due to the potential impact of climate change and climate variability on rainfed production systems, both farmers and policy makers will have to rely more on short- and long-term yield projections. The goal of this study was to develop a procedure for calibrating the Cropping System Model (CSM)-CROPGRO-Soybean model for six cultivars, to determine the potential impact of climate change on rainfed soybean for five locations in Georgia, USA, and to provide recommendations for potential adaptation strategies for soybean production in Georgia and other south-eastern states. The Genotype Coefficient Calculator (GENCALC) software package was applied for calibration of the soybean cultivar coefficients using variety trial data. The root mean square error (RMSE) between observed and simulated grain yield ranged from 201 to 413 kg/ha for the six cultivars. Generally, the future climate scenarios showed an increase in temperature which caused a decrease in the number of days to maturity for all varieties and for all locations. This will benefit late-planted soybean production slightly, while the increase in precipitation and carbon dioxide (CO2) concentration will result in a yield increase. This was the highest for Calhoun and Williamson and ranged from 31 to 49% for the climate change projections for 2050. However, a large reduction in precipitation caused a decrease in yield for Midville, especially based on the climate scenarios of the Global Climate Models (GCMs) Commonwealth Scientific and Industrial Research Organisation's model CSIRO-Mk3.0 and Geophysical Fluid Dynamics Laboratory's model GFDL-CM2.1. Overall, Calhoun, Williamson, Plains and Tifton will probably be more suitable for rainfed soybean production over the next 40 years than Midville. Farmers might shift to a later planting date, around 5 June, for the locations that were evaluated in the present study to avoid potential heat and drought stress during the summer months. The cultivars AG6702, AGS758RR and S80-P2 could be selected for rainfed soybean production since they had the highest rainfed yields among the six cultivars. In general, the present study showed that there are crop management options for soybean production in Georgia and the south-eastern USA that are adapted for the potential projected climate change conditions.

Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important grain crop globally. It stands out for its diversity of plant types, end‐uses, and roles in cropping systems. This diversity presents opportunities but also complicates evaluation of production options, especially under climate uncertainty. Ecophysiological models can dissect interacting effects of plant genotypes, crop management, and environment. We describe the sorghum module of the Cropping System Model (CSM) as implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) to illustrate potential applications and suggest areas for model improvement. Crop growth is simulated based on radiation use efficiency. Development responds to temperature and photoperiod. Partitioning rules vary with growth stages, respecting mass balance and maintaining functional equilibrium between roots and shoots. Routines for climate, soil, crop management, and model controls are shared with other crops in CSM. Modeled responses for eight real‐world and hypothetical cases are presented. These include growth under well‐managed conditions, responses to row‐spacing, population, sowing date, irrigation, defoliation, and increased atmospheric carbon dioxide concentration ([CO2]), and a long‐term sorghum and winter wheat (Triticum aestivum L.) rotation. Among traits and experiments considered, model accuracy was high for phenology (r2 = 0.96, P < 0.01 for anthesis and r2 = 0.91, P < 0.01 for maturity), moderate for grain yields (r2 values from 0.30 to 0.52, P < 0.01), depending on the simulated experiments, and low for unit grain weight (r2 = 0.02, not significant, NS) and leaf area index for forage sorghum (r2 = 0.18, NS).

Simulation models, such as the DSSAT (Decision Support System for Agrotechnology Transfer) Crop System Models are often used to characterize, develop and assess field crop production practices. In this study, one of the DSSAT Cropping System Model, CERES-Maize, was employed to characterize maize (Zea mays) yield and nitrogen dynamics in a 50-year maize production study at Woodslee, Ontario, Canada (42°13′N, 82°44′W). The treatments selected for this study included continuous corn/maize with fertilization (CC-F) and continuous corn/maize without fertilization (CC-NF) treatments. Sequential model simulations of long-term maize yield (1959-2008), near-surface (0-30 cm) soil mineral nitrogen (N) content (2000), and soil nitrate loss (1998-2000) were compared to measured values. The model did not provide accurate predictions of annual maize yields, but the overall agreement was as good as other researchers have obtained. In the CC-F treatment, near-surface soil mineral N and cumulative soil nitrate loss were simulated by the model reasonably well, with n-RMSE = 62 and 29%, respectively. In the CC-NF treatment, however, the model consistently overestimated soil nitrate loss. These outcomes can be used to improve our understanding of the long-term effects of fertilizer management practices on maize yield and soil properties in improved and degraded soils.

Temporal irregularities of rainfall and drought have major impacts on rainfed cropping systems. The main goal of this study was to develop an approach for realizing drought occurrence based on local winter wheat yield loss and rainfall. The domain study included 11 counties in the state of Washington that actively grow rainfed winter wheat and an uncertainty rainfall evaluation model using daily rainfall values from 1985 to 2007. An application was developed that calculates a rainfall index for insurance that was then used to determine the drought intensity for each study year and for each study site. Evaluation of the drought intensity showed that both the 1999–2000 and 2000–2001 growing seasons were stressful years for most of the study locations, while the 2005–2006 and the 2006–2007 growing seasons experienced the lowest drought intensity for all locations. Our results are consistent with local extension reports of drought occurrences. Quantification of drought intensity based on this application could provide a convenient index for insurance companies for determining the effect of rainfall and drought on crop yield loss under the varying weather conditions of semi-arid regions.