Bread wheat and durum wheat genotypes were grown in field experiments at two locations in New South Wales, Australia across several years and using two sowing times (‘early’ v. ‘late’). Genotypes were grouped based on genetic similarity. Grain yield, grain size, soil characteristics and daily weather data were collected. The weather data were used to calculate water and heat stress indices for four key growth periods around flowering. Least absolute shrinkage and selection operator (LASSO) was used to predict grain yield and to identify the most influential features (a combination of index and growth period). A novel approach involving the crop water supply–demand ratio effectively summarized water relations during growth. LASSO predicted grain yield quite well (adjusted R2 from 0.57 to 0.98), especially in a set of durum genotypes. However, the addition of other important variables such as lodging score, disease incidence, weed incidence and insect damage could have improved modelling results. Growth period 2 (30 days pre-flowering up to flowering) was the most sensitive for yield loss from heat stress and water stress for most features. Although one group of bread wheat genotypes was more sensitive to water stress (drought) in period 3 (20 days pre-flowering to 10 days post-flowering). Evapotranspiration was a significant positive feature but only in the vegetative phase (pre-flowering, period 1). This study confirms the usefulness of LASSO modelling as a technique to make predictions that could be used to identify genotypes that are suitable candidates for further investigation by breeders for their stress-tolerance ability.

Process-based crop models (PBCM) for sugarcane present many genotype parameters compared to other crops, making it harder to calibrate. Global sensitivity analysis (GSA) has thus become an important tool for understanding, calibrating and further developing PBCMs. This work used a recently updated Agronomic Modular Simulator for Sugarcane (SAMUCA) to simulate crop growth and development, conducted with two treatments: with green cane trash blanket (GCTB) and under bare soil (BARE). Using the extended Fourier Amplitude Sensitivity (eFAST) algorithm, GSA was performed on the 24 genotype parameters of SAMUCA. The objective was to determine the sample size (SZ): how many samplings are necessary to quantify the sensitivity indices. Additionally, we aimed to assess the influence of parameter range variation and identify which genotype parameters explain the highest variance in simulations of the SAMUCA model under BARE and GCTB conditions. The results showed that SZ greatly affected the convergence and sensitivity indices, and the SZ required here needed to be >2049 for the analysis to cover all variables. Two sets of parameter ranges were used for analysis (the first used maximum and integer values of each parameter reported in the literature; the second applied 25% perturbation to the previously calibrated values). The results indicated that the parameter range affected the parameters' order of importance. Furthermore, we identified that at different phenological stages of sugarcane development, distinct parameters were responsible for explaining the most variance of the output. However, there was no difference among ratoons or interference in the results of BARE or GCTB.

Water consumption in agriculture is impossible without considering relations between water, soil and plant. In this regard, there are various models and developed software in order to evaluate relation between soil, water and crop growth stages. These models can be used for irrigation planning if properly optimized and applied. AquaCrop is one of the known crop models, which was developed by the Food and Agriculture Organization of the United Nations. In order to optimize this model for crop production and irrigation management, an experiment was developed in a hydroponic cucumber greenhouse. Various parameters including water consumption volume, crop yield and leaf area index were measured during a season. A fuzzy control system was utilized for controlling temperature, relative humidity, planting bed moisture, light intensity and carbon dioxide values. The main purpose of designing a control system in the greenhouse is to achieve the desired values of temperature and relative humidity. In this model, evapotranspiration, irrigation requirements and crop yield were simulated. The results show that the AquaCrop model can estimate evapotranspiration with the least error in the greenhouse environment, which is controlled by a fuzzy controller. Also the system has estimated the crop yield and biomass of the product with a good degree of precision and it may support crop production in a greenhouse, including crop management and environmental control.

Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

Crop models can be used to explain yield variations associated with management practices, environment and genotype. This study aimed to assess the effect of plant densities using CSM-CROPGRO-Soybean for low latitudes. The crop model was calibrated and evaluated using data from field experiments, including plant densities (10, 20, 30 and 40 plants per m2), maturity groups (MG 7.7 and 8.8) and sowing dates (calibration: 06 Jan., 19 Jan., 16 Feb. 2018; and evaluation: 19 Jan. 2019). The model simulated phenology with a bias lower than 2 days for calibration and 7 days for evaluation. Relative root mean square error for the maximum leaf area index varied from 12.2 to 31.3%; while that for grain yield varied between 3 and 32%. The calibrated model was used to simulate different management scenarios across six sites located in the low latitude, considering 33 growing seasons. Simulations showed a higher yield for 40 pl per m2, as expected, but with greater yield gain increments occurring at low plant density going from 10 to 20 pl per m2. In Santarém, Brazil, MG 8.8 sown on 21 Feb. had a median yield of 2658, 3197, 3442 and 3583 kg/ha, respectively, for 10, 20, 30 and 40 pl per m2, resulting in a relative increase of 20, 8 and 4% for each additional 10 pl per m2. Overall, the crop model had adequate performance, indicating a minimum recommended plant density of 20 pl per m2, while sowing dates and maturity groups showed different yield level and pattern across sites in function of the local climate.

New farming systems and management options are needed in South Asia as the intensive rice–wheat production system is set to become increasingly unsustainable under climate change. In the current study, six cropping systems options/treatments varying in tillage, crop establishment method, residue management, crop sequence and fertilizer and water management were evaluated using a cropping systems model under current (1980–2009) and future (2030 and 2050) climate scenarios in the state of Bihar, India. The treatments were current farmers' practice (CP), best fertilizer and water management practices, zero tillage (ZT) with no crop residue retention, ZT with partial crop residue retention (ZTPR), future conservation agriculture-based rice–wheat intensive cropping system (FCS-1) and future conservation agriculture-based maize–wheat intensive cropping system (FCS-2). The results indicate that climate change is likely to reduce rice–wheat system productivity under CP by 4% across Bihar. All the crop management options studied increased yield, water productivity and net returns over that of the CP under the current and future climate scenarios. However, the ZTPR treatment gave significantly higher relative yield, lower annual yield variability and a higher benefit-cost-ratio than the other treatments across cropping system components and climate periods. Although all the new cropping system treatments had a positive yield implication under the current climate (compared to CP), they did not contribute to adaptation under the future climate except FCS-2 in wheat. It is concluded that adaptation to future climate must integrate both cropping system innovations, and genetic improvements in stress tolerance.

Weather and climate have a fundamental impact on plant development. Monitoring key observables, e.g. temperature and precipitation, is paramount for the interpretation of agricultural experiments and simulation of plant development. Whereas the presence of appropriate sensors in a research environment can be expected, the situation can be different in commercial agricultural settings. Local air temperature from online weather forecasts is investigated as a substitute for local weather station data. Hourly air temperature forecast and station data for several locations in Scotland and North East England are aggregated into daily air temperature values spanning a period of several months. Dates for key growth stages using temperatures from weather stations and weather forecast data are compared. For the examples discussed here the date differences in modelled key growth stages did not exceed 3 days indicating that temperature forecast data is suitable for farm-specific applications.