Calibration, testing and application of the AquaCrop model for bean crop under irrigation regimes.

Crop growth simulation models relate the soil-water-plant-atmosphere components to estimate the development and yield of plants in different scenarios, enabling the identification of efficient irrigation strategies. The aim of this study was to calibrate crop coefficients for a common bean cultivar (IAPAR 57) and assess the AquaCrop model's efficacy in simulating crop growth under different irrigation regimes (T0 - non-irrigated, T1-fully irrigated, and T2-deficit irrigated) and sowing dates (S1-March 21, S2-April 24, and S3-August 23). Successful calibration was achieved for crop seasons with suitable temperatures to crop growth (S1 and S3). However, during periods with suboptimal temperatures (April 24 season), coupled with reduced irrigation supply (T0 and T2), the AquaCrop model did not appropriately account for the combined effects of thermal and water stresses. Despite adjustments to stress coefficients, this led to an overestimation of crop growth and yield. In long-term simulations, the model successfully replicated the variability of crop water availability over cropping seasons, reflecting the impact of precipitation variations. It recommended irrigation strategies for the study region (irrigate at depletion of 120 and 170% of readily available water for sowing on March 21 and August 24, respectively) to achieve high crop yield (> 2,769 kg ha-1) and water productivity (1,050 to 1,445 kg m-3) with minimal application depths (< 150 mm). While acknowledging the need for improvements in thermal stress calculations, the AquaCrop model demonstrates promising utility in studies and applications where water availability significantly influences crop production.

Optimization of sowing date and irrigation levels for white oats using the CERES-Barley model.

Studies on the use of deficit irrigation and application of models for estimating agronomic performance of crops can help in more sustainable agricultural managements. The objective of this study was to evaluate the effect of irrigation levels on the agronomic performance of white oat (Avena sativa L.) and accuracy of the CERES-Barley model in simulating white oat growth and yield, as well as performing long-term simulation to identify the best sowing time for each irrigation management. The experiment consisted of five irrigation levels (11%, 31%, 60%, 87%, and 100%), being conducted in two agricultural years in southeastern Brazil. The model was calibrated with data of the treatment without water deficit (100%) of the first year and validated with the data of the other treatments in the 2 years. Long-term analyses, with a historical series of 16 years, were performed to recommend the best sowing dates for each irrigation management. Deficit irrigation linearly reduces the agronomic performance of white oat. The high accuracy of white oat yield estimation (R2 = 0.86; RMSE = 616 kg ha-1) using the CERES-Barley model allowed the long-term simulation for establishing the best sowing date for each irrigation level. For higher irrigation levels, sowing in periods with lower temperature (May and June) is more appropriate, as the 1 °C increment in the average temperature before flowering reduces crop yield by 600 kg ha-1. At irrigation levels with higher deficit, sowing in periods with higher rainfall (March and April) promotes higher crop yield.

Soybean yield in relation to distance from the Itaipu reservoir.

Crops close to small water bodies may exhibit changes in yield if the water mass causes significant changes in the microclimate of areas near the reservoir shoreline. The scientific literature describes this effect as occurring gradually, with higher intensity in the sites near the shoreline and decreasing intensity with distance from the reservoir. Experiments with two soybean cultivars were conducted during four crop seasons to evaluate soybean yield in relation to distance from the Itaipu reservoir and determine the effect of air temperature and water availability on soybean crop yield. Fifteen experimental sites were distributed in three transects perpendicular to the Itaipu reservoir, covering an area at approximately 10 km from the shoreline. The yield gradient between the site closest to the reservoir and the sites farther away in each transect did not show a consistent trend, but varied as a function of distance, crop season, and cultivar. This finding indicates that the Itaipu reservoir does not affect the yield of soybean plants grown within approximately 10 km from the shoreline. In addition, the variation in yield among the experimental sites was not attributed to thermal conditions because the temperature was similar within transects. However, the crop water availability was responsible for higher differences in yield among the neighboring experimental sites related to water stress caused by spatial variability in rainfall, especially during the soybean reproductive period in January and February.