Crop simulation models have a pivotal role to play in evaluating irrigation management strategies for improving agricultural water use. The objective of this study was to test and validate the AquaCrop model for maize under deficit irrigation management. Field observations from three experiments consisting of four treatments were used to evaluate model performance in simulating canopy cover (CC), biomass (B), yield (Y), crop evapotranspiration (ET_c), and water use efficiency (WUE). Statistics for root mean square error, model efficiency (E), and index of agreement for B and CC suggest that the model prediction is good under non-stressed and moderate stress environments. Prediction of final B and Y under these conditions was acceptable, as indicated by the high coefficient of determination and deviations 11% for B and 9% for Y indicate a reduction in the model reliability. Simulated ET_c and WUE deviation from observed values were within the range of 9.5% to 22.2% and 6.0% to 32.2%, respectively, suggesting that AquaCrop prediction of these variables is fair, becoming unsatisfactory as plant water stress intensifies. AquaCrop can be reliably used for evaluating the effectiveness of proposed irrigation management strategies for maize; however, the limitations should be kept in mind when interpreting the results in severely stressed conditions.

The system of rice intensification (SRI) uses less water and enhances rice yield through synergy among several agronomic management practices. This claim was investigated to determine the effects of crop growth, yield and irrigation water use, using two thirds of the recommended SRI practices and two rice varieties, namely Tainan11 (TN11) and Tidung30 (TD30). Irrigation regimes were (a) intermittent irrigation with three-day intervals (TD30₃ and TN11₃); (b) intermittent irrigation with seven-day intervals (TD30₇ and TN11₇) and (c) continuous flooding (TD30_F and TN11_F). Results showed that intermittent irrigation of three- and seven-day intervals produced water savings of 55% and 74% compared with continuous flooding. Total water productivity was greater with intermittent irrigation at seven-day intervals producing 0.35 kg·grain/m³ (TN11₇) and 0.46 kg·grain/m³ (TD30₇). Average daily headed panicle reduced by 166% and 196% for TN11₃ and TN11₇ compared with TN11_F, with similar reduction recorded for TD30₃ (150%) and TD30₇ (156%) compared with TD30_F. Grain yield of TD30 was comparable among irrigation regimes; however, it reduced by 30.29% in TN11₇ compared to TN11_F. Plant height and leaf area were greater in plants exposed to intermittent irrigation of three-day intervals.

This study aimed to establish a machine learning (ML)-based rice blast predicting model to decrease the appreciable losses based on short-term environment data. The average, highest and lowest air temperature, average relative humidity, soil temperature and solar energy were selected for model development. The developed multilayer perceptron (MLP), support vector machine (SVM), Elman recurrent neural network (Elman RNN) and probabilistic neural network (PNN) were evaluated by F-measures. Finally, a sensitivity analysis (SA) was conducted for the factor importance assessment. The study result shows that the PNN performed best with the F-measure (β = 2) of 96.8%. The SA was conducted in the PNN model resulting in the main effect period is 10 days before the rice blast happened. The key factors found are minimum air temperature, followed by solar energy and equaled sensitivity of average relative humidity, maximum air temperature and soil temperature. The temperature phase lag in air and soil may cause a lower dew point and suitable for rice blast pathogens growth. Through this study’s results, rice blast warnings can be issued 10 days in advance, increasing the response time for farmers preparing related preventive measures, further reducing the losses caused by rice blast.

Taiwan’s average annual rainfall is high compared to other countries around the world; however, it is considered a country with great demand for water resources. Rainfall along with alternate wetting and drying irrigation is proposed to minimize water demand and maximize water productivity for lowland paddy rice cultivation in southern Taiwan. A field experiment was conducted to determine the most suitable ponded water depth for enhancing water saving in paddy rice irrigation. Different ponded water depths treatments (T_2 cm, T_3 cm, T_4 cm and T_5 cm) were applied weekly from transplanting to early heading using a complete randomized block design with four replications. The highest rainwater productivity (2.07 kg/m³) was achieved in T_5 cm and the lowest in T_2 cm (1.62 kg/m³). The highest total water productivity, (0.75 kg/m³) and irrigation water productivity (1.40 kg/m³) was achieved in T_2 cm. The total amount of water saved in T_4 cm, T_3 cm and T_2 cm was 20, 40, and 60%, respectively. Weekly application of T_4 cm ponded water depth from transplanting to heading produced the lowest yield reduction (1.57%) and grain production loss (0.06 kg) having no significant impact on yield loss compared to T_5 cm. Thus, we assert that the weekly application of T_4 cm along with rainfall produced the best results for reducing lowland paddy rice irrigation water use and matching the required crop water.