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Simulating Cotton Growth and Productivity Using AquaCrop Model under Deficit Irrigation in a Semi-Arid Climate
2022, Aziz, Marjan, Rizvi, Sultan Ahmad, Sultan, Muhammad, Bazmi, Muhammad Sultan Ali, Shamshiri, Redmond R., Ibrahim, Sobhy M., Imran, Muhammad A.
AquaCrop is a water-driven model that simulates the effect of environment and management on crop production under deficit irrigation. The model was calibrated and validated using three databases and four irrigation treatments (i.e., 100%ET, 80%ET, 70%ET, and 50%ET). Model performance was evaluated by simulating canopy cover (CC), biomass accumulation, and water productivity (WP). Statistics of root mean square error (RMSE) and Willmott’s index of agreement (d) showed that model predictions are suitable for non-stressed and moderate stressed conditions. The results showed that the simulated biomass and yield were consistent with the measured values with a coefficient of determination (R2) of 0.976 and 0.950, respectively. RMSE and d-index values for canopy cover (CC) were 2.67% to 4.47% and 0.991% to 0.998% and for biomass were 0.088 to 0.666 ton/ha and 0.991 to 0.999 ton/ha, respectively. Prediction of simulated and measured biomass and final yield was acceptable with deviation ˂10%. The overall value of R2 for WP in terms of yield was 0.943. Treatment with 80% ET consumed 20% less water than the treatment with 100%ET and resulted in high WP in terms of yield (0.6 kg/m3) and biomass (1.74 kg/m3), respectively. The deviations were in the range of −2% to 11% in yield and −2% to 4% in biomass. It was concluded that AquaCrop is a useful tool in predicting the productivity of cotton under different irrigation scenarios.
Scientific Irrigation Scheduling for Sustainable Production in Olive Groves
2022, Aziz, Marjan, Khan, Madeeha, Anjum, Naveeda, Sultan, Muhammad, Shamshiri, Redmond R., Ibrahim, Sobhy M., Balasundram, Siva K., Aleem, Muhammad
The present study aimed at investigating scientific irrigation scheduling (SIS) for the sustainable production of olive groves. The SIS allows farmers to schedule water rotation in their fields to abate crop water stress and maximize yields, which could be achieved through the precise monitoring of soil moisture. For this purpose, the study used three kinds of soil moisture sensors, including tensiometer sensors, irrometer sensors, and gypsum blocks for precise measurement of the soil moisture. These soil moisture sensors were calibrated by performing experiments in the field and laboratory at Barani Agricultural Research Institute, Chakwal in 2018 and 2019. The calibration curves were obtained by performing gravimetric analysis at 0.3 and 0.6 m depths, thereby equations were developed using regression analysis. The coefficient of determination (R2 ) at 0.3 and 0.6 m depth for tensiometer, irrometer, and gypsum blocks was found to be equal to 0.98, 0.98; 0.75, 0.89; and 0.82, and 0.95, respectively. After that, a drip irrigation system was installed with the calibrated soil moisture sensors at 0.3 and 0.6 m depth to schedule irrigation for production of olive groves as compared to conventional farmer practice, thereby soil moisture profiles of these sensors were obtained to investigate the SIS. The results showed that the irrometer sensor performed as expected and contributed to the irrigation water savings between 17% and 25% in 2018 and 2019, respectively, by reducing the number of irrigations as compared toother soil moisture sensors and farmer practices. Additionally, olive yield efficiencies of 8% and 9%were observed by the tensiometer in 2018 and 2019, respectively. The outcome of the study suggests that an effective method in providing sustainable production of olive groves and enhancing yield efficiency.
Greenhouse Gas Mitigation Costs of Reduced Nitrogen Fertilizer
2022, Meyer-Aurich, Andreas, Karatay, Yusuf Nadi
The reduction of nitrogen (N) fertilizer use is a possible greenhouse gas (GHG) mitigation option, whereas cost estimation highly depends on assumptions of the yield response function. This paper analyzes the potential and range of GHG mitigation costs with reduced N fertilizer application based on empirical yield response data for winter rye (Secale cereale L.) and rapeseed (Brassica napus L.) from field experiments from 2013 to 2020 in Brandenburg, Germany. The field experiments included four to five N rates as mineral fertilizer treatments. Three different functional forms (linear-plateau, quadratic, and quadratic-plateau) were estimated to model yield response as a function of N supply. Economic calculations were based on relevant price–cost ratios. The results indicate that the opportunity costs of applying less fertilizer and the resulting GHG mitigation thereof vary in a great range across the years and crops estimated by different yield response functions. The linear-plateau function predominantly results in lower GHG mitigation costs than the quadratic and the quadratic-plateau function. On average, over eight years, a moderate reduction of N fertilizer (up to 20 kg/ha) offers a cost-efficient option for mitigating GHG emissions below EUR 50 per ton of CO2eq, even resulting in net profit gain in some cases.