FAO AquaCrop model for determining optimum wheat sowing date in Tarai region of Uttarakhand

The ideal sowing period is critical for maximizing the crop's yield potential under specific agroclimatic conditions (Nain, 2016; Patra et al., 2017). It influences the phenological stages of the crop's development and, as a result, the efficient conversion of biomass into economic yield. During rabi 2013-14, a field research was done at GBPUA&T's Borlaug Crop Research Centre to determine the best sowing dates for wheat crops employing Aquacrop model. Aquacrop model has been calibrated against vegetative and economic yield forthree sowing dates, viz., 3rd December, 18th December and 3rd January (Pareek et al., 2017). After calibrating the Aquacrop model, a set of conservative variables was obtained (Pareek et al., 2017). Afterward, the calibrated Aquacrop model was used to validate wheat yield and biomass for three years in a row, namely 2010-11, 2011-12 and 2012-13. The model subsequently used to simulate yield under different sowing dates. For all of the tested years, the simulation findings of the Aquacrop model reflected the observed crop yields and biomass of wheat. The model was used to simulate the optimum sowing week based on varying sowing dates and produced grain yield for a period of 10 years (Malik et al., 2013). The average and assured yield of wheat was worked out based on probability analysis (60, 75 and 90%). The optimum sowing time for Tarai region of Uttarakhand was suggested as first week of November followed by second week of November (Nain, 2016). In no case wheat should be sown during third week of November and beyond due to poor assured yield and average yield (Nain, 2016). The finding of the studies will help to increase productivity and production of wheat crop in Tarai region of Uttarakhand.

Net irrigation requirement under different climate scenarios using AquaCrop over Europe

Global soil water availability is challenged by the effects of climate change and a growing population. On average 70 % of freshwater extraction is attributed to agriculture, and the demand is increasing. In this study, the effects of climate change on the evolution of the irrigation water requirement to sustain current crop productivity are assessed by using the FAO crop growth model AquaCrop version 6.1. The model is run at 0.5° lat × 0.5° lon resolution over the European mainland, assuming a general C3-type of crop, and forced by climate input data from the Inter-Sectoral Impact Model Intercomparison Project phase three (ISIMIP3). First, the performance of AquaCrop surface soil moisture (SSM) simulations using historical meteorological input from two ISIMIP3 forcing datasets is evaluated with satellite-based SSM estimates. When driven by ISIMIP3a reanalysis meteorology for the years 2011–2016, daily simulated SSM values have an unbiased root-mean-square difference of 0.08 and 0.06 m3m−3 with SSM retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, respectively. When forced with ISIMIP3b meteorology from five Global Climate Models (GCM) for the years 2011–2020, the historical simulated SSM climatology closely agrees with the climatology of the reanalysis-driven AquaCrop SSM climatology as well as the satellite-based SSM climatologies. Second, the evaluated AquaCrop model is run to quantify the future irrigation requirement, for an ensemble of five GCMs and three different emission scenarios. The simulated net irrigation requirement (Inet) of the three summer months for a near and far future climate period (2031–2060 and 2071–2100) is compared to the baseline period of 1985–2014, to assess changes in the mean and interannual variability of the irrigation demand. Averaged over the continent and the model ensemble, the far future Inet is expected to increase by 67 mm year–1 (+30 %) under a high emission scenario Shared Socioeconomic Pathway (SSP) 3-7.0. Central and southern Europe are the most impacted with larger Inet increases. The interannual variability of Inet is likely to increase in northern and central Europe, whereas the variability is expected to decrease in southern regions. Under a high mitigation scenario (SSP1-2.6), the increase in Inet will stabilize around 40 mm year–1 towards the end of the century and interannual variability will still increase but to a smaller extent. The results emphasize a large uncertainty in the Inet projected by various GCMs.

Assessing Growth and Water Productivity for Drip-Irrigated Maize under High Plant Density in Arid to Semi-Humid Climates

Determining the water productivity of maize is of great significance for ensuring food security and coping with climate change. In 2018 and 2019, we conducted field trials in arid areas (Changji), semi-arid areas (Qitai) and semi-humid areas (Xinyuan). The hybrid XY335 was selected for the experiment, the planting density was 12.0 × 104 plants ha−1, and five irrigation amounts were set. The results showed that yield, biomass, and transpiration varied substantially and significantly between experimental sites, irrigation and years. Likewise, water use efficiency (WUE) for both biomass (WUEB) and yield (WUEY) were affected by these factors, including a significant interaction. Normalized water productivity (WP*) of maize increased significantly with an increase in irrigation. The WP* for film mulched drip irrigation maize was 37.81 g m−2 d−1; it was varied significantly between sites and irrigation or their interaction. We conclude that WP* differs from the conventional parameter for water productivity but is a useful parameter for assessing the attainable rate of film-mulched drip irrigation maize growth and yield in arid areas, semi-arid areas and semi-humid areas. The parametric AquaCrop model was not accurate in simulating soil water under film mulching. However, it was suitable for the prediction of canopy coverage (CC) for most irrigation treatments.

Modelling the impact of climate change on maize yield in Victoria Nile Sub-basin, Uganda

Agriculture is the backbone of Uganda’s economy, with about 24.9% contribution to the gross domestic product (GDP) as per the Uganda National Household Survey 2016/17. Agricultural productivity (yield per hectare) is still low due to the high dependence on rain-fed subsistence farming. Climate change is expected to further reduce the yield per hectare. Therefore, this study aims to evaluate the potential impact of climate change on maize yield in the Victoria Nile Sub-basin using the AquaCrop model. It further assesses the possible adaptation measures to climate change. The Hadley Centre Global Environmental Model version 2–Earth System (HadGEM2-ES) data downloaded from the Coordinated Regional Downscaling Experiment (CORDEX) was used to simulate maize yield in the near future (2021–2040), mid future (2041–2070) and late future (2071–2099). Results show that maize yield is likely to reduce by as high as 1–10%, 2–42% and 1–39% in the near, mid and late futures, respectively, depending on the agro-ecological zone. This decline in maize yield can have a significant impact on regional food security as well as socio-economic well-being since maize is a staple crop. The study also shows that improving soil fertility has no significant impact on maize yield under climate change. However, a combined application of supplementary irrigation and shifting the planting dates is a promising strategy to maintain food security and socio-economic development. This study presents important findings and adaptation strategies that policymakers and other stakeholders such as farmers can implement to abate the effects of climate change on crop production.

Identification of Suitable Sites for Jatropha curcas L. Bioenergy Plantation Using the AquaCrop Model

Jatropha curcas (JC), as a biofuel plant, has been reported to have various desired characteristics such as high oil content seeds (27–40%), fast-growth, easy cultivation, drought tolerance, and can be grown on marginal soil and wasteland, requiring fewer nutrients and management and does not interfere with existing food crops, insects, and pest resistance. This investigation was the first study of its type to use climatological data, blue/green water footprints, and JC seed production to identify suitable sites for JC bioenergy plantation using the AquaCrop FAO model across the Khyber Pakhtunkhwa province in northwest Pakistan. The JC seed yield (10 ton/ha) was at a maximum in the districts of Bannu, Karak, Hangu, Kurram, North Waziristan, Lakki Marwat, South Waziristan, and Dera Ismail Khan, in addition to its frontier regions, Tank, Peshawar, Mohmand, Orakzai, Khyber, Kohat, Charsadda, Mardan, Swabi, and Nowshera, respectively. Green water footprint (264 m3/ton of JC seed) and blue water footprint (825 m3/ton) was less in these areas. Furthermore, the results revealed that, depending on climatological circumstances, the southern part of the Khyber Pakhtunkhwa province is more appropriate for JC bioenergy plantation than the northern region. The districts of Bannu, Karak, Hangu, Kurram, North Waziristan, Lakki Marwat, South Waziristan, Dera Ismail Khan, and its frontier regions, Tank, Peshawar, Mohmand, Orakzai, Khyber, and Kohat, in Khyber Pakhtunkhwa province were identified to be the most ideal places for JC bioenergy plantation. As a result, under the Billion Tree Afforestation Project (BTAP) and the Green Pakistan Project, the Forest Department of Khyber Pakhtunkhwa should consider planting JC species in the province’s southern region. Furthermore, this research will provide scientific information to government and private sector officials for better management and optimum yield of the JC biofuel crop, as well as for the promotion of energy forestry in Pakistan.

Performance Evaluation of AquaCrop Model in Processing Tomato Biomass, Fruit Yield and Water Stress Indicator Modelling

A three-year long experiment was conducted on open-field tomato with different levels of water shortage stress. Three different water supply levels were set in 2017 and four levels for 2018 and 2019. Biomass and yield data were collected, along with leaf-temperature-based stress measurements on plants. These were used for calibration and validation of the AquaCrop model. The validation gave various results of biomass and yield simulation during the growing season. The largest errors in the prediction occurred in the middle of the growing seasons, but the simulation became more accurate at harvest in general. The prediction of final biomass and yields were good according to the model evaluation indicators. The relative root mean square error (nRMSE) was 12.1 and 13.6% for biomass and yield prediction, respectively. The modeling efficiency (EF) was 0.96 (biomass) and 0.99 (yield), and Willmott’s index of agreement (d) was 0.99 for both predicted parameters at harvest. The lowest nRMSE (4.17) was found in the simulation of final yields of 2018 (the calibration year). The best accuracy of the validation year was reached under mild stress treatment. No high correlation was found between the simulated and measured stress indicators. However, increasing and decreasing trends could be followed especially in the severely stressed treatments.

Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations

The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcsec (∼1 km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active–Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.