Analysing the impact of calibrating a low-cost soil moisture sensor on FAO Aquacrop model performance.

Poverty reduction programs across the world have invested in the agriculture sector, specifically in agricultural technology. Irrigation remains a crucial input to agriculture, and the lack of access to supplemental irrigation aggravates the distress of farmers, particularly, smallholders. Crop simulation models use parameters like crop characteristics, environmental conditions and management practices in combination with the local input data, to compute the 'yield response of crops to water', to better inform irrigation decision-making, for saving resources and/or increasing yield. Soil moisture data can be critical to develop more representative crop models by influencing soil hydraulic parameter estimation, and consequently improving the simulation of soil water movement. The dearth of cost-effective soil moisture sensors is a limitation to their effective incorporation in crop modelling, but calibrating them against primary or secondary standards can expand their scope of application. This study applies different calibration techniques on the low-cost capacitance based soil moisture sensor, Spectrum SM100. Calibration techniques include segmented linear regression, polynomial regression, spline regression, and machine learning algorithms such as support vector regression, random forest regression, multi-layer perceptron, extreme learning machine and support vector categorization. Independent soil moisture data are taken as both continuous and categorical variables, are calibrated both in the laboratory and field, and validated using field data. Field data is obtained from an experimental field in Kanpur (India) during a wheat cropping season in 2018. The experimental site is representative of an intensively managed rural landscape in the Ganga river basin, India. The calibrated soil moisture data are subsequently used in the  crop-water productivity model FAO Aquacrop to tune its soil hydraulic properties. Various models are developed with soil hydraulic parameter sets estimated using the calibrated soil moisture data. The respective performances of these models are compared with the default model performance (with parameters derived from the literature), based on outputs of interest such as above ground biomass, crop yield and water use efficiency. A representative crop model is then used to develop scenarios of irrigation scheduling, with varying degrees of water stress. Results indicate that calibrating the soil moisture sensors in laboratory conditions alone is not sufficient to parameterize soil hydraulic properties, and adequate parameterization requires sensor calibration in field conditions. Further, a cost-benefit analysis is conducted to assess and critically discuss the tradeoffs between the cost of soil moisture monitoring and the obtained crop yield.