Field-scale assessment of crop yield, crop water use efficiency, and water balances using different techniques to devise an ICT-based decision support solution for sub-surface drainage system with controlled drainage approach in Nordic agriculture

This study presents assessment of different types of water balances that exist within and in the surrounding of the crop using different methods like, 1) frequency analysis of crop specific growth threshold-based water balance; 2) field-scale vadose zone-based water balance. Moreover, a process-based crop water productivity modelling tool was also implemented with two distinct scenarios to simulate the working principle of Subsurface drainage system (SSDS) with CD approach along with adequate assumptions and knowledge of its limitations to simulate SSDS with CD approach for computation of climate and unsaturated soil zone-based fluxes of water balances. The process-based modelling tool also presents the quantification of the benefits to implement SSDS with Controlled Drainage (CD) approach in an agricultural case study field in the Northern Finland in terms of overall crop yield and the Crop Water Use Efficiency (CWUE). The modelling efforts with necessary calibration showed improved overall model performance to predict the crop yield that was measured using R² and RMSE. The result of crop yield and CWUE was observed as improved on an average from 0.38 & 1.04 tons/ha to 0.92 & 0.40 tons/ha respectively for Scenario1 and from 0.38 & 1.04 tons/ha to 0.92 & 0.40 tons/ha respectively for Scenario2. 22 years average Crop water use efficiency (CWUE) for Scenario1 was observed on an overage of 2.25 kg/m3 and for Scenario2 was on an overage of 2.28 kg/m3. The field-scale vadose zone-based soil water balances were computed through the implementation of the finite difference techniques to govern the soil water fluxes and the equations governing the steady-state groundwater table management. Comprehensive in-situ data collected during 2021 -2022 cropping season and processed using machine learning techniques like multi-linear regression to predict the missing datasets demonstrated the application of hybrid modelling techniques in which process-based modelling blended with machine learning techniques for agricultural water resources management. The volumetric water content (m3.m-3) simulated through combined model approach showed satisfactory results when compared with in-situ datasets with an accuracy of (RMSE) 0.038 and 0.023. This approach also simulated water depth inside agricultural drainage control structure (ADCS) of SSDS with CD approach, and estimation about the total daily controlled discharge from ADCS. The study finally discussed a tri-modular smart, and pro-active decision support system (DSS) that integrates a comprehensive database module required to assess current and future condition of weather, field, and crop development; a data integration and analysis module to collect different datasets, analyse collected dataset using machine learning techniques and process-based numerical techniques; a decision support module to communicate with the user about different operations related to SSDS with CD approach. A DSS which aims to deliver sustainable development goals (SDGs) and relevant initiatives for Nordic agriculture associated with the state of water, agriculture and the environment in multiple ways.