Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management.

The availability of accurate reference evapotranspiration (ETo) data is crucial for developing decision support systems for optimal water resource management. This study aimed to evaluate the accuracy of three empirical models (Hargreaves-Samani (HS), Priestly-Taylor (PT), and Turc (TU)) and three machine learning models (Multiple linear regression (LR), Random Forest (RF), and Artificial Neural Network (NN)) in estimating daily ETo compared to the Penman-Monteith FAO-56 (PM) model. Long-term data from 42 weather stations in Florida were used.

Towards sustainable watershed-based landscape restoration in degraded drylands: Perceived benefits and innovative pathways learnt from project-based interventions in Ethiopia.

Land degradation is one of the contemporary environmental challenges affecting regions inhabited by over one-third of the global population. In response to land degradation, restoration of degraded landscapes through area closure has been implemented through government and bilateral organizations for the last three decades in Ethiopia. Objectives of this study were to: i) explore the effects of landscape restoration on vegetation cover; ii) identify the perceived benefits to local communities; and 3) synthesize the lessons learnt on communities' willingness to sustain the restored landscapes.

Modeling adaptation of sorghum in Ethiopia with APSIM-opportunities with G×E×M.

Sorghum is an important food and feed crop in the dry lowland areas of Ethiopia. Farmers grow both early-sown long-duration landraces and late-sown short-duration improved varieties. Because timing and intensity of drought stress can vary in space and time, an understanding of major traits (G), environments (E), management (M), and their interactions (G×E×M) is needed to optimize grain and forage yield given the limited available resources.