Studies evaluating the complexity of models, which are suitable to simulate grass growth at regional scales in intensive grassland production systems scarce. Therefore, two different models (GrasProg1.0 and APSIM) with input requirements were compared against long-term observations from variety trials perennial ryegrass (Lolium perenne) Germany Denmark. The trial sites covered a large range env...

This paper analyzes the manipulability of the Generalized Money Rawlsian Fair (GMRF) solutions in the problem of fairly allocating objects when monetary compensations are possible [Alkan A., Demange G., Gale D., Fair allocation of indivisible goods and criteria of justice. Econometrica 59, 1023-1039]. These solutions are envy-free, efficient, and satisfy a strong form of solidarity under budget...

Soil erosion has been recognized as one of the major threats to our environment and water quality worldwide, especially in China. To mitigate nonpoint source water quality problems caused by soil erosion, best management practices (BMPs) and/or conservation programs have been adopted. Watershed models, such as the Annualized Agricultural Non-Point Source Pollutant Loading model (AnnAGNPS), have...

This paper presents a modeling approach based on Artificial Neural Networks (ANNs) in the Ouémé river catchment at Savè. To do this, we used precipitation data as input over period 1965 -2010 to simulate discharge study area by using two ANNs models such Long Short Term Memory (LSTM) and Recurrent Gate (GRU) models. Indeed, description of stochastic nature is better presented today than statist...

In this work, we have proposed a novel methodology for greenhouse tomato yield prediction, which is based on hybrid of an explanatory biophysical model—the Tomgro model, and machine learning model called CNN-RNN. The CNN-RNN models are calibrated/trained predicting yields while different fusion approaches (linear, Bayesian, neural network, random forest gradient boosting) exploited fusing the p...