Karim C. Abbaspour

TL;DR: The results reveal that developed SD model performance well in reflecting the dynamic behavior of the system in the current study area and an optimal growth pattern considering both socio-economic growth and EF requirements are found by comparing the different scenario simulation results.

TL;DR: In this article, the authors analyzed the factors influencing the farmers' decisions on fertilizer use and the implications for water quality and found that higher education level significantly reduces the probability of over-fertilization.

TL;DR: In this article, a large-scale crop model for simulating maize cultivation in sub-Saharan Africa (SSA) is presented, where planting dates are estimated using reported planting seasons, plant growth parameters were adopted from literature to reflect a low-yielding cultivar, and agricultural practice was mimicked by simulating continuous cultivation of maize with removal of plant residues.

TL;DR: The fact that each combination of optimization algorithm-objective functions may lead to a different set of optimum parameters, while having the same performance, makes the interpretation of dominant hydrological processes in a watershed highly uncertain.

TL;DR: This study evaluated existing regression‐based PTFs and developed two neural network algorithms using multilayer perceptron and general regression neural networks based on a set of 170 soil samples for predicting CEC in Aridisols of Isfahan in central Iran and found the neural network‐based models provided more reliable predictions.