|
|
|
| Registros recuperados: 36 | |
|
|
| Sojka, R.E.; Lentz, R.D.; Shainberg, I.; Trout, T.J.; Ross, C.W.; Robbins, C.W.; Entry, J.A.; Aase, J.K.; Bjorneberg, D.L.; Orts, W.J.; Westermann, D.T.; Morishita, D.W.; Watwood, M.E.; Spofford, T.L.; Barvenik, F.W.. |
| Tipo: Conference or Workshop Item |
Palavras-chave: Water-soluble PAM (WSPAM); Mass Import - autoclassified (may be erroneous). |
| Ano: 2000 |
URL: http://eprints.nwisrl.ars.usda.gov/943/1/1022.pdf |
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
| |
|
|
| King, B.A.; Bjorneberg, D.L.; Trout, T.J.; Mateos, L; Araujo, D. F.; Costa, R. N.. |
| The area irrigated by furrow irrigation in the U.S. has been steadily decreasing but still represents about 20% of the total irrigated area in the U.S. Furrow irrigation sediment loss is a major water quality issue and a method for estimating sediment loss is needed to quantify the environmental impacts and estimate effectiveness and economic value of conservation practices. Artificial neural network (NN) modeling was applied to furrow irrigation to predict sediment loss as a function of hydraulic and soil conditions. A data set consisting of 1926 furrow evaluations spanning three continents and a wide range of hydraulic and soil conditions was used to train and test a multilayer perceptron feed forward NN model. The final NN model consisted of 16 inputs,... |
| Tipo: Article |
Palavras-chave: Furrow irrigation; Water quality. |
| Ano: 2015 |
URL: http://eprints.nwisrl.ars.usda.gov/1593/1/1551.pdf |
| |
|
| |
| Registros recuperados: 36 | |
|
|
|
