Abstract

An initial step is made towards improving the global water application uniformity of centre pivots by adjusting the lateral speed based on diurnal changes of wind drift and evaporation losses (WDEL). A large WDEL dataset was used to construct an artificial neural network (ANN) model capable of predicting the above ground application efficiency (AGAE ≈ 1- WDEL) from input weather data. Given its reasonably good accuracy (RMSE = 1.5%), the ANN model was then applied in heuristic, simple and dynamic algorithms that used forecasted or real-time weather parameters corresponding to the hourly time intervals of the pivot's revolution time. For each timing interval, the final output of the algorithms is a coefficient ( λ ), that when multiplied by the lateral speed, can ensure a more consistent water delivery depth across the field. The proposed algorithms were theoretically tested for extremely hot, dry, humid, and windy days. These efforts indicated that under the climatic conditions of the study, λ could potentially vary between 0.92% and 1.06% if the revolution time is about 24 h. The λ coefficient typically increased from 1:00 AM to 9:00 AM, then decreased until the sunset, between 19:00 to 21:00, and then increased again until midnight. This general trend for λ could however, be disrupted by sudden changes in the wind speed. Future studies should conduct extensive field work to test the efficacy of the proposed algorithms and quantify the improvement of centre pivot global water application uniformity through the lateral speed adjustment. • Centre pivot lateral speed can be adjusted to account for a changing application efficiency. • Two theoretical algorithms were developed to improve the global water application uniformity. • The first algorithm uses forecasted weather data while the second uses real time data. • The lateral speed adjustment factor varied within ±6% during a 24-hr cycle. • Centre pivot lateral speed adjustment is easy to implement.

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