New multi-model approach gives good estimations of wheat yield under semi-arid climate in Morocco

Abstract

International audienceWheat production in Morocco is crucial for economy and food security. However, wheat production is difficult because the semi-arid climate causes very variable wheat yields. To solve this issue, we need better prediction of the impact of drought on wheat yields to adapt cropping management to the semi-arid climate. Here, we adapted the models WOFOST and CropSyst to agro-climatic conditions in Morocco. Six soft and durum wheat varieties were grown during the 2011–2012 and 2012–2013 growing seasons in the experimental sites of Sidi El Aydi, Khemis Zemamra and Marchouch. Drip irrigation and rainfed treatments were arranged in a randomised-block design with three replicates. We determined the phenological stages of emergence, tillering, stem elongation, flowering and maturity. We measured aboveground biomass six times along the season. These data were used to adapt WOFOST and CropSyst to local conditions. Our results show that both models achieved good estimations, with R2 always higher than 0.91, and positive values for Nash and Sutcliffe modelling efficiencies. Results of spatially distributed simulations were then analysed for the whole country in terms of different response to drought

Grain Yield Observations Constrain Cropland CO2 Fluxes Over Europe

“…This "wet soil" assumption is unrealistic for the dry regions of Europe such as the Mediterranean, especially for the case of spring crops that are sown later in the year, as we could be neglecting important soil moisture depletion happening before the start of the growing season. We can solve this issue by using soil moisture data-for instance, the ERA-Interim reanalysis product (Dee et al, 2011) The WOFOST model has been extensively used for research on crop yield and yield gap all over the world, notably in Europe (Bussay et al, 2015;Eitzinger et al, 2013;Foltescu, 2000;Kogan et al, 2013), Africa (Bregaglio et al, 2014;Kassie et al, 2014;Wolf et al, 2015), Middle East (Sargordi et al, 2013), India (Dua et al, 2013), and China (Wang et al, 2011). However, to our knowledge we are the first to apply WOFOST to estimate surface carbon exchange fluxes and to verify these against observations of GPP, TER, and NEE.…”

Contribution of Remote Sensing on Crop Models: A Review

“…Di Paola, et al [15] also, argue that misleading model validation may be caused by comparing model results with the outputs of other more general models. As uncertainties in model results related to model calibration and inputs may be high, users of these models should always refer to the conditions in which said models were developed and tested [52].…”

“…Sensors can acquire data remotely while being on board different platforms, such as satellites, aeroplanes, UAVs or handheld devices [52].…”

Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning

“…Conversely, estimation of LWD is based on its relationships with meteorological variables available in standard agro-meteorological stations. Some examples of empirical LWD models are the simple relative humidity threshold model (RHM), which simulates the leaf wetness occurrence when humidity is above a threshold [10,11]; the dew point depression method (DPM), based on the principle of dew formation [12,13]; and the classification and regression tree (CART) model, which considers the non-linear relationship between leaf wetness, wind speed, rainfall, dew temperature and relative humidity in decision nodes to determine leaf wetness [14]. Kim et al demonstrated the spatial portability of the CART model through its application in different environments [15].…”