Identifying gaps in actual and simulated/potential yield and growing season precipitation in Morocco.

The influence of growing season rainfall on agricultural production is indisputable. In Morocco, the production of crops such as barley, maize, and wheat is impacted by growing season rainfall. Due to persistent gaps in growing season rainfall and other drivers of crop yield, crops have experienced observed yields that are often below projected or potential yields. However, there are currently no studies that have quantified these gaps in yield and growing season rainfall in Morocco. To achieve this objective, time-series crop yield for all three crops and growing season rainfall data for the period 1991-2020 were collected from FAOSTAT and the World Bank climate portal, respectively. Growing season rainfall and crop yield data for the spatial variations were culled from System National de Suivi Agrometeorologique (GCMS) and the yield gaps atlas, respectively, for the same historical period. The data were subjected to bias correction to handle uncertainty. The projected/simulated crop yields and growing season rainfall were computed by regression analysis. Crop yield and growing season rainfall gaps were determined by establishing the difference between the projected and observed crop yields and rainfall data. The results show that observed and simulated wheat have a stronger relationship when compared to the other crops. Also, most years with crop yield gaps are associated with growing season rainfall gaps. Wheat records the lowest number of years with yield gaps and the highest number of years with growing season rainfall gaps during the entire data series. Therefore, even though yield gaps are strongly tied to growing season rainfall gaps, it is not the case for wheat, and therefore other drivers might be important because wheat has the lowest number of years with crop yield gaps and the highest number of years with growing season rainfall gaps. Spatially, yield and growing season rainfall gaps decline with increased latitude. The broader perspective and policy implication here is that a better understanding of yield and growing season rainfall gaps mandates an understanding of growing season rainfall and other drivers of yield. As a way forward, potential research should focus on identifying the drivers of yield gaps, sub-national experimentation at the plot level as well as on closing yield gaps through water and nutrient management.

A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data.

Soil moisture (SM) data are required at high spatio-temporal resolution-typically the crop field scale every 3-6 days-for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66-0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

Assessing the impact of global climate changes on irrigated wheat yields and water requirements in a semi-arid environment of Morocco.

The present work aims to quantify the impact of climate change (CC) on the grain yields of irrigated cereals and their water requirements in the Tensift region of Morocco. The Med-CORDEX (MEDiterranean COordinated Regional Climate Downscaling EXperiment) ensemble runs under scenarios RCP4.5 (Representative Concentration Pathway) and RCP8.5 are first evaluated and disaggregated using the quantile-quantile approach. The impact of CC on the duration of the main wheat phenological stages based on the degree-day approach is then analyzed. The results show that the rise in air temperature causes a shortening of the development cycle of up to 50 days. The impacts of rising temperature and changes in precipitation on wheat yields are next evaluated, based on the AquaCrop model, both with and without taking into account the fertilizing effect of CO2. As expected, optimal wheat yields will decrease on the order of 7 to 30% if CO2 concentration rise is not considered. The fertilizing effect of CO2 can counterbalance yield losses, since optimal yields could increase by 7% and 13% respectively at mid-century for the RCP4.5 and RCP8.5 scenarios. Finally, water requirements are expected to decrease by 13 to 42%, mainly in response to the shortening of the cycle. This decrease is associated with a change in temporal patterns, with the requirement peak coming two months earlier than under current conditions.