Weather-based predictive modeling of Cercospora beticola infection events in sugar beet in Belgium

Cercospora leaf spot (CLS; caused by Cercospora beticola Sacc.) is the most widespread and damaging foliar disease of sugar beet. Early assessments of CLS risk are thus pivotal to the success of disease management and farm profitability. In this study, we propose a weather-based modelling approach for predicting infection by C. beticola in sugar beet fields in Belgium. Based on reported weather conditions favoring CLS epidemics and the climate patterns across Belgian sugar beet-growing regions during the critical infection period (June to August), optimum weather conditions conducive to CLS were first identified. Subsequently, 14 models differing according to the combined thresholds of air temperature (T), relative humidity (RH), and rainfall (R) being met simultaneously over uninterrupted hours were evaluated using data collected during the 2018 to 2020 cropping seasons at 13 different sites. Individual model performance was based on the probability of detection (POD), the critical success index (CSI), and the false alarm ratio (FAR). Three models (i.e., M1, M2 and M3) were outstanding in the testing phase of all models. They exhibited similar performance in predicting CLS infection events at the study sites in the independent validation phase; in most cases, the POD, CSI, and FAR values were ≥84%, ≥78%, and ≤15%, respectively. Thus, a combination of uninterrupted rainy conditions during the four hours preceding a likely start of an infection event, RH > 90% during the first four hours and RH > 60% during the following 9 h, daytime T > 16 °C and nighttime T > 10 °C, were the most conducive to CLS development. Integrating such weather-based models within a decision support tool determining fungicide spray application can be a sound basis to protect sugar beet plants against C. beticola, while ensuring fungicides are applied only when needed throughout the season

AquaCrop and the Belgian collaborative agriculture monitoring (BELCAM) platform

peer reviewe

Crop Phenology Modelling Using Proximal and Satellite Sensor Data

peer reviewedUnderstanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling

Maize (Zea mays) and Sorghum (Sorghum bicolor) yield estimation at field and region scale by assimilation remote sensing data into AquaCrop model

In a context of climate change that directly affects agricultural production, yield estimation is of great importance regarding economic, geopolitical and food security issues. Traditionally carried out through agricultural costly and time-consuming surveys, yield estimation can be improved with crop growth models combined with Earth observation data. This research focuses on the implementation of an operational system to assimilate biophysical variables obtained from high spatial and temporal resolution satellite data into the AquaCrop model to estimate total biomass for maize and sorghum. First, the biophysical variable of interest, canopy cover, derived from satellite data was validated at the field scale with measured data obtained from digital hemispherical photography. The canopy cover parameters derived from the satellite time series – maximum canopy cover and emergence date – were assimilated into the AquaCrop model for better calibration and validation toward a limited number of maize fields in Belgium. The performance of the method allowed, in a second step, to estimate the total biomass of all the maize fields in the country using a crop mask and plot-specific data. To make the results available at the scale of the agricultural region, data aggregation was performed. This approach resulted in a satisfactory estimate of yields at the scale of the agricultural region. The approach was also tested in a semi-arid context and under weedy crop management in Niger for sorghum. It also provided interesting results both with reference data (measured) and with open access data. This research provides a framework for operationalizing yield estimation at the agricultural region scale using plot-specific information and the AquaCrop model.BELgian Collaborative Agriculture Monitoring at parcel level for sustainable cropping system

Tips for CC determination: field level and satellite derived

The Building Forward Better Initiativ

Performance of similarity analysis in the estimation of forage yields in the Sahelian zone of Niger

The study aims to test the performance of similarity analysis in herbaceous fodder biomass estimate in the Nigerian pastoral zone, in a context of insecurity and precipitation spatiotemporal variability. It is carried out on the time series of NDVI decadal images of SPOT VEGETATION for the period from 2001 to 2012 and on fodder biomasses measured in situ during the same period. Similarity analysis compares NDVI seasonal patterns to detect similar years using three criteria: the RMSE (Root Mean squared error), the MAD (Mean absolute Deviation), and R². Exploratory statistical analyzes with bootstrap are carried out to better characterize the observations resulting from the simulation. Moreover, the analysis of the parametric and non-parametric correlations is carried out to evaluate the level of link between the simulated data and the real data. The t test and the Wilcoxon test are then carried out in order to compare the means of the actual biomasses with those obtained by the similarity analysis. At the local level, the results indicate that the R² is more efficient than the RMSE and the MAD which have almost the same performances. The results of the similarity calculated with R² can be used as a proxy to the herbaceous phytomass measured in situ, as there is no significant difference between the simulated mean and the mean measured at the 1% threshold. On the other hand, the results of the similarity calculated with the RMSE and the MAD are not exploitable. Parametric and nonparametric correlations are all significant at the 1% threshold. However, the R² are low and vary between 0.32 and 0.45. It therefore seems necessary to continue the research, as numerous studies have revealed very good links between certain indices like the FAPAR, the EVI and the LAI and the aerial phytomasse

Testing an open app-based water management information sysem and its uptake among stakeholders in the Upper-Comoé river basin (Burkina Faso)

peer reviewe

Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived Fractional Vegetation Cover data

status: publishe