The drought in Serbia in the summer of 2017 heavily affected agricultural production, decreasing yields of maize, sunflower, soybean, and sugar beet. Monitoring moisture levels in crops can provide timely information about potential risk within a growing season, thus helping to create an early warning system for various stakeholders. The purpose of this study was to quantify the level of moisture stress in crops during summer and the consequences that it can have on yields. For that, maize and sunflower yield data provided by an agricultural company were used at specific parcels in the Backa region of Vojvodina province (Serbia) for 2017, 2018, 2019, and 2020. The crop moisture level was estimated at each parcel by calculating the normalized difference moisture index (NDMI) from Sentinel-2 data during the summer months (June–July–August). Based on the average NDMI value in July, the new crop moisture stress (CMS) index was introduced. The results showed that the CMS values at a specific parcel could be used for within-season estimation of maize and sunflower yield and the assessment of drought effects. The CMS index was tested for the current growing season of 2022 as an early warning system for yield reduction, demonstrating the potential to be included in a platform for digital agriculture, such as AgroSens, which is operational in Serbia.
<p>The drought in south-eastern Europe in the summer of 2017 heavily affected agricultural production, subsequently decreasing yields of maize. The European Drought Observatory provides Combined Drought Indicator for a 10-day period with coarse spatial resolution of 5 km, which is not localized on field level. It is derived from the combination of Standardized Precipitation Index (SPI), the Soil Moisture Index Anomaly (SMA), and the anomaly of the fraction of absorbed photosynthetically active radiation (FAPAR). Monitoring moisture levels in crops can provide timely information about the presence of abiotic stress in plants and improper development within a growing season. Heat stress and low levels of moisture in maize during summer can thereafter have detrimental consequences on yield. For that reason, in this study, the crop moisture level was estimated at specific parcels by calculating the normalized difference moisture index (NDMI) from Sentinel-2 multispectral imagery during summer months (June&#8211;July&#8211;August) and the time-series of NDMI were analyzed. Based on the average NDMI value in July, the crop moisture stress (CMS) index was calculated and divided into six classes. Maize yield data on parcel level were provided by an agricultural company for the period 2017 &#8211; 2021 in the Backa region of Vojvodina province, Serbia. Yield data for the period 2017-2020 were used to calculate average yield for each class of CMS, whereas yield data from 2021 were used for validation. Mean absolute error (MAE) and root-mean-square error (RMSE) were calculated and were around 1 t/ha. The results showed that the CMS values at a specific parcel could be used for within-season estimation of maize yield and the assessment of drought effects. Also, the CMS index was tested for the 2022 growing season which had drought hazard conditions in south-eastern Europe according to the European Drought Observatory. Expected maize yield reduction estimated for specific scouted fields showed substantial and below average yield values.</p>
<p>Increasing agricultural production is inevitable in the future since population growth and climate change have led to significant pressure on global food security. One of the ways is to intensify the existing cropland by multi-cropping practice, allowing multiple uses of a single field during one year.&#160; This research aims to identify and map double-cropping land using multi-temporal Sentinel 2 imagery from 2021 and advanced machine learning models. The case study focus is on Ba&#269;ka and Srem, regions located in the Autonomous Province of Vojvodina, Republic of Serbia. These regions are characterized by fertile land and widespread agriculture production. However, there is a low presence of double-cropping practice due to usually dry summers, but with a tendency to change as the number of irrigation systems increase.</p><p>Considering the small amount of double-cropping fields, there is a need for direct ground truth data collection. For that reason, the first step was to reduce the area of interest to get insight into the locations of potential double-cropping land. This result was obtained by using the threshold method based on the phenology of crops during the year. The NDVI (Normalized Difference Vegetation Index) time series was utilized to define appropriate thresholds for feature two peak values to discriminate double-cropping within each pixel. The identification of the results was used on-site for collecting ground truth data. Based on the collected data and the analyzed NDVI time series, besides double-crop, three more classes of arable land were distincted and included in the classification: single winter crops, single summer crops and clover. The collected data contained 46 parcels of double crops, 43 single winter crops, 55 single summer crops and 27 parcels of clover. We used time-series images to create a dataset for training the pixel-based Random Forest classification. The results showed a very high overall accuracy of 99% and an &#160;F-score higher than 0.9 for each of the classes.</p><p>This methodology is a suitable approach for detecting double-cropping systems, with further potential to identify exact crop types and the main practice of combining crops. The findings of this study showed that only about 2% of the study area was under this production. Except for positive economic outcomes, utilizing these systems brings significant environmental benefits and rational use of the soil without expanding physical cropland but with the same advantages. Therefore, the resulting geospatial datasets of double cropping croplands could help solve important questions relevant to food security, irrigation and climate change.</p>
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