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The aim of the study is to propose new modeling approaches for daily estimations of crop coefficient Kc for flooded rice (Oryza sativa L., ssp. indica) under various plant densities. Non-linear regression (NLR) and artificial neural networks (ANN) were used to predict Kc based on leaf area index LAI, crop height, wind speed, water albedo, and ponding water depth. Two years of evapotranspiration ETc measurements from lysimeters located in a Mediterranean environment were used in this study. The NLR approach combines bootstrapping and Bayesian sensitivity analysis based on a semi-empirical formula. This approach provided significant information about the hidden role of the same predictor variables in the Levenberg-Marquardt ANN approach, which improved Kc predictions. Relationships of production versus ETc were also built and verified by data obtained from Australia. The results of the study showed that the daily Kc values, under extremely high plant densities (e.g., for LAImax > 10), can reach extremely high values (Kc > 3) during the reproductive stage. Justifications given in the discussion question both the Kc values given by FAO and the energy budget approaches, which assume that ETc cannot exceed a specific threshold defined by the net radiation. These approaches can no longer explain the continuous increase of global rice yields (currently are more than double in comparison to the 1960s) due to the improvement of cultivars and agriculture intensification. The study suggests that the safest method to verify predefined or modeled Kc values is through preconstructed relationships of production versus ETc using field measurements. [ABSTRACT FROM AUTHOR]

In this work we evaluated the effects of a) rice cultivation under flooding conditions and b) seasonal flooding (May-October) without cultivation, on the pore size distribution of a SiL soil. Soil cores were collected from two different depth intervals (0-15 cm and 15-30 cm). After two years under flooding conditions, textural porosity increased at the expense of structural porosity. A percentage of drainage pores collapsed to storage pores; however, this time period was inadequate to bring about changes in the microporosity of the SiL soil. In the depth interval 15-30 cm, the rice root development reduced the effects of flooding on soil porosity. [ABSTRACT FROM AUTHOR]

This research is an outcome of the R&D activities of Ecodevelopment S.A. (steadily supported by the Hellenic Agricultural Organization—Demeter) towards offering precision farming services to rice growers. Within this framework, a new methodology for topdressing nitrogen prediction was developed based on machine learning. Nitrogen is a key element in rice culture and its rational management can increase productivity, reduce costs, and prevent environmental impacts. A multi-source, multi-temporal, and multi-scale dataset was collected, including optical and radar imagery, soil data, and yield maps by monitoring a 110 ha pilot rice farm in Thessaloniki Plain, Greece, for four consecutive years. RapidEye imagery underwent image segmentation to delineate management zones (ancillary, visual interpretation of unmanned aerial system scenes was employed, too); Sentinel-1 (SAR) imagery was modelled with Computer Vision to detect inundated fields and (through this) indicate the exact growth stage of the crop; and Sentinel-2 image data were used to map leaf nitrogen concentration (LNC) exactly before topdressing applications. Several machine learning algorithms were configured to predict yield for various nitrogen levels, with the XGBoost model resulting in the highest accuracy. Finally, yield curves were used to select the nitrogen dose maximizing yield, which was thus recommended to the grower. Inundation mapping proved to be critical in the prediction process. Currently, Ecodevelopment S.A. is expanding the application of the new method in different study areas, with a view to further empower its generality and operationality. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop an integrated approach to soil quality and fertility assessment in high-yielding rice agro-ecosystems threatened due to overexploitation of soil resources by intensive agriculture. The proposed approach is implemented considering representative pilot fields allocated throughout a study area based on the assumption that soils of similar general properties present a similar nutritional status due to common long-term management practices. The analysis includes (a) object-based image analysis for land zonation, (b) hot-spot analysis for sampling scheme evaluation, (c) setting of critical thresholds in soil parameters for detecting nutrient deficiencies and soil quality problems, and (d) Redundancy Analysis, TITAN analysis, and multiple regression for identifying individual or combined effects of general soil properties (e.g., organic matter, soil texture, pH, salinity) or non-soil parameters (e.g., topographic parameters) on soil nutrients. The approach was applied using as a case study the large rice agro-ecosystem of Thessaloniki plain in Greece considering some site specificities (e.g., high rice yields, calcareous soils) when setting the critical thresholds in soil parameters. The results showed that (a) 62.5% of the pilot fields' coverage has a simultaneous deficiency in Zn, Mn, and B, (b) organic matter (OM) was the most significant descriptor of nutrients' variance, and its cold spots (clustered regions of low OM values) showed important overlapping with the cold spots of K, Mg, Zn, Mn, Cu, and B, (c) a higher rate of availability increase in P, K, Mg, Mn, Zn, Fe, Cu, and B was observed when the OM ranged between 2 and 3%, and (d) the multiple regression models that assess K and P concentrations based on general soil properties showed an adequate performance, allowing their use for general assessment of their soil concentrations in the fields of the whole agro-ecosystem. [ABSTRACT FROM AUTHOR]