Rice blast forecasting models and their practical value: a review

Rice, after wheat, is the second largest cereal crop, and is the most consumed major staple food for more people than any other crop. Rice blast (caused by Pyricularia oryzae, teleomorph Magnaporthe grisea) is the most destructive of all rice diseases, causing multi-million dollar losses every year. Chemical control of this disease remains the most effective rice blast management method. Many attempts have been made to develop models to forecast rice blast. A review of literature of the rice blast forecasting models revealed that 52 studies have been published, with the majority capable of predicting only leaf blast. The most frequent input variable has been air temperature, followed by relative humidity and rainfall. Critical factors for the pathogenesis, such as leaf wetness, nitrogen fertilization and variety resistance have had limited integration in the development of these models. This review reveals low rates of model application due to inaccuracies and uncertainties in the predictions. Five models are part of current operational forecasting systems in Japan, Korea and India. Development of in-field rice-specific weather stations, along with integration of leaf wetness and end-user interactive inputs should be considered. This review will be useful for modelers, users and stakeholders, to assist model development and selection of the most suitable models for the effective rice blast forecasting

Rice blast forecasting models and their practical value: a review

Rice, after wheat, is the second largest cereal crop, and is the most consumed major staple food for more people than any other crop. Rice blast (caused by Pyricularia oryzae, teleomorph Magnaporthe grisea) is the most destructive of all rice diseases, causing multi-million dollar losses every year. Chemical control of this disease remains the most effective rice blast management method. Many attempts have been made to develop models to forecast rice blast. A review of literature of the rice blast forecasting models revealed that 52 studies have been published, with the majority capable of predicting only leaf blast. The most frequent input variable has been air temperature, followed by relative humidity and rainfall. Critical factors for the pathogenesis, such as leaf wetness, nitrogen fertilization and variety resistance have had limited integration in the development of these models. This review reveals low rates of model application due to inaccuracies and uncertainties in the predictions. Five models are part of current operational forecasting systems in Japan, Korea and India. Development of in-field rice-specific weather stations, along with integration of leaf wetness and end-user interactive inputs should be considered. This review will be useful for modelers, users and stakeholders, to assist model development and selection of the most suitable models for the effective rice blast forecasting

Exploitation of SAR and optical Sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R^2>0.93) and good accuracies (RMSE<0.83, rRMSE_m<23.6% and rRMSE_r<16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring

A high-resolution, integrated system for rice yield forecasting at district level

To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the ‘Tropical Japonica’ cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems

A Novel Compost for Rice Cultivation Developed by Rice Industrial By-Products to Serve Circular Economy

Rice is the major staple crop worldwide, whereas fertilization practices include mainly the application of synthetic fertilizers. A novel compost was developed using 74% of rice industrial by-products (rice bran and husks) and tested in rice cultivation in Greece&rsquo;s main rice producing area. Field experimentation was conducted in two consecutive growing seasons (2017 and 2018) and comprised six fertilization treatments, including four compost rates (C1: 80, C2: 160, C3: 320 kg ha&minus;1 of nitrogen all in split application, C4: 160 kg ha&minus;1 of nitrogen in single application), a conventional treatment, as well as an untreated control. A total of 21 morpho-physiological and quality traits were evaluated during the experimentation. The results indicated that rice plants in all compost treatments had greater height (8%&ndash;64%) and biomass (32%&ndash;113%) compared to the untreated control. In most cases, chlorophyll content index (CCI) and quantum yield (QY) were similar or higher in C3 compared to the conventional treatment. C2 and C3 exhibited similar or greater yields, 7.5&ndash;8.7 Mg ha&minus;1 in 2017 and 6.3&ndash;6.9 Mg ha&minus;1 in 2018, whereas the conventional treatment resulted in 7.3 Mg ha&minus;1 and 6.8 Mg ha&minus;1 in the two years, respectively. No differences were observed in most quality traits that affect the rice commodity. The current study reveals that in sustainable farming systems based on circular economy, such as organic ones, the application of the proposed compost at the rate of 6 Mg ha&minus;1 can be considered sufficient for the rice crop nutrient requirements