Population Trend, Crop Loss Assessment & Acaricidal Management of Polyphagotarsonemus latus (Banks) (Acari: Tarsonemidae) Infesting Mungbean Under Gangetic Basin Of West Bengal, India

Mungbean (Vigna radiata (L.) Wilczek), also known as green gram, is one of the important pulse crops grown in West Bengal, India. Mungbean is affected by various insect and non-insect pests, among which the yellow mite, Polyphagotarsonemus latus (Banks) plays an important role amounting huge crop loss. The peak population of the mite (23.2 mites/sq.cm leaf area) encountered during the initial budding stage of the crop and declined thereafter at the end of flowering stage. A tune of 50–80 % crop loss was estimated due to its infestation. Among seven acaricides tested, Dicofol, Diafenthiouron and Spiromesifen showed higher efficacy against the mite than the other acaricides used. The yellow mite is a regular and major pest of mungbean which appeared in a severe form in the farmer field especially during pre-kharif season which can be manage successfully by application of two successive round of acaricides like diafenthiouron or dicofol at 10 days interval during early reproductive stage of the crop that ensure the flowering and fruit setting of the crop giving satisfactory yield. The present study provides an important finding regarding the peak season of infestation by yellow mite and the effective measures to be taken against it which will help the mung bean growers to combat the loss from this havoc.

Evaluation of Earth Observation Products and Their Potential for Crop Damage and Crop Loss Assessment. The Case of Beacon Project.

<p>Agricultural Insurance (AgI) sector is expanding on a global scale and is projected to grow by €50 B, by 2020. This rapid growth is driven by a set of fundamental structural changes directly affecting the agricultural sector like more frequent and severe extreme weather events, growing global population and intensification of production systems. Insurance solutions are set to grow in importance for agricultural management, given that agriculture will continue to be increasingly dependent on risk financing support. However, the development and provision of insurance services/products in the agricultural sector is generally low as compared to other sectors of the economy, and in their majority, suffer from low market penetration.</p><p>In that frame, the BEACON toolbox was born, that aims to provide insurance companies with a robust and cost-efficient set of services that will allow them i) to alleviate the effect of weather uncertainty when estimating risk of AgI products; ii) to reduce the number of on-site visits for claim verification; iii) to reduce operational and administrative costs for monitoring of insured indices and contract handling; and iv) to design more accurate and personalized contracts. Specifically, BEACON scales-up on EO data and Weather Intelligence components, couples them with blockchain, to deliver the required functions for Weather Prediction and Assessment and Smart Contracts and offer the required services:</p><ul><li>Crop Monitoring, which provides contract profiling and crop monitoring data together with yield estimations.</li> <li>Damage Assessment Calculator, which supports AgI companies in better assess and calculate damage to proceed with indemnity pay-outs of claims.</li> <li>Anti-fraud Inspector, which allows AgI to automatically check the legitimacy of a claim submitted.</li> <li>Weather Risk Probability, which provides probabilities maps of extreme weather events that may occur in the upcoming season.</li> <li>Damage Prevention/ Prognosis – Early Warning System, which provides extreme weather alerts to AgI providers and their customers.</li> </ul><p>This work focuses on the Damage Assessment Calculator component. It provides an approach using different types of EO data, implemented in the operational workflow of BEACON that can be used by AgI companies to improve the prediction and crop loss assessment due to drought and hailstorms.</p><p> </p><p>Acknowledgements</p><p>This  project  has  received  funding  from  the  European  Union's Horizon 2020 Research and Innovation programme under grant agreement No 821964 (BEACON).</p>

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

This article reviews case studies which have used remote sensing data for different aspects of flood crop loss assessment. The review systematically finds a total of 62 empirical case studies from the past three decades. The number of case studies has recently been increased because of increased availability of remote sensing data. In the past, flood crop loss assessment was very generalized and time-intensive because of the dependency on the survey-based data collection. Remote sensing data availability makes rapid flood loss assessment possible. This study groups flood crop loss assessment approaches into three broad categories: flood-intensity-based approach, crop-condition-based approach, and a hybrid approach of the two. Flood crop damage assessment is more precise when both flood information and crop condition are incorporated in damage assessment models. This review discusses the strengths and weaknesses of different loss assessment approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat are the dominant sources of optical remote sensing data for flood crop loss assessment. Remote-sensing-based vegetation indices (VIs) have significantly been utilized for crop damage assessments in recent years. Many case studies also relied on microwave remote sensing data, because of the inability of optical remote sensing to see through clouds. Recent free-of-charge availability of synthetic-aperture radar (SAR) data from Sentinel-1 will advance flood crop damage assessment. Data for the validation of loss assessment models are scarce. Recent advancements of data archiving and distribution through web technologies will be helpful for loss assessment and validation.

Improvement and Validation of NASA/MODIS NRT Global Flood Mapping

The remote-sensing based Flood Crop Loss Assessment Service System (RF-CLASS) is a web service based system developed and managed by the Center for Spatial Information Science and Systems (CSISS). The system uses Moderate Resolution Imaging Spectroradiometer (MODIS)-based flood data, which was implemented by the Dartmouth Flood Observatory (DFO), to provide an estimation of crop loss from floods. However, due to the spectral similarity between water and shadow, a noticeable amount of false classification of shadow can be found in the DFO flood products. Traditional methods can be utilized to remove cloud shadow and part of mountain shadow. This paper aims to develop an algorithm to filter out noise from permanent mountain shadow in the flood layer. The result indicates that mountain shadow was significantly removed by using the proposed approach. In addition, the gold standard test indicated a small number of actual water surfaces were misidentified by the proposed algorithm. Furthermore, experiments also suggest that increasing the spatial resolution of the slope helped reduce more noise in mountains. The proposed algorithm achieved acceptable overall accuracy (>80%) in all different filters and higher overall accuracies were observed when using lower slope filters. This research is one of the very first discussions on identifying false flood classification from terrain shadow by using the highly efficient method.

Multiple-Disease System in Coffee: From Crop Loss Assessment to Sustainable Management

Assessment of crop loss due to multiple diseases and pests (D&P) is a necessary step in designing sustainable crop management systems. Understanding the drivers of D&P development and yield loss helps identify leverage points for crop health management. Crop loss assessment is also necessary for the quantification of D&P regulation service to identify promising systems where ecosystem service provision is optimized. In perennial crops, assessment of crop losses due to D&P is difficult, as injuries can affect yield over years. In coffee, one of the first perennials in which crop loss trials were implemented, crop losses concurrent with injuries were found to be approximately 50% lower than lagged losses that originated following the death of productive branches due to D&P. Crop losses can be assessed by field trials and surveys, where yield reduction factors such as the number of productive branches that have died are quantified, and by modeling, where damage mechanisms for each injury are considered over several years.