The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Serge Savary - One of the best experts on this subject based on the ideXlab platform.

  • Concepts, approaches, and avenues for modelling Crop health and Crop Losses
    European Journal of Agronomy, 2018
    Co-Authors: Serge Savary, Andrew Nelson, Annika Djurle, Paul D. Esker, Adam Sparks, Lilian Amorim, Armando Bergamin Filho, Tito Caffi, Nancy Castilla, Karen Garrett
    Abstract:

    This article addresses the modelling of Crop health and its impact on Crop Losses, with a special emphasis on plant diseases. Plant disease epidemiological models have many different shapes. We propose a summary of modelling structures for plant disease epidemics, which stem from the concepts of infection rate, of site, of basic infection rate corrected for removals (Rc), and of basic reproductive number (R0). Crop Losses, the quantitative and qualitative impacts of diseases and pests on Crop performances, can be expressed along many different dimensions. We focus on yield loss, defined as the difference between the attainable yield and the actual yield, in a production situation. The modelling of yield loss stems from the concept of damage mechanism, which can be applied to the wide range of organisms (including pathogens, weeds, arthropods, or nematodes) that may negatively affect Crop growth and performances. Damage mechanisms are incorporated in Crop growth models to simulate yield Losses. In both fields, epidemiology and Crop loss, we discuss the process of model development, including model simplification. We emphasize model simplification as a main avenue towards model genericity. This is especially relevant to enable addressing the diversity of Crop pathogens and pests. We also discuss the usefulness of considering differing evaluation criteria depending on the stage of model development, and thus, depending on modelling objectives. We illustrate progress made on two global key Crops where model simplification has been critical; rice and wheat. Modelling pests and diseases, and of the yield Losses they cause on these two Crops, lead us to propose the concept of Crop health syndrome as a set of injury functions, each representing the dynamics of an injury (such as, for example, the time-course of an epidemic). Crop health in a given context can be represented by the set of such injury functions, which in turn can be used as drivers for Crop loss models.

  • assessment of Crop health and Losses to plant diseases in world agricultural foci powerpoint abstract
    International Congress of Plant Pathology (2018): Plant Health in A Global Economy, 2018
    Co-Authors: Andrew Nelson, Serge Savary, Laetitia Willocquet, Paul D. Esker, S J Pethybridge, Neil Mcroberts
    Abstract:

    In November 2016, the ISPP Crop Loss Subject Matter Committee initiated a three‐month online global survey of Crop Losses. The global survey appears to be the first collective expert assessment of the importance of Losses caused by diseases and pests of the world’s five most important food Crops; wheat, rice, maize, soybean and potato. This voluntary survey on the location, frequency and magnitude of Crop Losses caused by diseases and pests generated 990 responses from 216 experts in 67 countries. These responses were used to generate expert‐based estimates of Crop Losses using a three step procedure: (i) an assessment of the representativeness and validity of the survey results by comparing the reported Losses and geographic distribution of diseases and pests to those in the CABI Crop Protection Compendium datasheets and other references; (ii) computation of average Losses per disease or pest per country for each country that featured in the survey results and imputation of Losses for countries where the disease or pest was known to occur but was not reported, and; (iii) adjustment of these average Losses based on national Crop production statistics and the ecology and management of each disease and pest. This talk presents the estimated Losses by Crop and by disease or pest, both globally and across eight geographic food security foci. We compare these new estimates with existing estimates and discuss the importance and validity of this global Crop loss assessment.

  • Crop Losses due to diseases and their implications for global food production Losses and food security
    Food Security, 2012
    Co-Authors: Serge Savary, Andrea Ficke, Jeannoel Aubertot, Clayton A Hollier
    Abstract:

    The status of global food security, i.e., the balance between the growing food demand of the world population and global agricultural output, combined with discrepancies between supply and demand at the regional, national, and local scales (Smil 2000; UN Department of Economic and Social Affairs 2011; Ingram 2011), is alarming. This imbalance is not new (Dyson 1999) but has dramatically worsened during the recent decades, culminating recently in the 2008 food crisis. It is important to note that in mid-2011, food prices were back to their heights of the middle of the 2008 crisis (FAO 2011). Plant protection in general and the protection of Crops against plant diseases in particular, have an obvious role to play in meeting the growing demand for food quality and quantity (Strange and Scott 2005). Roughly, direct yield Losses caused by pathogens, animals, and weeds, are altogether responsible for Losses ranging between 20 and 40 % of global agricultural productivity (Teng and Krupa 1980; Teng 1987; Oerke et al. 1994; Oerke 2006). Crop Losses due to pests and pathogens are direct, as well as indirect; they have a number of facets, some with short-, and others with long-term consequences (Zadoks 1967). The phrase “Losses between 20 and 40 %” therefore inadequately reflects the true costs of Crop Losses to consumers, public health, societies, environments, economic fabrics and farmers. The components of food security include food availability (production, import, reserves), physical and economic access to food, and food utilisation (e.g., nutritive value, safety), as has been recently reviewed by Ingram (2011). Although Crop Losses caused by plant disease directly affect the first of these components, they also affect others (e.g., the food utilisation component) directly or indirectly through the fabrics of trade, policies and societies (Zadoks 2008). Most of the agricultural research conducted in the 20th century focused on increasing Crop productivity as the world population and its food needs grew (Evans 1998; Smil 2000; Nellemann et al. 2009). Plant protection then primarily focused on protecting Crops from yield Losses due to biological and non-biological causes. The problem remains as challenging today as in the 20th century, with additional complexity generated by the reduced room for manoeuvre available environmentally, economically, and socially (FAO 2011; Brown 2011). This results from shrinking natural resources that are available to agriculture: these include water, agricultural land, arable soil, biodiversity, the availability of non-renewable energy, human labour, fertilizers (Smil 2000), and the deployment of some key inputs, such as high quality seeds and planting material (Evans 1998). In addition to yield Losses caused by diseases, these new elements of complexity also include post harvest quality Losses and the possible accumulation of toxins during and after the S. Savary (*) : J.-N. Aubertot INRA, UMR1248 AGIR, 24 Chemin de Borde Rouge, Auzeville, CS52627, 31326 Castanet-Tolosan Cedex, France e-mail: Serge.Savary@toulouse.inra.fr

  • quantification and modeling of Crop Losses a review of purposes
    Annual Review of Phytopathology, 2006
    Co-Authors: Serge Savary, P S Teng, Laetitia Willocquet, Forrest W Nutter
    Abstract:

    This review considers the cascade of events that link injuries caused by plant pathogens on Crop stands to possible (quantitative and qualitative) Crop Losses (damage), and to the resulting economic Losses. To date, much research has focused on injury control to prevent this cascade of events from occurring. However, this cascade involves a complex succession of components and processes whereby knowledge on Crop loss generates entry points for management. Proposed here is a framework linking different types of knowledge on Crop loss to a range of decision categories, from tactical to strategic short- or long-term. Important advances in this field are now under way, including a probabilistic treatment of the injury-damage relationship, or analyses of the sources of uncertainty attached to some components of the decision process. Management of injury profiles, rather than individual injuries, and shifts in dimensionality of Crop Losses are anticipated to contribute to the design of sustainable agricultural systems, and address global issues concerning food security and food safety.

Charles S Johnson - One of the best experts on this subject based on the ideXlab platform.

  • plant mortality distribution and Crop Losses in flue cured tobacco
    Plant Disease, 1991
    Co-Authors: Charles S Johnson
    Abstract:

    Relationships between Crop Losses in flue-cured tobacco (Nicotiana tabacum) and the distribution of plant mortality due to black shank, caused by Phytophthora parasitica var. nicotianae race 0, or injection of glyphosate were investigated in Virginia during 1986-1987. The number of plants compensating for the yield of adjacent dead plants decreased as plant mortality became increasingly clustered. Plot yield and gross economic returns increased linearly with the number of compensating plants per plot. In 1986, relationships between plant mortality distribution and plot yield or gross economic returns were unaffected by cause of death (P. p. nicotianae vs. a 41% solution of glyphosate) or inoculation date (4 vs. 6 wk after transplanting) (.)

Jonas Jagermeyr - One of the best experts on this subject based on the ideXlab platform.

  • severity of drought and heatwave Crop Losses tripled over the last five decades in europe
    Environmental Research Letters, 2021
    Co-Authors: Teresa Armada Bras, Julia Seixas, Nuno Carvalhais, Jonas Jagermeyr
    Abstract:

    Extreme weather disasters (EWD) can jeopardize domestic food supply and disrupt commodity markets. However, historical impacts on European Crop production associated with droughts, heatwaves, floods, and cold waves are not well understood - especially in view of potential adverse trends in the severity of impacts due to climate change. Here, we combine observational agricultural data (FAOSTAT) with an extreme weather disaster database (EM-DAT) between 1961 and 2018 to evaluate European Crop production responses to EWD. Using a compositing approach (superposed epoch analysis), we show that historical droughts and heatwaves reduced European cereal yields on average by 9 and 7.3%, respectively, associated with a wide range of responses (inter-quartile range +2 to -23%; +2 to -17%). Non-cereal yields declined by 3.8 and 3.1% during the same set of events. Cold waves led to cereal and non-cereal yield declines by 1.3 and 2.6%, while flood impacts were marginal and not statistically significant. Production Losses are largely driven by yield declines, with no significant changes in harvested area. While all four event frequencies significantly increased over time, the severity of heatwave and drought impacts on Crop production roughly tripled over the last 50 years, from –2.2 (1964-1990) to -7.3% (1991-2015). Drought-related cereal production Losses are shown to intensify by more than 3% per year. Both the trend in frequency and severity can possibly be explained by changes in the vulnerability of the exposed system and underlying climate change impacts.

Arun S Malik - One of the best experts on this subject based on the ideXlab platform.

  • predicting high magnitude low frequency Crop Losses using machine learning an application to cereal Crops in ethiopia
    AGUFM, 2018
    Co-Authors: Michael L Mann, James M Warner, Arun S Malik
    Abstract:

    Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in Losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method—combining remotely sensed data with agricultural survey data—that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely sensed data obtained near mid-season to predict substantial Crop Losses of greater than or equal to 25% due to drought at the village level for five primary cereal Crops. We train machine learning models to predict the likelihood of Losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 81% accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of soon-to-be available high-resolution, remotely sensed data such as the Harmonized Landsat Sentinel (HLS) data set.

  • predicting high magnitude low frequency Crop Losses using machine learning an application to cereal Crops in ethiopia
    Research Papers in Economics, 2018
    Co-Authors: Michael L Mann, James M Warner, Arun S Malik
    Abstract:

    Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in Losses at the village-level that are missing when reporting even at the zonal level. In this paper we propose a new data fusion method combining remotely-sensed data with agricultural survey data that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely-sensed data obtained near mid-season to predict substantial Crop Losses of greater than or equal to 25 percent due to drought at the village level for five primary cereal Crops. We train machine learning models to predict the likelihood of Losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 70 percent accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of newly available high resolution remotely-sensed data, such as the Harmonized Landsat Sentinel (HLS) data set.

L Karthikeyan - One of the best experts on this subject based on the ideXlab platform.

  • a review of remote sensing applications in agriculture for food security Crop growth and yield irrigation and Crop Losses
    Journal of Hydrology, 2020
    Co-Authors: L Karthikeyan, Ila Chawla, Ashok K Mishra
    Abstract:

    Abstract The global population is expected to reach 9.8 billion by 2050. There is an exponential growth of food production to meet the needs of the growing population. However, the limited land and water resources, climate change, and an increase in extreme events likely to pose a significant threat for achieving the sustainable agriculture goal. Given these challenges, food security is included in the United Nations’ Sustainable Development Goals (SDGs). Since the advent of Sputnik, followed by the Explorer missions, satellite remote sensing is assisting us in collecting the data at global scales. In this work, we review how satellite remote sensing information is utilized to assess and manage agriculture, an important component of ecohydrology. Overall, three critical aspects of agriculture are considered: (a) Crop growth and yield through empirical models, physics-based models, and data assimilation in Crop models, (b) applications pertaining to irrigation, which include mapping irrigation areas and quantification of irrigation, and (c) Crop Losses due to pests, diseases, Crop lodging, and weeds. The emphasis is on satellite sensors in optical, thermal, microwave, and fluorescence frequencies. We conclude the review with an outlook of challenges and recommendations. This paper is the first of a two-part review series. The second part reviews the role of satellite remote sensing in water security, wherein we discuss the aspects of water quality and quantity along with extremes (floods and droughts).