The Role of Harvest Timing in Pest Management: Grower Response to Infestation by the California Olive Fruit Fly

Crop Production/Industries,

Pests and Agricultural Commodity Losses: Evaluating Alternative Approaches to Damage Function Estimation

Estimating the economic impact of a pest requires linking biological and economic systems via a damage function. The most common damage function approach links exogenous pest populations to cumulative commodity yield losses at harvest. This type of representation is a reduced form because is not pest population levels per se that drive damage, but the underlying factors that affect pest populations and the susceptibility of the host. We specify and estimate a structural damage function and compare the results with those of the reduced form. We do so using two alternative models, one that explains the level of crop damage from a pest, and one that explains the timing of that damage during the host’s growing season. We address our objectives within an empirical application to the olive fruit fly in California. In formulating the structural damage function, we draw from current scientific literature on olive fly and olive fruit phenology. The structural damage function takes into account the feedback between climate, host susceptibility, and pest populations. Moreover, the structural approach disaggregates damage rates across space and time, unlike the typical reduced form. The estimation results indicate that endogeneity is a salient concern in both the timing of initial crop damage, and in the levels of damage evidenced in some cultivars. The structural damage function dominates the trapping-based reduced form in terms of explanatory power in every model estimated.Crop Production/Industries,

Implications of Simultaneity in a Physical Damage Function

A modeler must often rely on highly simplified representations of complex physical systems when analyzing associated economic issues. Herein, we consider a management problem in which a bioeconomic system exhibits simultaneity in processes governing productivity and damage. In this case, it may benefit the producer to sacrifice productivity to reduce the costs associated with increased damage. We specify empirically a structural damage relationship that explains the biological process by which an invasive species damages a host and estimate the structural model and its reduced form with an exceptional dataset on infestation of olives by the olive fruit fly. We contrast the results of these models with the approach typically taken in the economic literature, which expresses damage as a function of pest density. The population-based approach introduces significantly greater bias into the individual grower\u27s choice of damage-control inputs than estimates based on the structural model

Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance Across Composting Algorithms, Spectral Indices, and Sensors

There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the world’s diverted freshwater resources. We develop an improved irrigated land-use mapping algorithm that uses the seasonal maximum value of a spectral index to distinguish between irrigated and non-irrigated parcels in Idaho’s Snake River Plain. We compare this approach to two alternative algorithms that differentiate between irrigated and non-irrigated parcels using spectral index values at a single date or the area beneath spectral index trajectories for the duration of the agricultural growing season. Using six different pixel and county-scale error metrics, we evaluate the performance of these three algorithms across all possible combinations of two growing seasons (2002 and 2007), two datasets (MODIS and Landsat 5), and three spectral indices, the Normalized Difference Vegetation Index, Enhanced Vegetation Index and Normalized Difference Moisture Index (NDVI, EVI, and NDMI). We demonstrate that, on average, the seasonal-maximum algorithm yields an improvement in classification accuracy over the accepted single-date approach, and that the average improvement under this approach is a 60% reduction in county scale root mean square error (RMSE), and modest improvements of overall accuracy in the pixel scale validation. The greater accuracy of the seasonal-maximum algorithm is primarily due to its ability to correctly classify non-irrigated lands in riparian and developed areas of the study region

A Practical Guide for Managing Interdisciplinary Teams: Lessons Learned from Coupled Natural and Human Systems Research

Interdisciplinary team science is essential to address complex socio-environmental questions, but it also presents unique challenges. The scientific literature identifies best practices for high-level processes in team science, e.g., leadership and team building, but provides less guidance about practical, day-to-day strategies to support teamwork, e.g., translating jargon across disciplines, sharing and transforming data, and coordinating diverse and geographically distributed researchers. This article offers a case study of an interdisciplinary socio-environmental research project to derive insight to support team science implementation. We evaluate the project’s inner workings using a framework derived from the growing body of literature for team science best practices, and derive insights into how best to apply team science principles to interdisciplinary research. We find that two of the most useful areas for proactive planning and coordinated leadership are data management and co-authorship. By providing guidance for project implementation focused on these areas, we contribute a pragmatic, detail-oriented perspective on team science in an effort to support similar projects

Estimación de la concentración media diaria de material particulado fino en la región del Complejo Industrial y Portuario de Pecém, Ceará, Brasil

A exposição ao material particulado fino (MP2,5) está associada a inúmeros desfechos à saúde. Desta forma, monitoramento da concentração ambiental do MP2,5 é importante, especialmente em áreas amplamente industrializadas, pois abrigam potenciais emissores do MP2,5 e de substâncias com potencial de aumentar a toxicidade de partículas já suspensas. O objetivo desta pesquisa é estimar a concentração diária do MP2,5 em três áreas de influência do Complexo Industrial e Portuário do Pecém (CIPP), Ceará, Brasil. Foi aplicado um modelo de regressão não linear para a estimativa do MP2,5, por meio de dados de profundidade óptica monitorados por satélite. As estimativas foram realizadas em três áreas de influência (Ai) do CIPP (São Gonçalo do Amarante – Ai I, Paracuru e Paraipaba – Ai II e Caucaia – Ai III, no período de 2006 a 2017. As médias anuais das concentrações estimadas foram inferiores ao estabelecido pela legislação nacional em todas as Ai (8µg m-3). Em todas as Ai, os meses referentes ao período de seca (setembro a fevereiro) apresentaram as maiores concentrações e uma predominância de ventos leste para oeste. Os meses que compreendem o período de chuva (março a agosto) apresentaram as menores concentrações e ventos menos definidos. As condições meteorológicas podem exercer um papel importante nos processos de remoção, dispersão ou manutenção das concentrações do material particulado na região. Mesmo com baixas concentrações estimadas, é importante avaliar a constituição das partículas finas dessa região, bem como sua possível associação a efeitos adversos à saúde da população local.Exposure to fine particulate matter (PM2.5) is associated with numerous negative health outcomes. Thus, monitoring the environmental concentration of PM2.5 is important, especially in heavily industrialized areas, since they harbor potential emitters of PM2.5 and substances with the potential to increase the toxicity of already suspended particles. This study aims to estimate daily concentrations of PM2.5 in three areas under the influence of the Industrial and Port Complex of Pecém (CIPP), Ceará State, Brazil. A nonlinear regression model was applied to estimate PM2.5, using satellitemonitored optical depth data. Estimates were performed in three areas of influence (Ai) of the CIPP (São Gonçalo do Amarante – AiI, Paracuru and Paraipaba – AiII, and Caucaia – AiIII), from 2006 to 2017. Estimated mean annual concentrations were lower than established by Brazil’s national legislation in all three Ai (8µg m-³). In all the Ai, the months of the dry season (September to February) showed the highest concentrations and a predominance of east winds, while the months of the rainy season (March to August) showed the lowest concentrations and less defined winds Weather conditions can play an important role in the removal, dispersal, or maintenance of concentrations of particulate matter in the region. Even at low estimated concentrations, it is important to assess the composition of fine participles in this region and their possible association with adverse health outcomes in the local population.La exposición al material particulado fino (MP2,5) está asociada a innumerables problemas de salud. Por ello, la supervisión de la concentración ambiental del MP2,5 es importante, especialmente en áreas ampliamente industrializadas, puesto que albergan potenciales emisores de MP2,5 y de sustancias con potencial de aumentar la toxicidad de partículas ya suspendidas. El objetivo de esta investigación es estimar la concentración diaria del MP2,5 en tres áreas de influencia del Complejo Industrial y Portuario de Pecém (CIPP), Ceará, Brasil. Se aplicó un modelo de regresión no lineal para la estimación del MP2,5, mediante datos de profundidad óptica supervisados por satélite. Las estimaciones fueron realizadas en tres áreas de influencia (Ai) del CIPP (São Gonçalo do Amarante – Ai I, Paracuru y Paraipaba – Ai II y Caucaia – Ai III en el período de 2006 a 2017. Las medias anuales de las concentraciones estimadas fueron inferiores a lo establecido por la legislación nacional en todas las Ai (8µg m-³). En todas las Ai, los meses referentes al período de sequía (de setiembre a febrero) presentaron las mayores concentraciones y una predominancia de vientos este a oeste, los meses que comprenden el período de lluvia (marzo a agosto) presentaron las menores concentraciones y vientos menos definidos. Las condiciones meteorológicas pueden ejercer un papel importante en los procesos de eliminación, dispersión o mantenimiento de las concentraciones del material particulado en la región. Incluso con bajas concentraciones estimadas es importante que se evalúe la constitución de las partículas finas de esta región, así como su posible asociación con efectos adversos para la salud de la población local

Managing a Pest with Harvest Timing: Implications for Crop Quality and Price

This article examines a case in which growers’ pest management decisions collectively generate a change in price that reduces the losses from infestation for some but further harms others. Olive growers in California control the olive fruit fly not only by spraying insecticides but also by harvesting olives earlier, sacrificing quality and altering the industry’s fruit quality distribution. Growers of higher quality fruit alter harvest timing the most, benefiting from the resulting change in the quality premium at the expense of growers of lower quality fruit. Across the industry, the change in the quality premium leads to greater reliance on chemical control

Pests and Agricultural Commodity Losses: Evaluating Alternative Approaches to Damage Function Estimation

Estimating the economic impact of a pest requires linking biological and economic systems via a damage function. The most common damage function approach links exogenous pest populations to cumulative commodity yield losses at harvest. This type of representation is a reduced form because is not pest population levels per se that drive damage, but the underlying factors that affect pest populations and the susceptibility of the host. We specify and estimate a structural damage function and compare the results with those of the reduced form. We do so using two alternative models, one that explains the level of crop damage from a pest, and one that explains the timing of that damage during the host’s growing season. We address our objectives within an empirical application to the olive fruit fly in California. In formulating the structural damage function, we draw from current scientific literature on olive fly and olive fruit phenology. The structural damage function takes into account the feedback between climate, host susceptibility, and pest populations. Moreover, the structural approach disaggregates damage rates across space and time, unlike the typical reduced form. The estimation results indicate that endogeneity is a salient concern in both the timing of initial crop damage, and in the levels of damage evidenced in some cultivars. The structural damage function dominates the trapping-based reduced form in terms of explanatory power in every model estimated

Erratum: Chance, E.W., et al. Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Compositing Algorithms, Spectral Indices, and Sensors. Remote Sens. 2017, 9, 546

In the published paper [1], the title and Appendix Tables A4, A5, A7, and A8 contain typographical errors. The correct title and table captions are as follows: [...