Explaining Andean Potato Weevils in Relation to Local and Landscape Features: A Facilitated Ecoinformatics Approach

Parsa, Soroush; Ccanto, Raul; Olivera, Edgar; Scurrah, Martin; Alcázar, Juan Francisco Jiménez; Rosenheim, Jay A.

doi:10.1371/journal.pone.0036533

Cited by 17 publications

(9 citation statements)

References 23 publications

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“…In line with other studies in related domains, this study demonstrates that the use of empirical modelling techniques on observational data is well suited to help solving very concrete problems, and that it supports learning from data to confirm or update the knowledge of decision makers [ 31 , 76 , 77 ]. Among the existing techniques, CIF models were reported as a flexible and powerful alternative for these types of analyses [ 78 , 79 ].…”

Section: Resultssupporting

confidence: 83%

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches

et al. 2016

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Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability.

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Section: Resultssupporting

confidence: 83%

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches

et al. 2016

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“…A study describing the distribution of Andean potato weevils, Premnotrypes spp. (Coleoptera: Curculionidae), showed heavier infestation of potato tubers along field edges (Parsa et al 2012). Edge-biased distribution was also reported regarding wheat stem sawflies, Cephus cinctus Norton (Hymenoptera: Cephidae), in dryland wheat fields (Nansen et al 2005a(Nansen et al , 2005b.…”

Section: Edge-biased Distributions In Open Field Systemsmentioning

confidence: 90%

Edge-biased distributions of insects. A review

Nguyen

Nansen

2018

Agron. Sustain. Dev.

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Spatial ecology includes research into factors responsible for observed distribution patterns of organisms. Moreover, the spatial distribution of an animal at a given spatial scale and in a given landscape may provide valuable insight into its host preference, fitness, evolutionary adaptation potential, and response to resource limitations. In agro-ecology, in-depth understanding of spatial distributions of insects is of particular importance when the goals are to (1) promote establishment and persistence of certain food webs, (2) maximize performance of pollinators and natural enemies, and (3) develop precision-targeted monitoring and detection of emerging outbreaks of herbivorous pests. In this article, we review and discuss a spatial phenomenon that is widespread among insect species across agricultural systems and across spatial scales-they tend to show "edge-biased distributions" (spatial distribution patterns show distinct "edge effects"). In the conservation and biodiversity literature, this phenomenon has been studied and reviewed intensively in the context of how landscape fragmentation affects species diversity. However, possible explanations of, and also implications of, edge-biased distributions of insects in agricultural systems have not received the same attention. Our review suggests that (1) mathematical modeling approaches can partially explain edge-biased distributions and (2) abiotic factors, crop vegetation traits, and environmental parameters are factors that are likely responsible for this phenomenon. However, we argue that more research, especially experimental research, is needed to increase the current understanding of how and why edge-biased distributions of insects are so widespread. We argue that the fact that many insect pests show edge-biased distribution patterns may be used to optimize both pest monitoring practices and precision targeting of pesticide applications and releases of natural enemies.

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“…A key weakness of the approach was the inability to consider the effects of very high L. hesperus densities, as these are excluded from the commercial farming setting by uniformly aggressive farmer pest suppression. The use of ecoinformatics in several subdisciplines of the agricultural sciences is growing [ 38 - 43 ], providing a research methodology that can complement traditional experimental approaches to enhancing agricultural productivity.…”

Section: Discussionmentioning

confidence: 99%

Ecoinformatics Can Reveal Yield Gaps Associated with Crop-Pest Interactions: A Proof-of-Concept

Rosenheim

Meisner

2013

PLoS ONE

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Farmers and private consultants execute a vast, decentralized data collection effort with each cropping cycle, as they gather pest density data to make real-time pest management decisions. Here we present a proof of concept for an ecoinformatics approach to pest management research, which attempts to harness these data to answer questions about pest-crop interactions. The impact of herbivory by Lygus hesperus on cotton is explored as a case study. Consultant-derived data satisfied a ‘positive control’ test for data quality by clearly resolving the expected negative relationship between L. hesperus density and retention of flower buds. The enhanced statistical power afforded by the large ecoinformatics dataset revealed an early-season window of crop sensitivity, during which L. hesperus densities as low as 1-2 per sample were associated with yield loss. In contrast, during the mid-season insecticide use by farmers was often unnecessary, as cotton compensated fully for moderate L. hesperus densities. Because the dataset emerged from the commercial production setting, it also revealed the limited degree to which farmers were willing to delay crop harvest to provide opportunities for compensatory fruiting. Observational approaches to pest management research have strengths and weaknesses that complement those of traditional, experimental approaches; combining these methods can contribute to enhanced agricultural productivity.

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Explaining Andean Potato Weevils in Relation to Local and Landscape Features: A Facilitated Ecoinformatics Approach

Cited by 17 publications

References 23 publications

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches

Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches

Edge-biased distributions of insects. A review

Ecoinformatics Can Reveal Yield Gaps Associated with Crop-Pest Interactions: A Proof-of-Concept

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