“…After approximately 30 days, there was a very sharp decrease in the relative pesticide efficacy and our model suggests that this is because the root system outgrew the soil zone with efficacious concentrations of pesticide (typically for corn root nodes 4 and above). Malard et al (2020) also identified a strong influence of timing on the success of pest control action when modeling the lepidopteran Opisina arenosella as a pest of coconut farming in Sri Lanka; use of their modeling approach to determine optimal timing of biological control was found to outperform timing that was either on a fixed date or determined by pest population thresholds. Fig.…”
Chemical control of insect pests remains vital to agricultural productivity, but limited mechanistic understanding of the interactions between crop, pest and chemical control agent have restricted our capacity to respond to challenges such as the emergence of resistance and demands for tighter environmental regulation. Formulating effective control strategies that integrate chemical and non-chemical management for soil-dwelling pests is particularly problematic owing to the complexity of the soil-root-pest system and the variability that occurs between sites and between seasons. Here, we present a new concept, termed COMPASS, that integrates ecological knowledge on pest development and behaviour together with crop physiology and mechanistic understanding of chemical distribution and toxic action within the rhizosphere. The concept is tested using a two-dimensional systems model (COMPASS-Rootworm) that simulates root damage in maize from the corn rootworm
Diabrotica
spp. We evaluate COMPASS-Rootworm using 119 field trials that investigated the efficacy of insecticidal products and placement strategies at four sites in the USA over a period of ten years. Simulated root damage is consistent with measurements for 109 field trials. Moreover, we disentangle factors influencing root damage and pest control, including pest pressure, weather, insecticide distribution, and temporality between the emergence of crop roots and pests. The model can inform integrated pest management, optimize pest control strategies to reduce environmental burdens from pesticides, and improve the efficiency of insecticide development.
“…After approximately 30 days, there was a very sharp decrease in the relative pesticide efficacy and our model suggests that this is because the root system outgrew the soil zone with efficacious concentrations of pesticide (typically for corn root nodes 4 and above). Malard et al (2020) also identified a strong influence of timing on the success of pest control action when modeling the lepidopteran Opisina arenosella as a pest of coconut farming in Sri Lanka; use of their modeling approach to determine optimal timing of biological control was found to outperform timing that was either on a fixed date or determined by pest population thresholds. Fig.…”
Chemical control of insect pests remains vital to agricultural productivity, but limited mechanistic understanding of the interactions between crop, pest and chemical control agent have restricted our capacity to respond to challenges such as the emergence of resistance and demands for tighter environmental regulation. Formulating effective control strategies that integrate chemical and non-chemical management for soil-dwelling pests is particularly problematic owing to the complexity of the soil-root-pest system and the variability that occurs between sites and between seasons. Here, we present a new concept, termed COMPASS, that integrates ecological knowledge on pest development and behaviour together with crop physiology and mechanistic understanding of chemical distribution and toxic action within the rhizosphere. The concept is tested using a two-dimensional systems model (COMPASS-Rootworm) that simulates root damage in maize from the corn rootworm
Diabrotica
spp. We evaluate COMPASS-Rootworm using 119 field trials that investigated the efficacy of insecticidal products and placement strategies at four sites in the USA over a period of ten years. Simulated root damage is consistent with measurements for 109 field trials. Moreover, we disentangle factors influencing root damage and pest control, including pest pressure, weather, insecticide distribution, and temporality between the emergence of crop roots and pests. The model can inform integrated pest management, optimize pest control strategies to reduce environmental burdens from pesticides, and improve the efficiency of insecticide development.
“…Dampak dari praktik pengendalian hama pada komunitas serangga di pertanaman kedelai dapat dilihat melalui pendekatan jejaring makanan (Macfadyen et al 2015;Malard et al 2020).…”
“…Very low infestation was the best evaluated class with 90% of correctly calculated values in this class ( Agroecological management of pests relies on high complexity level systems. Agroecosystems require two integrations: a horizontal integration of the numerous populations of pests and a vertical integration of several combined management methods of pests (Aubertot et al, 2005;Malard et al, 2020). IPSIM-Cirsium only tackles the vertical integration of practices to control specifically C. arvense.…”
Section: Evaluation Of the Predictive Qualitymentioning
Throughout Europe, Cirsium arvense is the most problematic perennial weed in arable crops, whether managed under organic or conventional agriculture. Non-chemical control methods are limited with partial efficacy. Knowledge is missing on their effect across a wide gradient of cropping systems and pedoclimates. To achieve effective Cirsium arvense management ensuring crop productivity while limiting the reliance of cropping systems on herbicide, expert-based models are needed to gather knowledge on the effect of individual levers and their interactions in order to (i) design and assess finely tuned combinations of farming practices in different pedoclimates and (ii) support decisions for Cirsium arvense control. Based on expert-knowledge and literature, we developed IPSIM-Cirsium, a hierarchical qualitative model which evaluates the infestation of Cirsium arvense as a function of farming practices, climate conditions, soil descriptors and their interactions. IPSIM-Cirsium is a multi-attribute model considering all possibilities of interactions between factors, it estimates the infestation rate of the field graded according to a four-level scale. The model outputs were confronted to independent field observations collected across 6 fields, over a 16-year period in 3 sites. IPSIM-Cirsium showed a satisfactory predictive quality (accuracy of 78.2%). IPSIM-Cirsium can be used as a tool for crop advisors and researchers to assist the design of systems less reliant on herbicides, for farmers and advisers to assess ex-ante prototypes of cropping systems, and for teachers as an educational tool to share agroecological weed management knowledge.
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