Predicting stem borer density in maize using RapidEye data and generalized linear models

Abdel‐Rahman, Elfatih M.; Landmann, Tobias; Richard, Kyalo; Ong’amo, George; Mwalusepo, Sizah; Sulieman, Saad; Rü, Bruno Le

doi:10.1016/j.jag.2016.12.008

Cited by 11 publications

(4 citation statements)

References 57 publications

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“…Factoring in other yield-reducing factors such as wild fires, pests and diseases would also help improve forecasts and crop damage assessment by insurers. While some studies have isolated the spectral differences due to pest damage, more work on other crops remains (Abdel-Rahman et al, 2017). This will establish further relationships making crop yield estimations more objective in relation to underlying factors.…”

Section: Satellite Data Yield Factors and Crop Monitoring Indicesmentioning

confidence: 99%

Satellite-Based Crop Monitoring and Yield Estimation—A Review

Lungu¹,

Chabala²,

Shepande³

2020

JAS

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To sustain food security and crop condition monitoring, yield estimation must improve at local and global scales. The aim of this review was to give a background of satellite-based crop monitoring and crop yield estimation, including the use of crop models. Recently, most advances in remote sensing techniques, aimed at complimenting the traditional crop harvest surveys, have focused on high-production and information-rich areas. However, there is limited research in dynamic landscapes using these techniques at local scales in most Southern African countries. Models such as the Decision Support System Agro-Technology’s (DSSAT) CERES-model, and Agricultural Production Simulator (APSIM) have been used to simulate maize biophysical parameters and yield variability in a changing climate. Despite the successes, there is still need to consider yield prediction using simplified models that decision-makers can use to plan for food support and sales. The application of freely-available satellite data with focus on maize crop as a staple for Southern Africa, highlights some challenges such as heavy reliance on agro-meteorological estimations and regional estimations of crop yield. It also raises questions of predicting across large growing belts without consideration of diverse cropping patterns. Conversely, future opportunities in crop monitoring and yield estimation using remotely sensed-data still shed a light of hope. For instance, employing multi-model configurations or multi-model ensembles is one of the major missing gaps needing consideration by crop modeling research. Other simpler, but versatile opportunities are the use of crop –monitoring applications on smart phones by small holder farmers to provide phenological data to decision makers throughout a growing season.

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Section: Satellite Data Yield Factors and Crop Monitoring Indicesmentioning

confidence: 99%

Satellite-Based Crop Monitoring and Yield Estimation—A Review

Lungu¹,

Chabala²,

Shepande³

2020

JAS

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“…Binary logistic modeling has been successfully proven in numerous studies using remote sensing variables as predictors. Such models are also known to render robust variable relevancies, when correlation among variables is accounted for [46,47].…”

Section: ) Logistic Regression Modelmentioning

confidence: 99%

A Spatially Transferable Drought Hazard and Drought Risk Modeling Approach Based on Remote Sensing Data

et al. 2020

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Drought adversely affects vegetation conditions and agricultural production and consequently the food security and livelihood situation of the often most vulnerable communities. In spite of recent advances in modeling drought risk and impact, coherent and explicit information on drought hazard, vulnerability and risk is still lacking over wider areas. In this study, a spatially explicit drought hazard, vulnerability, and risk modeling framework was investigated for agricultural land, grassland and shrubland areas. The developed drought hazard model operates on a higher spatial resolution than most available drought models while also being scalable to other regions. Initially, a logistic regression model was developed to predict drought hazard for rangelands and croplands in the USA. The drought hazard model was cross-verified for the USA using the United States Drought Monitor (USDM). The comparison of the model with the USDM showed a good spatiotemporal agreement, using visual interpretation. Subsequently, the explicit and accurate USA model was transferred and calibrated for South Africa and Zimbabwe, where drought vulnerability and drought risk were assessed in combination with drought hazard. The drought hazard model used time series crop yields data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and biophysical predictors from satellite remote sensing (SPI, NDVI, NDII, LST, albedo). A McFadden’s Pseudo R² value of 0.17 for the South African model indicated a good model fit. The plausibility of the drought hazard model results in southern Africa was evaluated by using regional climate patterns, published drought reports and a visual comparison to a global drought risk model and food security classification data. Drought risk and vulnerability were assessed for southern Africa and could also be spatially explicit mapped showing, for example, lower drought vulnerability and risk over irrigated areas. The innovative aspect of the presented drought hazard model is that it can be applied to other countries at a global scale, since it only uses globally available data sets and therefore can be easily modified to account for country-specific characteristics. At the same time, it can capture regional drought conditions through a higher resolution than other existing global drought hazard models. This model addressed the gap between global drought models, that cannot spatially and temporally explicitly capture regional drought effects, and sub-regional drought models that may be spatially explicit but not spatially transferable. Since we used globally available and spatially consistent data sets (both as predictors and response variables), the approach of this study can potentially be used globally to enhance existing modelling routines, drought intervention strategies and preparedness measures.

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“…The abundance of natural enemies particularly may in turn influence pest population in the ecosystem (Copeland et al 2006). Studies have reported how NDVI can predict the distribution and abundance of insect pests and pollinators and monitor long-term landscape structure (Abdel-Rahman et al 2017, Toukem et al 2020, 2022). For example, Toukem et al (2020) reported a high density of Tephritid fruit flies infesting avocado, Persea americana Miller (Laurales: Lauraceae), in low NDVI.…”

Section: Introductionmentioning

confidence: 99%

Interactions between integrated pest management, pollinator supplementation, and normalized difference vegetation index in pumpkin, Cucurbita maxima (Cucurbitales: Cucurbitaceae), production

Waithaka,

Kasina,

Samita

et al. 2023

Environmental Entomology

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Sustainable production of pumpkin (Cucurbita maxima Duchesne) partly relies on integrated pest management (IPM) and pollination services. A farmer-managed field study was carried out in Yatta and Masinga Sub-Counties of Machakos County, Kenya, to determine the effectiveness of a recommended IPM package and its interaction with stingless bee colonies (Hypotrigona sp.) for pollinator supplementation (PS). The IPM package comprised Lynfield traps with cuelure laced with the organophosphate malathion, sprays of Metarhizium anisopliae (Mechnikoff) Sorokin isolate ICIPE 69, the most widely used fungal biopesticide in sub-Saharan Africa, and protein baits incorporating spinosad. Four treatments—IPM, PS, integrated pest and pollinator management (which combined IPM and PS), and control—were replicated 4 times. The experiment was conducted in 600 m2 farms in 2 normalized difference vegetation index (NDVI) classes during 2 growing seasons (October 2019–March 2020 and March–July 2020). Fruits showing signs of infestation were incubated for emergence, fruit fly trap catches were counted weekly, and physiologically mature fruits were harvested. There was no effect of IPM, PS, and NDVI on yield across seasons. This study revealed no synergistic effect between IPM and PS in suppressing Tephritid fruit fly population densities and damage. Hypotrigona sp. is not an efficient pollinator of pumpkin. Therefore, we recommend testing other African stingless bees in pumpkin production systems for better pollination services and improved yields.

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Predicting stem borer density in maize using RapidEye data and generalized linear models

Cited by 11 publications

References 57 publications

Satellite-Based Crop Monitoring and Yield Estimation—A Review

Satellite-Based Crop Monitoring and Yield Estimation—A Review

A Spatially Transferable Drought Hazard and Drought Risk Modeling Approach Based on Remote Sensing Data

Interactions between integrated pest management, pollinator supplementation, and normalized difference vegetation index in pumpkin, Cucurbita maxima (Cucurbitales: Cucurbitaceae), production

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