Abstract
Understanding the detailed timing of crop phenology and their variability enhances grain yield and quality by providing precise scheduling of irrigation, fertilization, and crop protection mechanisms. Advances in information and communication technology (ICT) provide a unique opportunity to develop agriculture-related tools that enhance wall-to-wall upscaling of data outputs from point-location data to wide-area spatial scales. Because of the heterogeneity of the worldwide agro-ecological zones where crops are cultivated, it is unproductive to perform plant phenology research without providing means to upscale results to landscape-level while safeguarding field-scale relevance. This paper presents an advanced, reproducible, and open-source software for plant phenology prediction and mapping (PPMaP) that inputs data obtained from multi-location field experiments to derive models for any crop variety. This information can then be applied consecutively at a localized grid within a spatial framework to produce plant phenology predictions at the landscape level. This software runs on the ‘Windows’ platform and supports the development of process-oriented and temperature-driven plant phenology models by intuitively and interactively leading the user through a step-by-step progression to the production of spatial maps for any region of interest in sub-Saharan Africa. Maize (Zea mays L.) was used to demonstrate the robustness, versatility, and high computing efficiency of the resulting modeling outputs of the PPMaP. The framework was implemented in R, providing a flexible and easy-to-use GUI interface. Since this allows for appropriate scaling to the larger spatial domain, the software can effectively be used to determine the spatially explicit length of growing period (LGP) of any variety.
Highlights
The recent rise and evolution of data analytics have greatly modernized the worldwide agriculture environment thereby improving the efficiency of monitoring farming environments [1,2]
Agriculture 2020, 10, 515 intensively over the last decade with the intent to enhance agricultural production at scale [3,4,5,6,7,8]. The output of such technologies has revolutionized the empirical optimization of production and accurate predictions of output which has aided in precision agriculture planning and management [7,9,10]
The period prediction and mapping (PPMaP) package, freely available at https://github.com/Atoundem/PPMaP, combines data obtained from multi-location field experiments for plant phenology to derive temperature-dependent models for any crop variety
Summary
The recent rise and evolution of data analytics have greatly modernized the worldwide agriculture environment thereby improving the efficiency of monitoring farming environments [1,2]. The PPMaP package, freely available at https://github.com/Atoundem/PPMaP, combines data obtained from multi-location field experiments for plant phenology to derive temperature-dependent models for any crop variety This information is applied consecutively at an individual grid-level within a spatial framework to produce predictions at scale [8]. The PPMaP is a framework that runs on the ‘Windows’ platform and enables multi-location field experiments for plant phenology to derive temperature-dependent models for any crop variety and provides a grid specific recommendation domain for the variety This enables the successful extrapolation of the length of growing periods (LGPs) for any varieties with high levels of accuracy with the lowest potential risk of failure [14]. In sub-Saharan Africa, maize stands out as an essential staple food, providing approximately 25% of total calories in the average diet of the majority of the population [29,30]
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