Abstract
A crop model incorporating proximal sensing images from a remote-controlled aerial system (RAS) can serve as an enhanced alternative for monitoring field-based geospatial crop productivity. This study aimed to investigate wheat productivity for different cultivars and various nitrogen application regimes and determine the best management practice scenario. We simulated spatiotemporal wheat growth and yield by integrating RAS-based sensing images with a crop-modeling system to achieve the study objective. We conducted field experiments and proximal sensing campaigns to acquire the ground truth data and RAS images of wheat growth conditions and yields. These experiments were performed at Gyeongsang National University (GNU), Jinju, South Gyeongsang province, Republic of Korea (ROK), in 2018 and 2019 and at Chonnam National University (CNU), Gwangju, ROK, in 2018. During the calibration at GNU in 2018, the wheat yields simulated by the modeling system were in agreement with the corresponding measured yields without significant differences (p = 0.27–0.91), according to two-sample t-tests. Furthermore, the yields simulated via this approach were in agreement with the measured yields at CNU in 2018 and at GNU in 2019 without significant differences (p = 0.28–0.86), as evidenced by two-sample t-tests; this proved the validity of the proposed modeling system. This system, when integrated with remotely sensed images, could also accurately reproduce the geospatial variations in wheat yield and growth variables. Given the results of this study, we believe that the proposed crop-modeling approach is applicable for the practical monitoring of wheat growth and productivity at the field level.
Highlights
Wheat (Triticum) is a global staple food crop, a cereal grain, and a grass cultivated worldwide with broad adaptability from temperate to cold environments (Martin et al, 2005; Shewry, 2009)
We found that the modeling system closely reproduced the measured data showing differences in leaf area index (LAI) and above-ground dry mass (AGDM) between the cultivars and planting seasons
This study introduced an RS-integrated crop model (RSCM) for simulating the fieldbased geospatial variations in wheat growth and yield to determine a best management practice to achieve improved wheat productivity
Summary
Wheat (Triticum) is a global staple food crop, a cereal grain, and a grass cultivated worldwide with broad adaptability from temperate to cold environments (Martin et al, 2005; Shewry, 2009). A mathematical crop model contains growth parameters specific to cultivars and environments, and it is highly dependent on the growth and development of the canopy (Ahuja et al, 2000; Jones et al, 2003). Steady simulations using a crop model require many inputs, encompassing parameters, and variables related to the environment, soil, and weather. A mathematical crop model is often weak in terms of geographical or regional simulations of crop growth and productivity with realistic precision. This drawback is attributable to the lack of appropriate spatial and temporal information regarding canopy growth (Doraiswamy et al, 2003) and the flaws in the input data (Moulin et al, 1998)
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call