Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information

Abstract

Agroecosystem models that simulate crop growth as a function of weather conditions and soil characteristics are among the most promising tools for improving crop yield and achieving more sustainable agricultural production systems. This study aims at using spatially distributed crop growth simulations to investigate how field-scale patterns in soil properties obtained using geophysical mapping affect the spatial variability of soil water content dynamics and growth of crops at the square kilometer scale. For this, a geophysics-based soil map was intersected with land use information. Soil hydraulic parameters were calculated using pedotransfer functions. Simulations of soil water content dynamics performed with the agroecosystem model AgroC were compared with soil water content measured at two locations, resulting in RMSE of 0.032 and of 0.056 cm 3 cm −3 , respectively. The AgroC model was then used to simulate the growth of sugar beet (Beta vulgaris L.), silage maize (Zea mays L.), potato (Solanum tuberosum L.), winter wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.), and winter rapeseed (Brassica napus L.) in the 1-by 1-km study area. It was found that the simulated leaf area index (LAI) was affected by the magnitude of simulated water stress, which was a function of both the crop type and soil characteristics. Simulated LAI was generally consistent with the observed LAI calculated from normalized difference vegetation index (LAI NDVI) obtained from RapidEye satellite data. Finally, maps of simulated agricultural yield were produced for four crops, and it was found that simulated yield matched well with actual harvest data and literature values. Therefore, it was concluded that the information obtained from geophysics-based soil mapping was valuable for practical agricultural applications.

Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR

“…PAI increase also slows down in this temporal window. The structures appearing on the western side are consistent with those soil areas with increased sand and gravel fractions closer to the surface, which are known to be more affected by water scarcity [47]. These patterns are also similar to those experienced with the previous TLS study for crop height at the same field during the 2008 and 2009 growing seasons [48].…”

“…The field ranges from 101 m in the west to 103 m above mean sea level (MSL) in the east, with an average annual precipitation of 715 mm and average temperature of 10.2°C. The soil is composed of Pleistocene loess and translocated loess from the Holocene, along with sand and gravels at the lower depths, which were discovered at this site through previous geophysical studies [47]. The west side of the field has increased sand and gravel amounts that are at shallower depths, which has been shown to cause heterogeneity among the crops during periods of water scarcity [47].…”

“…The soil is composed of Pleistocene loess and translocated loess from the Holocene, along with sand and gravels at the lower depths, which were discovered at this site through previous geophysical studies [47]. The west side of the field has increased sand and gravel amounts that are at shallower depths, which has been shown to cause heterogeneity among the crops during periods of water scarcity [47]. Likewise, the same field was previously observed using a terrestrial laser scanner (TLS) in a study for crop height estimations [48].…”

“…where k(θ) is the light extinction coefficient for a given solar zenith angle. The k(θ) is generally obtained using the literature as ground observations are often not available and k(θ) is difficult to measure in the field [47]. Moreover, many studies use constant k(θ) over the growing period to simplify calculations [56].…”

“…Moreover, many studies use constant k(θ) over the growing period to simplify calculations [56]. A study calculating LAI for winter wheat using RapidEye multispectral satellite imagery was performed at the same study area in Selhausan, Germany [47]. In that study, k(θ) was derived by inverse parameterization with comparison to ground measurements.…”