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

C Brogi,L Weihermüller,M Herbst,T G Reichenau,A Klosterhalfen,H Vereecken,C Montzka,J A Huisman

doi:10.1002/vzj2.20009

C Brogi, L Weihermüller + Show 6 more

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https://doi.org/10.1002/vzj2.20009

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Abstract

AbstractAgroecosystem 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 cm3 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 (LAINDVI) 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.

Highlights

Horizonation and soil texture were obtained from the geophysics-based soil map, whereas bulk density was obtained from literature values and from previous unpublished soil sampling in the area
All these data were than propagated through two pedotransfer function (PTF) to obtain the soil hydraulic parameters used in the AgroC simulation
A low RMSE of 0.032 cm3 cm−3 was obtained when the soil profile provided by the geophysics-based soil map well represented the actual soil profile

Summary

Introduction

Reductions in crop performance can be directly or indirectly attributed to a number of factors, such as insufficient nutrient availability, inadequate crop and soil management practices, adverse weather conditions, occurrence of pests and diseases, and water shortage (Baret, Houles, & Guérif, 2007; Foley et al, 2011; Sánchez, 2010; Slingo, Challinor, Hoskins, & Wheeler, 2005). Agriculture is the largest consumer of freshwater (Brauman, Siebert, & Foley, 2013), with most crops being produced in rainfed conditions (Rosegrant, Ringler, & Zhu, 2009). Within this context, water stress caused by below-average precipitation is considered to be a severe threat for sustainable crop production in the coming decades (Anjum et al, 2011). Our ability to simulate within-field variability of water stress and the resulting reduction in crop growth is still limited (Batchelor, Basso, & Paz, 2002)

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