Robust spatial frameworks for leveraging research on sustainable crop intensification

Meeting demand for food, fiber, feed, and fuel in a world with 9.7 billion people by 2050 without negative environmental impact is the greatest scientific challenge facing humanity. We hypothesize that this challenge can only be met with current and emerging technologies if guided by proactive use of a broad array of relevant data and geospatial scaling approaches to ensure local to global relevance for setting research priorities and implementing agricultural systems responsive to real-time status of weather, soils, crops, and markets. Despite increasing availability of field-scale agricultural data, robust spatial frameworks are lacking to convert these data into actionable knowledge. This commentary article highlights this knowledge gap and calls attention to the need for developing robust spatial frameworks that allow appropriate scaling to larger spatial domains by discussing a recently developed example of a data-driven strategy for estimating yield gaps of agricultural systems. To fully leverage research on sustainable intensification of cropping systems and inform policy development at different scales, we call for new approaches combining the strengths of top-down and bottom-up approaches which will require coordinated efforts between field scientists, crop modelers, and geospatial researchers at an unprecedented level

Estimating Maize Grain Yield From Crop Biophysical Parameters Using Remote Sensing

The overall objective of this investigation was to develop a robust technique to predict maize (Zea mays L.) grain yield that could be applied at a regional level using remote sensing with or without a simple crop growth simulation model. This study evaluated capabilities and limitations of the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index 250-m and MODIS surface reflectance 500-m products to track and retrieve information over maize fields. Results demonstrated the feasibility of using MODIS data to estimate maize green leaf area index (LAIg). Estimates of maize LAIg obtained from Wide Dynamic Range Vegetation Index using data retrieved from MODIS 250-m products (e.g. MOD13Q1) can be incorporated in crop simulation models to improve LAIg simulations by the Muchow-Sinclair-Bennet (MSB) model reducing the RMSE of LAIg simulations for all years of study under irrigation. However, more accurate estimates of LAIg did not necessarily imply better final yield (FY) predictions in the MSB maize model. The approach of incorporating better LAIg estimates into crop simulation models may not offer a panacea for problem solving; this approach is limited in its ability to simulate other factors influencing crop yields. On the other hand, the approach of relating key crop biophysical parameters at the optimum stage with maize grain final yields is a robust technique to early FY estimation over large areas. Results suggest that estimates of LAIg obtained during the mid-grain filling period can used to detect variability of maize grain yield and this technique offers a rapid and accurate (RMSE \u3c 900 kg ha-1) method to detect FY at county level using MODIS 250-m products

An evaluation of MODIS 8- and 16-day composite products for monitoring maize green leaf area index

The seasonal patterns of green leaf area index (GLAI) can be used to assess crop physiological and phenological status, to assess yield potential, and to incorporate in crop simulation models. This study focused on examining the potential capabilities and limitations of satellite data retrieved from the moderate resolution imaging spectroradiometer (MODIS) 8- and 16-day composite products to quantitatively estimate GLAI over maize (Zea mays L.) fields. Results, based on the nine years of data used in this study, indicated a wide variability of temporal resolution obtained from MODIS 8- and 16-day composite periods and highlighted the importance of information about day of MODIS products pixel composite for monitoring agricultural crops. Due to high maize GLAI temporal variability, the inclusion of day of pixel composite is necessary to decrease substantial uncertainties in estimating GLAI. Results also indicated that maize GLAI can be accurately retrieved from the 250-m resolution MODIS products (MOD13Q1 and MOD09Q1) by a wide dynamic range vegetation index with root mean square error (RMSE) below 0.60 m2 m−2 or by the enhanced vegetation index with RMSE below 0.70 m2 m−2

Robust spatial frameworks for leveraging research on sustainable crop intensification

Meeting demand for food, fiber, feed, and fuel in a world with 9.7 billion people by 2050 without negative environmental impact is the greatest scientific challenge facing humanity. We hypothesize that this challenge can only be met with current and emerging technologies if guided by proactive use of a broad array of relevant data and geospatial scaling approaches to ensure local to global relevance for setting research priorities and implementing agricultural systems responsive to real-time status of weather, soils, crops, and markets. Despite increasing availability of field-scale agricultural data, robust spatial frameworks are lacking to convert these data into actionable knowledge. This commentary article highlights this knowledge gap and calls attention to the need for developing robust spatial frameworks that allow appropriate scaling to larger spatial domains by discussing a recently developed example of a data-driven strategy for estimating yield gaps of agricultural systems. To fully leverage research on sustainable intensification of cropping systems and inform policy development at different scales, we call for new approaches combining the strengths of top-down and bottom-up approaches which will require coordinated efforts between field scientists, crop modelers, and geospatial researchers at an unprecedented level.This article is published as Grassini, Patricio, Cameron M. Pittelkow, Kenneth G. Cassman, Haishun S. Yang, Sotirios Archontoulis, Mark Licht, Kendall R. Lamkey et al. "Robust spatial frameworks for leveraging research on sustainable crop intensification." Global Food Security (2017). 10.1016/j.gfs.2017.01.002. Posted with permission.</p