Use of Multispectral Airborne Images to Improve In-Season Nitrogen Management, Predict Grain Yield and Estimate Economic Return of Maize in Irrigated High Yielding Environments

Abstract

Vegetation indices (VIs) derived from active or passive sensors have been used for maize growth monitoring and real-time nitrogen (N) management at field scale. In the present multilocation two-year study, multispectral VIs (green-and red-based), chlorophyll meter (SPAD) and plant height (PltH) measured at V12-VT stage of maize development, were used to distinguish among the N status of maize, to predict grain yield and economic return in high yielding environments. Moreover, linear plateau-models were performed with VIs, SPAD and PltH measurements to determine the amount of N needed to achieve maximum maize grain yields and economic return. The available N in the topsoil (0-30 cm) was measured, and its relationship with VIs, maize yield and maize N requirements was analyzed. Green-based VIs were the most accurate indices to predict grain yield and to estimate the grain yield optimum N rate (GYON r ) (216.8 kg N ha −1 ), but underestimated the grain yield optimum N available (GYON a ) (248.6 kg N ha −1 ). Red-based VIs slightly overestimated the GYON r and GYON a , while SPAD highly underestimated both of them. The determination of the available N did not improve the accuracy of the VIs to determine the grain yield. The green chlorophyll index (GCI) distinguished maize that would yield less than 84% of the maximum yield, showing a high potential to detect and correct maize N deficiencies at V12 stage. The economic optimum nitrogen rate (EON r ) and economic optimum nitrogen available (EON a ) were determined below the GYON r and the GYON a , demonstrating that maximum grain yield strategies in maize are not normally the most profitable for farmers. Further research is needed to fine-tune the response of maize to N applications when deficiencies are detected at V12 stage, but airborne imagery could be useful for practical farming implementation in irrigated high yielding environments.

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

“…One of the most frequent applications of PA techniques is the use of multi-spectral images to improve in-season N management and yield prediction [19]. Those applications are very interesting for maize growers, because maize is a high N-demanding crop where insufficient supply of N can result in important economic losses, whereas excessive fertilization implies wasting resources and increasing environmental pollution.…”

“…In this respect, other works have demonstrated the effectiveness of remote sensing-based vegetation indices to track phenological changes such as leaf greenness to infer the maize N status and needs, as well as for yield prediction. For example, Maresma et al [19,24] investigated the correlation of different multispectral vegetation indices with the N status at V12 (12 leaves with visible leaf collar) just before tasselling with a single date image. However, other authors point out that good estimator of biomass and yield can be derived from the use of temporal series of remote-sensing images to characterize dynamics of crop canopy throughout the growing and development cycle [25,26].…”

Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

“…This supports the need for having an N recommendation tool that combines both temporal and spatial variability to better predict the EONR [5,78,79]. For ZP17, imagery was able to capture the response to the base N application, prior to in-season N application [23,25,53,59,80]. Imagery obtained after in-season N application (14 July and 4 September; Figure 4) indicated that the in-season application adequately addressed crop N needs as treatment differences were no longer present [24,25,80].…”

“…While much work has been done demonstrating that a UAV with passive multispectral sensors can detect N stress in cereal crops [53,[58][59][60], little has been done to transform these multispectral sensor readings into a practical N recommendation system to be applied in commercial farming operations [61], thereby further contributing to the limited adoption of this technology. Therefore, the present work developed, implemented, and evaluated a practical system for transforming passive multispectral imagery collected with a UAV into a spatially variable, in-season N prescription.…”

Peer Review #2 of "Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data (v0.2)"

“…Maize is one of the main food crops today and is planted on a large scale worldwide. The timely and effective access to high-resolution spatial crop-development information provides important guidance for precision agricultural management, which allows the implementation of effective fertilization programs (Cilia et al 2014;Gracia-Romero et al 2017;Samborski et al 2009), irrigation measures (Barker et al 2018;Ma et al 2018;Maresma et al 2018), and early production forecasts (Elazab et al 2016;Kitchen et al 2003;Vergara-Diaz et al 2016). Crop above-ground biomass (AGB) is an important indicator for effectively assessing crop growth and yield, and also an important ecological indicator for assessing the efficiency of which crops use light and store carbon in ecosystems.…”