Using spatially variable nitrogen application and crop responses to evaluate crop nitrogen use efficiency

Abstract

Low nitrogen use efficiency (NUE) is ubiquitous in agricultural systems, with mounting global scale consequences for both atmospheric aspects of climate and downstream ecosystems. Since NUE-related soil characteristics such as water holding capacity and organic matter are likely to vary at small scales (< 1 ha), understanding the influence of soil characteristics on NUE at the subfield scale (< 32 ha) could increase fertilizer NUE. Here, we quantify NUE in four conventionally managed dryland winter-wheat fields in Montana following multiple years of sub-field scale variation in experimental N fertilizer applications. To inform farmer decisions that incorporates NUE, we developed a generalizable model to predict subfield scale NUE by comparing six candidate models, using ecological and biogeochemical data gathered from open-source data repositories and from normal farm operations, including yield and protein monitoring data. While NUE varied across fields and years, efficiency was highest in areas of fields with low N availability from both fertilizer and estimated mineralization of soil organic N (SON). At low levels of applied N, distinct responses among fields suggest distinct capacities to supply non-fertilizer plant-available N, suggesting that mineralization supplies more available N in locations with higher total N, reducing efficiency for any applied rate. Comparing modelling approaches, a random forest regression model of NUE provided predictions with the least error relative to observed NUE. Subfield scale predictive models of NUE can help to optimize efficiency in agronomic systems, maximizing both economic net return and NUE, which provides a valuable approach for optimization of nitrogen fertilizer use.

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

“…PFP = Yield N total applied (10) NutE (Equation ( 11)) quantifies the efficiency with which a plant converts absorbed nitrogen into yield, serving as a direct indicator of yield-related nitrogen use. On the other hand, NIE (Equation ( 12)) and NCE (Equation ( 13)), as defined by Olson et al [37] are specific types of NutE.…”

“…For example, they have been used for evaluating biomass [49], PNC [50], yield [30], and Plant Nitrogen Uptake (PNU) [51]. Additionally, these sensors are instrumental in measuring key NUE indicators such as the NNI [52], the PFP [46], and the NUtE [10]. Therefore, MS sensors offer a comprehensive toolkit for assessing a wide range of variables that contribute to a more holistic understanding of NUE traits.…”

“…This necessitates advanced analysis methods, often incorporating machine learning and specialized software, to handle the data's high dimensionality [53,54]. As analytical techniques for HSI data have advanced, there has been a surge in studies using HSI with UAVs for assessing plant NUE, particularly in the recent years of 2022 and 2023, examining indicators like NNI [55,56], NUtE [10,37], and NBI [57].…”

“…To date, several methods have been employed for plant NUE assessment, including the system nitrogen balances methods [3], calculated by comparing the difference between nitrogen inputs and nitrogen outputs [4]; soil-based methods which focus on the rate of soil nitrogen mineralization, nitrification, and denitrification [5]; plant-based methods which involve the nitrogen content collected and analyzed from plant tissues [6]; and isotope-labeled method which could track the fate and movement of nitrogen in the soil-plant system [7]. In addition, with the development of remote sensing technologies, research is increasingly using images taken by satellites and drones to monitor arable crops [8,9] and estimate NUE over large area cropping systems [10] for a more comprehensive understanding of NUE in a spatially explicit manner.…”