Global Sensitivity Analysis of Key Parameters in the APSIMX-Sugarcane Model to Evaluate Nitrate Balance via Treed Gaussian Process

Abstract

Difficulties in direct monitoring of nitrate balance in agricultural fields reveal the importance of modeling and quantifying the affecting parameters on nitrate balance. We constructed meta-models for APSIMX-Sugarcane using the treed gaussian process and conducted a global sensitivity analysis for nitrate uptake and leaching under three conditions: (1) bare land (BL) to examine the influence of soil hydraulic characteristics, (2) N-free treatment under radiation use efficiency (RUE) ranges (i) 1.2–1.8 [N-free(a)] and (ii) 1.8–2.5 [N-free(b)], and (3) urea conditions to examine the influence of plant growth. Generated meta-models showed good accuracy (for all conditions: R2 > 0.70; NRMSE < 16%; AI > 0.90). The most influential parameters (sensitivity indices ≥ 0.02) were as follows: for leached NO3−N in BL: the parameter rerated to saturated flow-proportion of water between saturation and field capacity (SWCON) of all soil layers; for NO3− uptake and leached NO3−N in N-free(a) and urea: RUE of the phenological stage (PS) 3 (RUE3) and 4, tt_emerg_to_begcane, green_leaf_no, and y_n_conc_crit_leaf of PS 4 (NCL4); in N-free(b): RUE3, NCL4, and SWCON of soil layers 0–15 cm; 15–30 cm, which confirmed that influential parameters were depended on N-stress. The outcomes of this study are useful for enhancing the accuracy and efficiency of crop modeling.

Sustainable intensification with irrigation raises farm profit despite climate emergency

“…The aim of this work was not to examine crop responses to N application per se but rather to compare whole‐farm intensification through both increased N application per unit area and increased farm area under irrigation with that of the Baseline scenario. Readers are directed to other works (Bilotto et al, 2021; Mielenz et al, 2016; Rathnappriya et al, 2022; Robertson & Lilley, 2016) for sensitivity effects of nitrogen on crop growth.…”

“…T A B L E 2 Details of crop type, sowing date, irrigated farm area and level of nitrogen fertiliser input for 16 scenarios combining four wholefarm adaptations (Baseline, Diversified, Intensified and Simplified) and four irrigation infrastructure types (Gravity, Pipe & Riser, Pivot and Drip) (adapted from Monjardino et al, 2022, and Whole Readers are directed to other works (Bilotto et al, 2021;Mielenz et al, 2016;Rathnappriya et al, 2022;Robertson & Lilley, 2016) for sensitivity effects of nitrogen on crop growth.…”

Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML

“…TinyML-based green AI addresses key challenges in precision agriculture by providing better tuning of environmental parameters [70], reducing resource consumption [71], improving crop yield and quality [72], and promoting sustainable development. As an example, the TinyML intelligent control system outperforms traditional models in maintaining temperature and humidity balance [73], reducing system response time and resource consumption [74], achieving smarter and more efficient food production [75], reducing energy waste and environmental pollution [76], and thus protecting the environment and mitigating climate change [17].…”