Towards Smart Agricultural Water–Nitrogen Management: A Multi-Criteria Decision Framework for Rapeseed Production in Southwest China Using EWM-TOPSIS Model

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Open Access Article

by
Run Xue

1,2,
Yue Jiang

2,*,
Hong Li

1,
Imran Ali Lakhiar

1,3 and
Junjun Ran

4

1

Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China

2

School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

3

Faculty of Agricultural Engineering and Technology, Sindh Agriculture University, Tandojam 70060, Pakistan

4

College of Water Resource and Hydropower, Sichuan University, Chengdu 650065, China

Author to whom correspondence should be addressed.

Submission received: 15 July 2026 / Revised: 7 August 2026 / Accepted: 10 August 2026 / Published: 12 August 2026

Abstract

To address the limited synergy between integrated water–fertilizer technologies and intelligent application equipment, as well as the low water–nitrogen use efficiency in rapeseed production systems in southwestern China—which together constrain the large-scale adoption of smart fertigation equipment—this study conducted a two-season field experiment using an integrated water–fertilizer application system. The experiment included two irrigation regimes (W1: 60% ET c; W2: 100% ET c) and three nitrogen rates (F1: 220, F2: 300, F3: 360 kg N ha−1), plus a rainfed control (CK), to quantify rapeseed responses to water–nitrogen interactions under an equipment-based fertigation framework. Results showed that water–nitrogen interactions significantly regulated rapeseed physiological processes, growth, yield formation, and resource-use efficiency. Compared with CK, appropriate water and nitrogen supply markedly enhanced PSII efficiency and overall energy conversion. In particular, W2F2 increased leaf photosynthetic rate by 51.2% and 50.8% across the two seasons. Water–nitrogen coupling also improved yield components such as branch number and thousand-seed weight, thereby increasing final yield. Although nitrogen partial factor productivity declined with increasing N rates, smart fertigation improved economic returns to varying degrees. EWM-TOPSIS results indicated that W2F2 consistently achieved the highest comprehensive performance across both seasons. By balancing yield, seed quality, resource efficiency, and economic benefit, this treatment represents the optimal strategy under intelligent fertigation systems. Overall, this study provides a decision-oriented optimization framework based on crop physiological responses under smart water–fertilizer equipment, offering practical guidance for intelligent fertigation deployment in rapeseed systems in southwestern China.

1. Introduction

Rapeseed (Brassica napus L.) is an economically important global crop valued for edible oil production and multiple-purpose utilization [1,2,3]. In 2023, the global rapeseed planting area reached 43.45 million hectares. As the world’s second-largest producer, China contributes 17.96% of global rapeseed output [4]. Winter rapeseed is a major cash crop in southwest China [5,6]. Nevertheless, its long growth cycle and high water requirement conflict with frequent seasonal drought in this region. The seasonal precipitation of approximately 200 mm during the rapeseed growing period cannot meet the crop water demand of 300–500 mm [5]. Drought stress thus severely restricts rapeseed yield, and supplementary irrigation is indispensable for local rapeseed production. Previous studies have demonstrated that over-winter supplementary irrigation improves the soil hydrothermal conditions and winter survival of rapeseed and mitigates spring drought damage [7,8,9]. However, rapeseed is sensitive to both water shortage and surplus. Water deficit inhibits root activity and nitrogen acquisition, whereas excessive irrigation degrades soil aeration, lowers nitrogen-use efficiency and triggers nitrogen leaching-related environmental risks. Accordingly, rational supplementary irrigation is critical to balance rapeseed water demand and seasonal drought in Southwest China.

Nitrogen serves as another essential nutrient for winter rapeseed. Optimal nitrogen supply facilitates dry-matter accumulation, photosynthetic performance, yield and quality formation [10]. Nevertheless, over-nitrogen application is widespread in local farming practice. Such fertilization patterns reduce nitrogen-use efficiency, raise nitrate leaching risks to groundwater, and increase lodging susceptibility, which eventually causes yield and quality losses [11,12,13]. In traditional winter rapeseed production in southwestern China, single-dimensional nitrogen or irrigation management cannot simultaneously achieve high yield, superior quality, high resource efficiency and environmental sustainability [14]. Driven by advances in smart agricultural hardware and precision water–fertilizer technologies, intelligent irrigation and variable-rate fertigation platforms offer promising solutions to improve water–nitrogen use efficiency for rainfed croplands [15,16]. Such systems support timely, quantitative and site-specific water and nutrient supply, greatly departing from traditional extensive field management [17,18]. Nevertheless, large-scale field deployment of these smart devices is still constrained by heterogeneous field conditions and the shortage of field-validated water–nitrogen regulation strategies. The mismatch between theoretical optimization and practical field performance further limits their popularization [19,20]. Accordingly, it is necessary to consider the practical field adaptability of integrated smart fertigation equipment, and clarify rapeseed physiological, yield-quality and resource-use responses under equipment-driven water–nitrogen combinations, so as to develop practical precision management strategies.

Preite et al. [21] integrated soil and environmental sensor data with short-term weather conditions to develop an intelligent irrigation management system, enabling automated and intelligent irrigation regulation. Chen et al. [22] combined the DSSAT crop model with distributed reinforcement learning techniques to establish a multi-source information-driven precision intelligent irrigation regulation system, which significantly improved cotton irrigation water-use efficiency and crop yield. However, existing intelligent irrigation and fertigation systems mainly rely on external information, such as soil moisture and meteorological conditions, for water–nitrogen regulation, while lacking water–nitrogen management strategies driven by crop physiological feedback. Under complex field conditions, environmental parameter-based water–nitrogen optimization strategies cannot fully capture real-time crop requirements, thereby limiting the precision application and broader adoption of intelligent irrigation and fertigation technologies. Nowadays, advanced crop intelligent monitoring technologies enable real-time acquisition of crop physiological status, providing reliable information for determining water and fertilizer input requirements in fertigation systems [23,24]. Among these physiological indicators, chlorophyll fluorescence parameters reflect photosystem activity and stress levels, and can sensitively characterize the damage of abiotic stress to photosynthetic apparatus [25,26]. Gas exchange parameters, such as photosynthetic rate, directly represent plant carbon assimilation capacity and growth status, serving as important physiological feedback signals for precision water–nitrogen decision-making [27,28]. Nevertheless, multiple dimensions, including physiological responses, yield, quality, and resource-use efficiency, need to be comprehensively considered to identify optimal water–nitrogen management strategies rather than relying on single-indicator evaluation.

The entropy weight-TOPSIS (EWM-TOPSIS) is an objective multi-criteria decision-making tool that mitigates information redundancy and subjective weighting bias [29,30]. In this framework, indicator weights are objectively quantified via entropy weighting according to experimental data variation, followed by TOPSIS calculation of distances to positive- and negative-ideal solutions to obtain relative closeness coefficients for treatment ranking. Widely adopted in crop water–fertilizer assessment, this method can reliably distinguish the comprehensive performance of different agronomic treatments [31,32]. Therefore, the objectives of this study are (1) to analyze how irrigation–nitrogen regimes regulated by integrated smart fertigation equipment affect rapeseed growth, chlorophyll fluorescence and seed quality, and to reveal the regulatory mechanism of equipment-mediated water–nitrogen interaction on crop growth and quality formation; (2) to evaluate water–nitrogen use efficiency as well as economic input–output performance across different water–fertilizer coupling treatments, and quantify resource-use and economic responses under equipment-based management; and (3) to establish an EWM-TOPSIS-based multi-criteria optimization framework for winter rapeseed under smart fertigation, and determine the optimal management strategy for green-efficient production in southwestern China. This work provides theoretical support and decision-making references for parameter tuning and large-scale application of intelligent irrigation and variable fertilization equipment in rapeseed planting.

2. Materials and Methods

2.1. Experimental Site

The field experiment was conducted over two consecutive winter rapeseed growing seasons from October 2024 to May 2026 at Wufangpo Village, Daying County, Sichuan Province, China (105°08′ E, 30°34′ N; 377 m above sea level). The experimental site is located in the central Sichuan Basin and is characterized by a subtropical humid monsoon climate, with a mean annual air temperature of 16.5 °C, annual precipitation of approximately 930 mm, annual sunshine duration of 1380 h, and a frost-free period of approximately 279 days. The experimental soil was classified as a silt loam according to the USDA soil texture classification, with a sand:silt:clay ratio of 34:58:8. Prior to the experiment, composite soil samples were collected from the 0–20 cm soil layer to determine the initial physicochemical properties. The soil had a bulk density of 1.37 g cm−3, a field water-holding capacity of 0.42 cm 3 cm−3, and a permanent wilting point of 0.19 cm 3 cm−3. The soil pH was 6.8, with a soil organic matter content of 21.1 g kg−1, total nitrogen of 0.91 g kg−1, available phosphorus of 8.71 mg kg−1, and available potassium of 147.8 mg kg−1.

Figure 1 presents the long-term (1974–2023) monthly climatic characteristics of the experimental site based on data obtained from the National Meteorological Data Service Center, including monthly maximum (T max), mean (T avg), and minimum (T min) air temperatures, average relative humidity (RH avg), sunshine duration (S d), and precipitation (rain). The lowest temperatures occurred in January, while most precipitation was concentrated in April and May. In contrast, cumulative precipitation during the winter rapeseed growing period from November to March was only 101.6 mm, which is insufficient to satisfy the crop water requirement without supplemental irrigation. The daily variations in precipitation, maximum, mean, and minimum air temperatures, and relative humidity during the two experimental seasons are presented in Figure 2. Total precipitation during the winter rapeseed growing period was 187.6 mm and 245.4 mm in the first and second seasons, respectively. The corresponding mean daily air temperatures were 11.1 °C and 12.4 °C, while the average relative humidities were 89.9% and 88.5%, respectively. The cumulative solar radiation reached respective values of 1295.7 and 1285.1 MJ m−2 during the two growing seasons.

Figure 1. Meteorological data from the experimental site over the past 50 years.

Figure 2. Meteorological data from the experimental site in the S1 (a) and S2 (b) seasons.

2.2. Experimental Design

The experimental crop management and basal fertilization practices were consistent with local farmers’ conventional methods. The rapeseed cultivar used in this study was the hybrid variety “Xianyou 188”. Seedlings were transplanted manually on 6 November 2024, and 4 November 2025, with corresponding harvest dates on 6 May 2025, and 2 May 2026. The experiment included two irrigation levels (W1: low irrigation, 60% ET c; W2: high irrigation, 100% ET c) and three nitrogen application rates (F1: 220 kg N ha−1; F2: 300 kg N ha−1; F3: 360 kg N ha−1), resulting in six water–nitrogen coupling treatments. In addition, a rainfed control without nitrogen fertilization (CK) was included, giving a total of seven treatments. Each treatment was replicated three times, resulting in 21 plots arranged in a randomized complete block design. The planting density of rapeseed is 83,333 plants per hectare, with uniform planting density across all treatments. Before transplanting, a uniform basal application of rapeseed-specific compound fertilizer (N:P 2 O 5:K 2 O = 25:10:10, total nutrients ≥ 40%) was applied at 720 kg ha−1, corresponding to 180 kg N ha−1. This was used to ensure sufficient nutrient availability during early seedling establishment and to promote vigorous root and shoot development. During the overwintering and bolting stages, irrigation and topdressing nitrogen were applied at a ratio of 4:6. The total topdressing nitrogen amounts for N1, N2, and N3 were 60, 120, and 180 kg N ha−1, respectively. All irrigation events were conducted using a sprinkler system under non-rainy conditions with wind speed below 3 m s−1 to minimize meteorological interference. Other field management practices, including pest control and weed management, were carried out uniformly following local conventional agronomic practices. The intelligent fertigation system used in this study was independently developed and manufactured by Jiangsu University. The system mainly consisted of a fertigation controller, fertilizer mixing tank, pipeline system, and intelligent control unit. The sensing module was integrated with multiple sensors to enable real-time acquisition of environmental information, including soil moisture content and meteorological conditions. These data were used to calculate crop evapotranspiration (ET c) and determine irrigation water requirements.

2.3. Physiological Indicators

The chlorophyll fluorometer (ChlF) was monitored using a portable photosynthesis system (MultispeQ V2.0, PHOTOSYNQ INC, East Lansing, MI, USA). Saturation pulse method (PAM) technology was used to measure ChlF parameters, while simultaneously recording leaf temperature, photosynthetically active radiation (PAR), leaf thickness, SPAD, and environmental temperature and humidity in the field. The ChlF contains the maximum quantum yield of PSII (Fv/Fm), the actual quantum yield of PSII (Φ PSII), the relative photosynthetic electron transfer rate (ETR), the photochemical quenching (qP), and the non-photochemical quenching (NPQ). The Fv/Fm and Φ PSII represents the maximum and actual proportion of energy absorbed by the crop that can be used for photosynthetic processes, respectively. In the condition without any stress, the plant’s Fv/Fm is generally 0.84, which means that 84% of the energy is converted to the photosynthetic process. The qL represents the proportion of open-state photosystem II reaction centers and it reflects the level of photosynthetic activity. The NPQ represents the proportion of energy absorbed by photosystem II that is used to dissipate as heat, and the ability of plants to dissipate excess light energy as heat. When measuring leaf physiological parameters at different growth stages of rapeseed, representative functional leaves were selected. During the seedling stage, the second and third fully expanded true leaves at the top were selected; during the bolting stage, the third and fourth functional leaves below the main stem apex were selected; during the flowering stage, the first and second fully expanded leaves below the main inflorescence were selected; and during the silique development stage, the upper functional leaves adjacent to the siliques were measured. All measurements were conducted on sunny days between 9:00 and 11:00 a.m. and at night under no sunlight. For each growth stage, three plants were randomly selected from different treatments and arranged in a diagonal pattern to measure leaf ChlF parameters, avoiding leaves affected by disease, damage, or shading to ensure that the data accurately reflected the true photosynthetic physiological dynamics of the rapeseed. The quantum yield of regulated energy dissipation in photosystem II (Φ NPQ) represents the fraction of excitation energy absorbed by PSII that is dissipated as heat through regulated photoprotective mechanisms. The quantum yield of non-regulated energy dissipation in photosystem II (Φ NO) represents the fraction of excitation energy absorbed by PSII that is passively dissipated as heat and re-emitted