## 1 Introduction
[PERSON] applied the DEA approach to improve energy efficiency and reduce greenhouse gas emissions from rice production [26], and they proposed a non-parametric method of DEA and MOGA (multi-objective genetic algorithm) to estimate the energy efficiency of wheat farmers and the reduction in greenhouse gas emissions in the Ahvaz county of Iran [27]. [PERSON] used the DEA approach by considering two models, namely BCC and CCR models, with 15 decision-making units and the research results show that energy budgeting and optimization were carried out for rice-wheat-green gram cultivation in three different tillage systems, viz. conservation tillage, reduced tillage, and zero tillage, along with five different residue and fertilizer doses. The DEA method is not only used in agriculture, but also used to evaluate the utilization rate of medical and other social resources. For example, [PERSON] used the DEA method to evaluate the average technical efficiency assessment in public hospitals during and following the COVID-19 pandemic [28]. [PERSON], [PERSON] et al. developed a novel integrated generalized DEA method to evaluate hospitals that provided stroke care services [29], and [PERSON] used DEA to evaluate the dynamic performance assessment of hospitals [30]. [PERSON] used the DEA method to study the spatio-temporal characteristics of the intensive use of cultivated land (CLIU), carbon emission efficiency (CEE), and carbon emissions from cultivated land (CLCE) in the Yellow River basin of China, as well as the spatial correlation between CLIU and CEE [31]. [PERSON] et al. optimized the energy required by orange producers and greenhouse gas emissions using DEA [32]. Some performance evaluation methods combine DEA with other methods, such as the joint use of the life cycle assessment (LCA) and DEA, known as the LCA + DEA methodology, which is an expanding area of research in this quest. [PERSON], [PERSON] et al. studied the potential greenhouse gas emission reductions in soybean farming [33] and the benchmarking of environmental impacts in rice paddy production [34]. [PERSON], [PERSON] et al. used this method to improve the energy efficiency of barley production [35]. [PERSON] used the LCA/time-series DEA method to evaluate the eco-efficiency of complex forestry enterprises [36]. [PERSON], [PERSON] et al. proposed a novel machine learning approach that combined the K-means algorithm with optimization models for eco-efficiency evaluation [37]; at the same time, [PERSON], [PERSON] et al. compared the machine learning tools and algorithms of another new artificial intelligence method, and used this method to evaluate eco-efficiency [38]. For maize yield, [PERSON] analyzed the production efficiency of organic agriculture in Spain using the two-stage DEA model and found that the low efficiency of the organic production model stems from insufficient output [39]. [PERSON] and [PERSON] used the DEA model to evaluate the technical efficiency and maize production efficiency of planting in different agro-ecological areas of Zimbabwe and Ghana [40; 41]. [PERSON] and [PERSON] employed the DEA model to evaluate the technical efficiency, the economies of scale, and cost-effectiveness of small-scale corn production in Nigerian traditional planted maize and hybrid corn [42; 43]. [PERSON] used an improved DEA model (the EMPEA model) to evaluate the resource utilization efficiency and sustainability of the corn production system in northern Ghana [44]. In some studies, the DEA model was combined with other methods to conduct the evaluation [45; 46]. Nevertheless, these studies used DEA methods to evaluate the economic or technical efficiency of maize planting in a certain area, but they did not evaluate the performance of cultivation measures, which could guide farmers to achieve maximum efficiency. Orthogonal experiments and statistical analysis are often used to determine the optimal cultivation measures [47; 48].