







































Ecology, Economy, and Society–the INSEE Journal 8(2): 67-96, July 2025 

 

RESEARCH PAPER 

Eco-efficiency of Crop Production in Madhya 
Pradesh: A Directional Distance Function Approach  
Nihal Singh Khangar and Mohanasundari Thangavel 

Abstract: The growing global population presents a dual challenge: increasing crop 
production while minimizing environmental impact. To overcome this challenge, 
the eco-efficiency of crops must be improved. Measuring eco-efficiency—defined 
as the ratio of desirable output (crop production) and undesirable output 
(environmental degradation) to resource use (inputs)—is crucial for sustainable 
agriculture. This study assesses the eco-efficiency of crop production in Madhya 
Pradesh, India, using life cycle assessment (LCA) and data envelopment analysis–
directional distance function (DEA–DDF). We obtained the input data for crops 
from multiple packages of practices from government sources for the 2021–2022 
agricultural year. LCA quantified the environmental impact of crop production, 
while DEA–DDF evaluated efficiency by considering both economic output and 
environmental degradation. Our results indicate that rainfed wheat, maize, 
sorghum, and soybean exhibit production inefficiencies, with an average 
inefficiency of 0.22, suggesting a 22% potential for improvement. Inefficient 
decision-making units can enhance efficiency by optimizing input use, reducing 
environmental degradation, and increasing crop and residue output. The study also 
determines target values for input reduction and output improvement to guide 
sustainable agriculture. It helps optimise crop eco-efficiency by emphasizing 
resource-efficient and environmentally sustainable agricultural practices, thereby 
supporting long-term food security. 

Keywords: Environmental Impact, Eco-efficiency, Data Envelopment Analysis, 
Directional Distance Function, Agricultural Impact 

 

 

 

 

 Research Scholar, School of Humanities and Social Sciences, Indian Institute of 
Technology Indore, Madhya Pradesh, 453552; Phd2101261008@iiti.ac.in  

 Assistant Professor, School of Humanities and Social Sciences, Indian Institute of 

Technology Indore, Madhya Pradesh, 453552; mohana@iiti.ac.in  

Copyright © Khangar and Thangavel 2025. Released under Creative Commons Attribution 
© NonCommercial 4.0 International licence (CC BY-NC 4.0) by the authors.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581–6152 (print); 2581–6101 (web).  

DOI: https://doi.org/10.37773/ees.v8i2.1429  

mailto:Phd2101261008@iiti.ac.in
mailto:mohana@iiti.ac.in
https://doi.org/10.37773/ees.v8i2.1429


Ecology, Economy and Society–the INSEE Journal [68] 

 

1. INTRODUCTION 

Agriculture is a significant source of global greenhouse gas (GHG) 
emissions, contributing substantially to climate change (Khangar and 
Mohanasundari 2025; Jimmy et al. 2017; Lynch et al. 2021), which, in turn, 
affects agricultural productivity (Adams et al. 1998; Taki et al. 2018). Since 
2000, GHG emissions from global agrifood systems have increased by 9%, 

reaching 16 Gt CO₂ eq in 2020 (FAO 2022). Numerous studies have 
highlighted that agricultural practices, soil management, irrigation, and 
fertilizer use significantly influence emissions from crop production (Aryal 
et al. 2015; Kumar et al. 2021; Safa and Samarasinghe 2012; Syp et al. 2015; 
Vetter et al. 2017; Taki et al. 2018; Tayefeh et al. 2018). These emissions 
exacerbate environmental degradation (Khangar and Mohanasundari 2023, 
2024; Nayak et al. 2023), posing challenges to achieving sustainability and 
food security. Agriculture not only contributes to climate change but is also 
adversely affected by it, as rising temperatures and extreme weather 
conditions reduce crop yields (Arora 2019; Kumar et al. 2024; Kumar, Ray, 
and Mohanasundari 2024; Malhi et al. 2021). This cyclical relationship 
underscores the urgent need for sustainable agricultural practices. 

Given the growing global population, the challenge is to increase food 
production while minimizing environmental harm. This calls for the 
development of innovative production practices that optimize resource use. 
Enhancing crop eco-efficiency, which refers to producing more agricultural 
output with fewer inputs (such as water, energy, and fertilizers) while 
reducing environmental impacts, is crucial to achieving sustainability. Yet, 
farmers, especially in developing countries, face hurdles such as high input 
costs, low yields, land degradation, and inefficiencies in production, 
necessitating policy discussions on agricultural subsidies and sustainability 
measures (Manogna and Mishra 2020; Yadava 2017). 

This study assesses the production–environmental efficiency of major crops 
in Madhya Pradesh using data envelopment analysis (DEA), a widely used 
tool for measuring eco-efficiency. The study extends the concept of 
technical efficiency by incorporating environmental impacts. While DEA 
traditionally measures technical efficiency, this study uses data envelopment 
analysis–directional distance function (DEA-DDF), which allows for the 
inclusion of undesirable outputs (e.g., emissions) and provides a broader 
measure of eco-efficiency in crop production. The study aims to provide 
insights into how different crop production systems vary in efficiency, 
thereby helping policymakers and stakeholders develop sustainable farming 
strategies. One of the core goals of this study is to contribute to the global 
movement toward sustainable agriculture by informing decision-making 



[69] Khangar and Thangavel 

 

with evidence-based analysis. Notably, this study makes a fundamental 
contribution to achieving the United Nations Sustainable Development 
Goals (SDGs), particularly goals 2 (zero hunger), 12 (responsible 
consumption and production), and 13 (climate action). 

This paper begins with a review of the environmental impact of agriculture 
and methods for measuring eco-efficiency, followed by a discussion of the 
empirical approach. The results highlight variations in eco-efficiency across 
crops, emphasizing the need for targeted interventions. The study 
concludes with policy recommendations, stressing the role of DEA in 
guiding sustainable agricultural practices. By promoting resource-efficient 
farming, this study contributes to the broader discourse on sustainable 
agriculture and environmental conservation. Through this research, we aim 
to contribute to the ongoing debate on sustainable agriculture and promote 
a more resilient and environmentally conscious approach to food 
production. 

 

2. LITERATURE REVIEW 

The literature review is structured into two main sections. The first section 
presents a methodological review of various eco-efficiency assessment 
methods, evaluating their theoretical foundations and practical applications. 
The second section examines past studies that have employed eco-
efficiency and similar approaches to identify research gaps both in terms of 
methodology and the existing literature. 

2.1. Methodological Review 

DEA is a mathematical programming approach used to measure the relative 
efficiency of decision-making units (DMUs) by assessing the ratio of 
weighted outputs to weighted inputs (Allen et al. 1997; Iribarren et al. 2015). 
It eliminates subjectivity in eco-efficiency measurement (Masternak-Janus 
and Rybaczewska-Błażejowska 2017; Picazo-Tadeo et al. 2011; Sanjuan et al. 
2011) and is recognized as an effective means of reducing environmental 
pressures. It is also widely accepted in policymaking (Kuosmanen and 
Kortelainen 2005). Developed initially to estimate firm efficiency (Cooper et 
al. 2011), DEA has been applied widely in agriculture to assess sustainability 
and efficiency (Toma et al. 2015), in conjunction with life cycle assessment 
(LCA). 

In agricultural studies, emissions are often treated as inputs due to their 
controllable nature, following the arbitrary treatment of undesirable outputs 
by Dyckhoff and Allen (2001). While some researchers follow this approach 



Ecology, Economy and Society–the INSEE Journal [70] 

 

(Korhonen and Luptacik 2004; Zhang et al. 2008), Seiford and Zhu (2002) 
argue that it does not accurately represent the production process. The 
DDF in the DEA approach addresses this issue by considering undesirable 
outputs separately (Chung et al. 1997; Färe and Grosskopf 2004).  

DEA has evolved to incorporate stochastic models and concepts of weak 
or marginal disposability (Amirteimoori et al. 2023). Hua and Bian (2007) 
identify six methods to handle undesirable factors: ignoring them, treating 
them as inputs, using nonlinear models (Färe et al. 1989), applying nonlinear 
monotone transformations and linear monotone transformations (Seiford 
and Zhu 2002), and lastly, employing the DDF approach. A challenge in 
eco-efficiency measurement is assigning weights to emissions, as default 
weight assignments may not be ideal (Tyteca 1996). To address this, 
Kuosmanen and Kortelainen (2005) employ DEA with constrained linear 
programming to assign soft weights. 

2.2. Review of Past Studies  

In agriculture, eco-efficiency is well substantiated, encompassing various 
crops, techniques, and horticulture (Picazo-Tadeo et al. 2011). Numerous 
studies have investigated the eco-efficiency of agricultural crop production 
using DEA (Cao et al. 2022; Coluccia et al. 2020; Huang et al. 2018) and 
stochastic frontier analysis (SFA) (Orea and Wall 2016; Song and Chen 
2019). However, these studies explore eco-efficiency in agriculture only at 
the farm or crop levels, within specific geographic regions, and are thus 
limited in scope. Mohammadi et al. (2015) combined LCA and DEA to 
benchmark the environmental impacts of rice production by applying the 
constant returns to scale (CRS) model with a focus on reducing the input 
(environmental harm) at constant production yield.  

In a comprehensive examination of eco-efficiency in rice production in 
China, Huang et al. (2022) integrated LCA and DEA, arriving at a combined 
eco-efficiency value of 0.51. In contrast, Aslam et al. (2021) reported eco-
efficiency values of 0.88 and 0.90 for wheat and rice, respectively, in India, 
thus demonstrating a higher eco-efficiency compared to China. Pishgar-
Komleh et al. (2020) added to this discourse by estimating a wheat 
production efficiency of 0.43 ± 0.23 in Poland. Notably, they argue that 
enhancing the performance of inefficient farms could lead to a remarkable 
57% reduction in resource usage.  

Adding to the regional perspective, Ding et al. (2024) investigated the eco-
efficiency of grains in China, revealing values of 0.67 at CRS and 0.71 at 
variable returns to scale (VRS). Numerous studies, such as those conducted 
by Bagheri et al. (2020), Chaloob et al. (2018), Fusco et al. (2023), and Hsu et 



[71] Khangar and Thangavel 

 

al. (2023), also examine the eco-efficiency of aggregate agricultural 
production across different regions. Evidencing results obtained using 
typical DEA models—such as the Charnes, Cooper, and Rhodes (CCR) 
model, based on CRS, and the Banker, Charnes, and Cooper (BCC) model, 
based on VRS (Kyrgiakos et al. 2023)—Fusco et al. (2023) measured the 
eco-efficiency of agricultural produce, combining it with soft weightages for 
emissions, highlighting significant territorial disparities in eco-efficiency 
across Italy. Northern regions in Italy demonstrated higher eco-efficiency, 
while southern regions showed greater scope for improvement. 

We find that most studies in the agricultural context a) use a soft weightage 
system for emissions, considering them as an input and applying a classic 
DEA approach; b) consider only GHGs as environmental emissions; or c) 
use the DEA–DDF approach. Therefore, in this study, we combine the 
DEA with restricted weights for emissions and consider them as 
undesirable output. We use the analytical hierarchy process (AHP) to assign 
subjective weightage to emissions while distributing the weights. In this 
way, this study can demonstrate a more efficient production process and 
improved efficiency scores. Further, we integrate economic outcomes and 
environmental impacts assessed using the LCA into an eco-efficiency (EE) 
ratio. This ratio is determined using a weight estimation model based on 
DEA, which provides distinct weights for each emission category using 
AHP, considering the vast category of impacts.  

Based on the identified literature gap, this study aims to measure the eco-
efficiency of major crops, including irrigated wheat, rainfed wheat, direct-
seeded rice, maize, sorghum, millet, and soybeans, in Madhya Pradesh, 
India. This study investigates which crop is the most eco-efficient in the 
state and what should be the target to make all the crops sustainable and 
eco-efficient. 

 

3. DATA AND METHODOLOGY 

This section outlines the regional input–output data sources and the 
methodological framework used to assess agricultural eco-efficiency. It 
details the analytical approaches employed to evaluate environmental 
emissions using LCA, measure efficiency performance using DEA–DDF, 
and assign subjective weights to emissions and inputs. 

3.1. Data Sources 

We collected the input data for the crops from multiple packages of 
practices (PoPs) from government sources, as shown in Table 1. 

 



Ecology, Economy and Society–the INSEE Journal [72] 

 

 

Table 1: Life Cycle Production Inventory Data Sources for Crops 

Crop Production input data source 

Wheat 
Package of Practice, Farmer Welfare and Agriculture Development 
Department, Madhya Pradesh (n.d.a)  

Rice 
Package of Practice, Farmer Welfare and Agriculture Development 
Department, Madhya Pradesh (n.d.b)  

Maize 
Package of Practice, Farmer Welfare and Agriculture Development 
Department, Madhya Pradesh (n.d.c)  

Soybean Package of Practice, National Food Security Mission (n.d.) 

Sorghum 
Indian Council of Agricultural Research –Indian Institute of Millets 
Research (ICAR-IIMR) (n.d.a) 

Millet ICAR–IIMR (n.d.b) 

Source: Authors’ compilation 

Residue is also an output of agricultural practices. Therefore, in this study, 
DEA considers agricultural residue as an output. It further assumes that the 
residue is untreated and, therefore, not included in the LCA of crops. Since 
residue data are not readily available, we calculated the amount of residue 
by adopting the cool farm tool (CFT) model, which is represented by the 
following equation: 

Q
residue
FU  =  Ra + Rb 

where Q
residue
FU  is the quantity of residue per functional unit (e.g., ha-1), R 

represents the residue, with subscript “a” denoting above-ground residues 
and “b” denoting below-ground residues. The above-ground residue (Ra) 
was calculated as follows: 

Ra = YCrop * DCrop *[
DCrop

YCrop
] 

where “Y” represents crop yield, “D” refers to the dry matter fraction of 

the crop, and 
DCrop

YCrop
 is the ratio of above-ground residue dry matter to the 

harvested yield of the respective crop.  

Residue below ground was assessed by the given equation: 

Rb = Ra *[
Rbiomass

Ra
] 

where the Rbiomass refers to the ratio of the below-ground root biomass of 
the crop to the above-ground shoot biomass, which is assumed to be zero 
because the primary focus is on above-ground biomass. This study follows 
IPCC (2019) in calculating the ratio of the above-ground residue dry matter 



[73] Khangar and Thangavel 

 

to the harvested yield for crops, as well as that of the below-ground root 
biomass to the above-ground shoot biomass of crops. The average output 
data of the crops has been obtained from the Indiastat database (n.d.). 

3.2. Empirical Analysis 

The empirical analysis follows a structured approach to assess agricultural 
eco-efficiency. First, we undertook LCA to quantify various environmental 
emissions resulting from agricultural activities. We then assigned subjective 
weights to these emissions to aggregate them into a single composite 
emission score. Similarly, inputs were weighted with equal importance to 
ensure a balanced evaluation. Finally, we performed DEA using these 
processed inputs and outputs to measure the relative efficiency of different 
production units or regions. 

3.2.1 Life cycle assessment  

Figure 1. Cyclical Impact of Climate Change on Agricultural Production 

Source: Authors’ compilation 

We performed LCA for select major crops within the system boundary to 
calculate environmental emissions. We used the ReCiPe 2016 midpoint 
method for impact assessment using the OpenLCA 1.11 software. Figure 1 
represents the LCA goal and system boundary. All the processes, starting 



Ecology, Economy and Society–the INSEE Journal [74] 

 

from land preparation to harvesting, were included in the LCA. We then 
performed a sensitivity analysis using a Monte Carlo simulation of 1,000 
iterations to obtain the most reliable result. 

3.2.2 Data envelopment analysis 

Due to the limitation of using single-time-frame crop data, we employed 
DEA for efficiency measurement. Additionally, we also used DEA–DDF, 
as it allows for the incorporation of undesirable outputs, such as emissions, 
and provides a more comprehensive assessment of eco-efficiency. The 
analysis was conducted using RStudio, leveraging relevant statistical and 
econometric packages for DEA–DDF. The methods also required a 
weighting process. Since the process entailed some subjectivity, the weights 
assigned do not match stakeholders’ values.  

We measured the EE ratio using a single emission index comprising various 
categories of impacts on the environment. The numerator was the 
contribution of crops to the economy and the environment, and the 
denominator was the inputs required to produce crops. To assign weights 
to environmental impacts, we adopted the AHP method (discussed further), 
following Zhu’s (1996) approach for textile manufacturing. The weighting 
process for various impact categories is described below.  

This study adopted a general approach to calculating eco-efficiency coined 
by Färe and Grosskopf (2004), wherein the production output set can be 
written as: 

𝑃(𝐼) =

{(𝐼𝑤, 𝑂𝑑 , 𝐸𝐷)∑ 𝛼𝑗𝐼𝑗
𝑤

𝑛

𝑗=1
≤ 𝐼𝑤 ,∑ 𝛼𝑗𝑂𝑗

𝑑
𝑛

𝑗=1
≥ 0𝑑 ,∑ 𝛼𝑗𝐸𝐷𝑗

𝑛

𝑗=1
= 𝐸𝐷, 

𝛼𝑗 ≥ 0, 𝑗 = 1,2,3…𝑛}  

here, P(I) represents the production possibility, α is the slack weights of 
input, desirable output, and undesirable output. Null jointness is imposed 
via the following restriction on the undesirable outputs: EDj > 0, (j =1, 2..., 
n).  

EE is written as follows: 

∑𝛼𝐽𝑂𝑗
𝑑 + ∑𝛼𝑗𝐸𝐷𝑗

∑𝐼𝑗
𝑤  

where ED = ∑ 𝑤𝑖𝑗𝑚𝑖𝑗

𝑛

𝑗=1
. Here, 𝑚𝑖𝑗 is the midpoint impact, and 𝑤𝑖𝑗 is 

the subjective weight for the respective impact of the jth DMU (jth crop). Od 



[75] Khangar and Thangavel 

 

consists of two desirable outputs: Oj
1 and Oj

2 are output 1 (crop yield) and 

output 2 (crop residue) of the jth DMU, respectively. ∑𝐼𝑗
𝑤 is the weighted 

input of the jth DMU, typically denoted as ∑ 𝑤𝑘𝐼𝑘𝑗

𝑛

𝑗=1
. Further, 𝐼𝑘𝑗 

represents the kth input of the jth DMU. Similarly, 𝑤𝑘 represents the weights 
for the kth input. The detailed process for selecting the subjective weightage 
for the input and ED calculation is described further. 

A directional growth was introduced to the model: 

𝑔 = |

𝑔𝑂11 𝑔𝑂21 −𝑔𝐸𝐷1

𝑔𝑂12 𝑔𝑂22 −𝑔𝐸𝐷2

𝑔𝑂1𝑗 𝑔𝑂2𝑗 −𝑔𝐸𝐷𝑗

| 

where the direction vector 𝑔 represents the desired direction in which we 
want to improve efficiency or move in the output space. Each component 
of the vector represents the weight or importance assigned to a specific 
output. We assumed an increase of 20% and 10% in crop yield and residue, 

respectively, and a 20% decrease in ED, forming a direction {𝑔 = (20%, 
10%, −20%)} for each DMU, respectively. In this way, we assigned 
directions to the desirable outputs while imposing restrictions on the 
undesirable outputs.  

The objective function of the model is:  

�⃗⃗� (𝐼0
𝑤, 𝑂0

𝑑 , 𝐸𝐷0; 𝑔) = Max β 

This objective function can be easily understood as: 

𝑚𝑎𝑥  (∑𝛼𝐽𝑂𝑗
𝑑)𝑚𝑖𝑛  (∑𝛼𝑗𝐸𝐷𝑗)

𝑚𝑖𝑛 (∑𝐼𝑗
𝑤)

 

Subject to: 

∑𝛼𝑗𝐼𝑗
𝑤

𝑛

𝑗=1

≤ 𝐼𝑗0
𝑤

 
 

∑𝛼𝑗𝑂𝑗
𝑑

𝑛

𝑗=1

≥ 𝑂𝑗0
𝑑

 
+ 𝛽𝑔𝑂 

𝑑
        

 

∑𝛼𝑗𝐸𝐷𝑗

𝑛

𝑗=1

= 𝐸𝐷𝑗0 − 𝛽𝑔𝐸𝐷  



Ecology, Economy and Society–the INSEE Journal [76] 

 

∑𝛼𝑗

𝑛

𝑗=1

≥ 0 ≤ 1; ∑𝑤𝑖𝑗

𝑛

𝑗=1

≥ 0 ≤ 1; ∑𝑤𝑘

𝑛

𝑗=1

≥ 0 ≤ 1;  𝑗 = 1,2…𝑛 

As discussed earlier, due to the subjectivity of environmental damage, 
subjective weights were assigned to emissions. The method for assigning 
weight is described in the following section. The various impact categories 
have been aggregated to a single weighted emission (ED), which is 
considered an undesirable output in the study. 

3.2.3 Subjective weights for impacts and inputs 

To create a composite measure of environmental impact, we assigned 
subjective weights to individual emission categories and inputs, which are 
further discussed below. 

a. Single emission index  

In this study, we assigned weights using the AHP developed by Saaty 
(1980), where the emissions hierarchy is constructed based on the relative 
damage potential of the impacts, as adopted from Huijbregts et al. (2016), 
similar to Khangar and Mohanasundari (2023). We performed the AHP 

using a 1515 matrix, where “m” depicts the row and “n” column, as shown 
here: 

|

𝑎11 𝑎12    𝑎13 …… . 𝑎1𝑛

𝑞21 𝑎22   𝑎23 …… . . 𝑎2𝑛

𝑎31 𝑎32   𝑎33 …… . . 𝑎3𝑛

𝑎𝑚1 
𝑎𝑚2      𝑎𝑚3     …… . 𝑎𝑚𝑛 

|  where the sum of each column “n” is 

depicted as 

|

|

𝑥1

𝑥2

𝑥3

.

.

.
𝑥𝑛

|

|

 

The relative value matrix (RVM) is obtained by dividing the individual 
variable by the sum of columns (i.e., X1, X2…Xn). Then, the priority vectors 
are obtained by averaging the RVM row-wise (1): 



[77] Khangar and Thangavel 

 

𝑅𝑉𝑀 =

[
 
 
 
 
 
 
 
 
𝑎11

𝑥1
    

𝑎12

𝑥2
    

𝑎13

𝑥3
 ……  

𝑎1𝑛

𝑥𝑛 
𝑎21

𝑥1
    

𝑎22

𝑥2
    

𝑎23

𝑥3
 ……  

𝑎2𝑛

𝑥𝑛 
𝑎31

𝑥1
    

𝑎32

𝑥2
    

𝑎33

𝑥3
  …… 

𝑎3𝑛

𝑥𝑛 
𝑎𝑚1

𝑥1
    

𝑎𝑚2

𝑥2
    

𝑎𝑚3

𝑥3
…… 

𝑎𝑚𝑛

𝑥𝑛 ]
 
 
 
 
 
 
 
 

 

Row average of “m” 𝑃𝑉 =

|

|

|

𝑃1

𝑃2

𝑃3

.

.

.

.
𝑃𝑚

|

|

|

 ………………….......................... (1) 

The priority vectors function as ambivalent weightage for the respective 
impact categories. To confirm the weightage, we checked the consistency of 
the weights. The weighted sum matrix (WSM) was calculated based on the 
matrix provided in equation (2) to proceed further: 

WSM = 𝑃1 |

𝑎11

𝑎12

𝑎13

𝑎1𝑛

| + 𝑃2 |

𝑎21

𝑎22

𝑎23

𝑎2𝑛

| + 𝑃3 |

𝑎31

𝑎32

𝑎33

𝑎3𝑛

| + ………𝑃𝑚 |

𝑎𝑚1

𝑎𝑚2

𝑎𝑚3

𝑎𝑚𝑛

|   …… (2) 

Thereafter, we divided all the elements of the WSMs by their respective 

priority vector element (i.e., P1, P2…. Pm). Then, max was obtained by 
estimating the average of the values derived when WSMs were divided by 
the respective priority vector elements. Next, we calculated the consistency 
index (CI) as follows: 

CI = max - n / n-1 
The consistency ratio (CR) is calculated by dividing the CI by the average 
random consistency. On the condition that CI < 0.1, the calculation is 
deemed acceptable (Golden and Wang 1989); the priority vector elements 
(i.e., P1, P2…. Pm) were considered as the weightage for the respective 
impact categories. 

b. Weights used for agri-inputs 

The agro-production scenario has various inputs such as seeds, soil 
nutrients, crop protection chemicals, fuel, and water. However, considering 
Golany and Roll’s (1989) rule of thumb for dataset size, the inputs were 



Ecology, Economy and Society–the INSEE Journal [78] 

 

aggregated into a single-weighted input. As this study involved seven 
DMUs, we limited it to three outputs (including one undesirable output) 
and one input to achieve good discriminatory power from the CCR and 
BCC models. The uniform weighting method was adopted to assign 
weights to the inputs manually. We assigned a conditional equal weight to 

each input (𝑤𝑖 =
1

𝑛
 or ∑ 𝑤�̇�

𝑛
𝑖=1 =

1

𝑛
+

1

𝑛
+

1

𝑛
⋯

1

𝑛
= 1; where “wi” represents 

the assigned weight for the respective input, and “n” represents the number 
of inputs), assuming that each input is equally important for the production 
process. Another reason for using this weighting approach was to ensure 
the balanced treatment of each input, making it a practical scheme in 
agricultural production scenarios. 

4. RESULTS 

The results revealed significant environmental impacts associated with the 
crops, demonstrating their efficiency and performance. Detailed findings 
are presented below in the respective subsections. 

4.1. Environmental Impacts of Crops  

The LCA results revealed distinct environmental impacts across the studied 
crops, highlighting differences in resource use and emissions. Key impact 
categories included global warming potential, land use, and eutrophication. 
These variations underscore the importance of crop selection in sustainable 
agricultural practices. The detailed results are further discussed in the 
subsequent subsections. 

4.1.1 Descriptive statistics of environmental impacts 

Table 2. Descriptive Statistics of the Environmental Impacts of Crops 

Impact 
Reference/ 
measurement 
unit 

Mean 
Standard 
deviation 

Min Max- 

Range 

(max–
min) 

PMF kg PM2.5 eq 1.42 0.27 0.88 2.9 2.02 
FRS kg oil eq 227.4 41.92 72.55 429.2 356.65 
FWET kg 1,4-DCB 72.32 27.85 4.5 191.67 187.17 
FEUT kg P eq 0.28 0.07 0.16 0.7 0.54 
GWP kg CO2 eq 862.67 120.24 459.49 1387.45 927.96 
HCT kg 1,4-DCB 1438.11 786.12 4.7 5514.36 5509.66 
HNCT kg 1,4-DCB 104662.2 52556.72 567.47 346564 345996.6 
ALU m2a crop eq 293.66 95.07 16.5 597.2 580.7 
MET kg 1,4-DCB 114979.93 59004.6 6.48 389969.4 389962.9 
MEUT kg N eq 1.12 0.86 0.05 6.24 6.19 
MRS kg Cu eq 23.43 6.92 0.9 40.84 39.94 
OF kg NOx eq 6.74 1.39 3.58 14.35 10.77 



[79] Khangar and Thangavel 

 

OD kg CFC11 eq 0.01 0 0.01 0.03 0.02 
TEAF kg SO2 eq 3.79 0.5 2.71 6.56 3.85 
TETO kg 1,4-DCB 24522.12 9037.7 641.86 69653.1 69011.2 

Note: ALU: agricultural land use, DSR: direct-seeded rice, GWP: global warming 
potential, FWET: freshwater ecotoxicity, FEUT: freshwater eutrophication, MET: 
marine ecotoxicity, MEUT: marine eutrophication, TEAF: terrestrial acidification, 
TETO: terrestrial ecotoxicity, OF: ozone formation emission, FRS: fossil resource 
scarcity, MRS: mineral resource scarcity, HCT: human carcinogenic toxicity, 
HNCT: human non-carcinogenic toxicity, OD: stratospheric ozone depletion. 
Source: Authors’ analysis 

The average environmental impact of particulate matter formation (PMF) 
from all the crops ranged between 1.42 ± 0.27 (mean ± standard deviation). 
The average global warming potential (GWP) was found to be 862.67 ± 
120.24. The land-use emissions arising from the cultivation of the selected 
crops were found to be 293.66 ± 95.07 per hectare of production. The 
descriptive statistics are presented in Table 2. 

4.1.2 Crop- and category-wise environmental impacts 

Table 3 presents a comprehensive environmental impact assessment for 
various crops, elucidating their ecological footprint across diverse 
categories. The study shows that direct-seeded rice (DSR) production has 
the highest emissions among crops. Most of the fossil fuel (diesel) induced 
emissions were found to be higher in DSR production. Bajra (a millet) was 
observed to be the most environmentally sustainable crop. In terms of 
PMF, DSR has the highest impact, at 2.90 kg PM2.5 eq, while bajra and 
soybeans exhibit comparatively lower values at 0.88 and 0.90 kg PM2.5 eq, 
respectively. This can primarily be attributed to field preparation and 
harvesting processes.  

Further, DSR was found to have the highest GWP, with a value of 1,387.45 
kg CO2 eq, while bajra has the least GWP of 459.49 kg CO2 eq. DSR and 
sorghum have the highest freshwater eutrophication potential, with values 
of 191.67 and 158.86 kg 1,4-DCB, respectively, owing to the runoff of 
nutrients to water bodies. Fossil resource scarcity (FRS) potential indicates 
that rainfed wheat has the lowest impact at 188.50 kg oil eq, while DSR 
records the highest impact at 429.20 kg oil eq.  

The production of crops also releases certain carcinogenic and non-
carcinogenic pollutants. DSR was found to have the highest potential to 
cause cancerous and non-cancerous diseases (Khangar and Mohanasundari 
2024; Khangar et al. 2025); conversely, bajra has the least potential. This can 
be attributed to the use of agrochemicals and anaerobic conditions in DSR 
production, which contribute to the release of pollutants. 



Ecology, Economy and Society–the INSEE Journal [80] 

 

Freshwater ecotoxicity (FWET) values indicate that millet is the least 
impactful at 4.50 kg 1,4-DCB, in contrast to DSR. Terrestrial acidification 
(TEAF) and terrestrial ecotoxicity (TETO) similarly reveal the varying 
environmental impacts of different crops across multiple dimensions. Table 
3 presents all other impact categories of the crops. The values contribute to 
a comprehensive understanding of the environmental consequences of 
agricultural practices, aiding in the identification of areas for improvement 
and promoting sustainable cultivation methods.  

 

Table 3. Crop-wise Environmental Impacts 

Impact 
cate-
gory 

Reference 
unit 

Irrigated 
wheat 

Rainfed 
wheat 

DSR Maize Sor-
ghum 

Millet Soy-
bean 

PMF kg PM2.5 eq 1.3 1.4 2.9 1.0 1.6 0.9 0.9 
FRS kg oil eq 266.0 188.5 429.2 205.8 266.5 72.6 163.3 
FWET kg 1,4-DCB 36.4 36.2 191.7 66.4 158.9 4.5 12.2 
FEUT kg P eq 0.2 0.2 0.3 0.2 0.2 0.7 0.2 
GWP kg CO2 eq 854 613 1388 931 1121 460 674 
HCT kg 1,4-DCB 2041 2420 5515 23 45 4.7 19 
HNCT kg 1,4-DCB 208033 171149 346564 1770 3310 568 1244 
ALU m2a crop eq 524.4 553.8 110.2 140.1 113.4 16.5 597.2 
MET kg 1,4-DCB 227976 186329 389970 200 244 7 135 
MEUT kg N eq 0.5 0.4 0.1 0.1 0.1 6.2 0.4 
MRS kg Cu eq 35.9 5.0 38.6 40.8 6.1 0.9 36.6 
OF kg NOx eq 5.6 6.8 14.4 3.9 7.8 5.3 3.6 
OD kg CFC11 eq 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
TEAF kg SO2 eq 4.0 3.3 6.6 2.9 4.0 3.0 2.7 
TETO kg 1,4-DCB 12658 12720 40475 69653 27687 642 7821 

Note: ALU: agricultural land use, DSR: direct-seeded rice, GWP: global warming 
potential, FWET: freshwater ecotoxicity, FEUT: freshwater eutrophication, MET: 
marine ecotoxicity, MEUT: marine eutrophication, TEAF: terrestrial acidification, 
TETO: terrestrial ecotoxicity, OF: ozone formation emission, FRS: fossil resource 
scarcity, MRS: mineral resource scarcity, HCT: human carcinogenic toxicity, 
HNCT: human non-carcinogenic toxicity, OD: stratospheric ozone depletion. 
Source: Authors’ analysis using the ReCiPe 2016 midpoint impact assessment 
method 

4.2. Eco-efficiency of Crops 

In a classical DEA model, where only desirable outputs are considered, 
efficiency scores range from 0 to 1. A score of 1 implies full efficiency, 
meaning a DMU operates on the efficiency frontier. In contrast, scores less 
than 1 indicate relative inefficiency. The classical model prefers higher 
values for desirable outputs, and efficiency is maximized when a DMU 



[81] Khangar and Thangavel 

 

achieves the highest output levels given its inputs. However, when 
undesirable outputs are incorporated into the analysis, the interpretation of 
efficiency scores can change. In the presence of undesirable outputs, lower 
values for these outputs are preferred. The directional distance DEA model 
with undesirable outputs aims to assess efficiency while minimizing 
undesirable outputs. In this model, the efficiency score is formulated to 
reflect both the maximization of desirable outputs and the minimization of 
undesirable outputs. As a result, an efficiency score of 0 represents a DMU 
that fully achieves the desirable output while minimizing the undesirable 
output. Scores greater than 0 indicate inefficiency, with higher values 
representing larger deviations from the efficiency frontier in both directions 
(for desirable and undesirable outputs). 

This study, when conducted per the CCR model, identified three efficient 
decision-making units (EDMUs). Conversely, when employing the BCC 
model—which assumes variable returns to scale (either increasing or 
decreasing)—the study identified five EDMUs. The BCC model offers a 
more flexible approach to efficiency assessment compared to the CCR 
model. This discrepancy in the number of identified EDMUs underscores 
the influence of assumptions, specifically constant or variable returns to 
scale, on the outcomes of DEA studies. The efficiency ratios of the various 
crop productions in Madhya Pradesh are depicted in Table 4. 

Table 4. Eco-efficiency of Crops under the CCR and BCC Assumptions  
Eco-efficiency 

DMU Constant return 
to scale (CCR) 

Variable return 
to scale (BCC) 

IW 0.0000 0.0000 
RF 0.2686 0.0000 
DSR 0.0000 0.0000 
Maize 0.1131 0.0000 
Sorghum 0.2888 0.1981 
Millet  0.0000 0.0000 
Soybean 0.8975 0.8466 

EDMU 3 5 

AIn 0.224 0.149 

AAIn 0.39 0.52 

Note: IW: irrigated wheat, RF: rainfed wheat, DSR: direct-seeded rice, EDMU: 
number of eco-efficient crops, AAIn: aggregated average inefficiency of inefficient 
crops, AIn: average inefficiency of all crops. 
Source: Authors’ analysis 

Under the CCR assumption, the study found that IW, DSR, and bajra 
(millet) can be produced efficiently in Madhya Pradesh. However, the 
production of RF, sorghum, soybean, and maize is relatively inefficient. 



Ecology, Economy and Society–the INSEE Journal [82] 

 

Thus, their production can be improved, and the environmental damage 
(ED) caused by them can be reduced. With the BCC assumption, the study 
observed that IW, RF, DSR, maize, and millet perform efficiently; 
meanwhile, sorghum and soybean are less efficient. The primary cause of 
this might be the crops’ relatively higher input requirements and lower yield 
levels. These DMUs can be made efficient by achieving the target amount 
of output while reducing input consumption and ED.  

Aggregate mean crop inefficiency was found to be 0.22 and 0.149 under the 
CCR and BCC assumptions, respectively, indicating that there is a 22% and 
14.9% directional potential for effective resource utilization under both 
assumptions. Inconsistency in maize and RF efficiency, as well as 
inefficiency under different assumptions, suggests that these crops operate 
at a scale where they could potentially adjust their input usage to achieve 
higher efficiency levels. In the CCR model, they may appear inefficient 
because they are not adjusting their scale of operations optimally, whereas 
in the BCC model, they can adjust their scale, thus appearing efficient. 

4.2.1 Targets under the CCR and BCC assumptions 

This study aimed to identify and analyse the key targets for achieving 
efficiency among DMUs. Efficiency is a paramount consideration for 
organizations seeking optimal performance, effective resource utilization, 
and enhanced productivity. DEA was used as a robust framework for 
evaluating and benchmarking the performance of DMUs. As we delve into 
the results, it becomes evident that understanding the specific targets 
contributing to efficiency is essential when trying to enhance the overall 
efficiency and effectiveness of crops. This study sheds light on the critical 
factors and benchmarks that propel DMU efficiency, providing valuable 
insights for strategic decision-making and performance improvement.  

Under the CCR and BCC assumptions, we find that inefficient DMUs can 
be converted into efficient ones by reducing input and ED while 
simultaneously increasing the desirable output (crop and residue) based on 
the functional direction given to them. Under the CCR assumption, the 
inputs of RF, maize, sorghum, and soybean can be reduced by 26.86%, 
11.31%, 28.88%, and 89.75%, respectively. Meanwhile, the undesirable 
output (environmental damage) can be reduced by 0.01%, 0.004%, 0.03%, 
and 0.29%, respectively, with a significant increase in desirable outputs. 
Table 4 presents the results for the BCC assumption. However, assuming 
nonlinearity between input and output, the BCC model disseminates the 
best results in this situation. Under this assumption, sorghum and soybean 
have the potential to reduce input by 19.81% and 84.66% and ED by 
0.02% and 0.27%, respectively, with a significant increase in crop residue 



[83] Khangar and Thangavel 

 

and crop grain to become efficient DMUs. The input can be decreased by 
sharing it with their peer crops. 

The eco-efficiency ratio and associated targets are influenced by the chosen 
direction vector, denoted as “g”. For instance, when prioritizing a 10% 
reduction in inputs and a 40% decrease in ED—while concurrently aiming 
for a 20% increase in yield and a 10% increase in residue—the efficiency 
ratios and requisite targets for achieving crop efficiency change (Table 5).  

 

Table 4. Targets to Achieve Eco-efficient Production (G = 0%, 20%, 10%, −20%) 

  Increase and decrease under 
CCR (%) 

Increase and decrease under 
BCC (%) 

DMU I CY CR ED I CY CR ED 

IW 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 
RF −26.86 0.16 7.25 -0.01 0.00 0.00 0.00 0.00 
DSR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 
Maize −11.31 0.02 39.03 -0.004 0.00 0.00 0.00 0.00 
Sorghum −28.88 1.28 0.08 −0.03 −19.81 2.27 2.48 −0.02 
Millet 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 
Soybean −89.75 53.45 0.34 −0.29 −84.66 123.12 46.79 −0.27 

Note: Negative values indicate a decrease. IW: irrigated wheat, RF: rainfed wheat, 
DSR: direct-seeded rice, I: aggregated input, CY: crop yield, CR: crop residue, ED: 
environmental degradation. 
Source: Authors’ analysis 

The findings indicate that to enhance the eco-efficiency of soybean 
cultivation under the BCC assumption, a reduction of 25.86% in ED and 
84.64% in inputs is necessary, alongside increases of 125.27% and 48.30% 
in yield and residue, respectively. Similarly, assuming CCR, a reduction of 
26.47% in ED and 86.61% in inputs is warranted, along with increases of 
100.28% and 31.47% in soybean yield and residue, respectively, to ensure 
sustainable crop production.  

Table 5. Targets to Achieve Eco-efficient Production (G = −10%, 20%, 10%, −40%)  

DMU CCR BCC 

 EE I CY CR ED EE I CY CR ED 

IW 0 0 0 0 0 0 0 0 0 0 
RF 3.08 −26.24 2.16 9.15 −0.07 0 0 0 0 0 
DSR 0 0 0 0 0 0 0 0 0 0 
Maize 1.95 −10.89 0.5 39.72 −0.08 0 0 0 0 0 
Sorghum 4.45 −27.92 2.64 1.48 −0.47 3.16 −19.81 2.31 2.53 −0.33 
Millet 0 0 0 0 0 0 0 0 0 0 
Soybean 73.52 −86.61 100.3 31.47 −26.47 71.84 −84.64 125.3 48.3 −25.86 



Ecology, Economy and Society–the INSEE Journal [84] 

 

Note: Negative values indicate a decrease. IW: irrigated wheat, RF: rainfed wheat, 
DSR: direct-seeded rice, I: aggregated input, CY: crop yield, CR: crop residue, ED: 
environmental degradation. 
Source: Authors’ analysis 

It is noteworthy that efficiency ratios are contingent upon the chosen 
direction of inputs and outputs; however, the categorization of crops into 
efficient and inefficient remains consistent across both directional choices. 
Hence, it can be inferred that these crops demonstrate inefficiency in their 
production processes, underscoring the need for sustainable practices to 
enhance efficiency. 

5. DISCUSSION 

This study represents a pioneering effort in evaluating the eco-efficiency of 
agricultural produce in the central region of India, specifically, in the state 
of Madhya Pradesh. In the field of agricultural eco-efficiency, determining 
the returns to scale holds significant importance. Given the absence of 
precise information on the correlation between input and output, and the 
increased proportion of input to output, this study, following the approach 
of Fusco et al. (2023), adopted the constant returns to scale (CCR model) 
assumption. Additionally, the study employed the variable returns to scale 
model (BCC model) to address uncertainties and identify the sensitivity of 
crops to changes in scale. The DEA results reveal that when the scale of 
production is assumed to have no impact on efficiency, four crops—rainfed 
wheat, maize, sorghum, and soybean—are found to be inefficient. 
Conversely, when the scale of production is assumed to be disproportionate 
to output, inefficiency is observed only in the production of sorghum and 
soybeans. It is noteworthy that, despite exhibiting higher environmental 
emissions in major impact categories, DSR proves to be an efficient crop in 
both scenarios. Its efficiency score remains unaffected by the scale of 
production and input–output ratio.  

It is crucial to recognize that the outcomes derived from DEA serve as a 
relative efficiency measure and vary based on the specific crop or farm. In 
this study, the aggregated eco-efficiency results for the selected crops reveal 
an aggregated average inefficiency of 0.39 at CRS and 0.52 at VRS. While 
these figures align closely with findings by Ding et al. (2024) and Fusco et al. 
(2023), they deviate slightly, reinforcing the notion that India exhibits 
greater ecological efficiency than China, as argued by Aslam et al. (2021). 
Zyłowski and Kozyra’s (2023) investigation underscores the multifaceted 
nature of these results. They highlight that factors such as geographical area, 
cultivation season, crops included, soil condition, temperature, the use of 
soil nutrients, and the type of DEA model employed can influence crop 



[85] Khangar and Thangavel 

 

efficiency. Consequently, variations in outcomes compared to other studies 
are to be expected. Furthermore, this study identified DSR as an efficient 
cultivation method, characterized by a reduced environmental impact and 
higher yield potential compared to traditional flooded paddy cultivation 
practices. This conclusion diverges from the findings of Huang et al. (2022) 
and Chaloob et al. (2018). 

Additionally, the DEA results facilitate the identification of benchmark 
units from which inefficient units can seek guidance. Under the BCC 
assumption, to enhance the efficiency of soybean and sorghum production, 
rainfed wheat and millet stand out as notable reference crops, exhibiting 
potential avenues for improving resource utilization and productivity. In the 
context of CRS, irrigated wheat, DSR, and millet emerge as potential 
benchmarking crops, providing insights into best practices and efficient 
resource allocation. In the pursuit of improving the efficiency of rainfed 
wheat, soybean, and maize, DSR and irrigated wheat emerge as possible 
peer crops.  

Identifying these peer crops creates opportunities for the exchange of farm 
inputs, including fertilizers, pesticides, and herbicides. By adopting best 
practices from these efficient peer crops, rainfed wheat cultivation can 
optimize resource use, potentially reducing its overall reliance on inputs. 
Furthermore, for enhancing the efficiency of soybean and sorghum 
production, irrigated wheat serves as a pertinent peer crop. This opens 
avenues for the exchange and adoption of farming techniques and inputs to 
elevate the efficiency of soybean and sorghum cultivation, potentially 
reducing the use of fertilizers, pesticides, and herbicides while promoting 
sustainable and resource-efficient farming practices.  

The identification of peer crops for inefficient crops also provides an 
opportunity to exchange inputs to optimize output while minimizing both 
input usage and ED. This collaborative approach can be implemented 
through techniques such as intercropping or crop rotation, tailored to the 
specific season. Intercropping involves sowing crops in the same season, 
whereas crop rotation involves planting crops in different or successive 
seasons. In intercropping, two crops share nutrients concurrently, 
enhancing overall resource efficiency. Similarly, the immediate sowing of a 
different crop in crop rotation allows the utilization of existing nutrients 
and inputs left by the previous crop, leading to a simultaneous reduction in 
inputs and environmental impact. For instance, implementing a crop 
rotation strategy that involves planting sorghum following irrigated wheat 
can enhance nitrogen use efficiency and decrease reliance on synthetic 
fertilizers. 



Ecology, Economy and Society–the INSEE Journal [86] 

 

Figure 2 illustrates various cropping patterns and the interconnectedness of 
crops in both scenarios, providing a visual representation of the identified 
peer connections among crops and potential cropping patterns to increase 
eco-efficiency. The findings of this study support those of Paroda (2022) 
for better, sustainable, and eco-efficient agriculture. The findings from 
various global studies on crop rotation and intercropping, such as millet 
with soybean in Chapagain et al. (2018), wheat with soybean and maize in 
Janovicek et al. (2020), wheat with sorghum in Holman et al. (2023), millet 
with sorghum in Stoop (1987), and rice with maize in Erythrina et al. (2022), 
suggest potential benefits for these crop combinations. However, in the 
specific climatic conditions of Madhya Pradesh, further primary 
experimental investigation is necessary. 

Figure 2. Peer Crops and Crop Eco-efficiency Optimization Pattern 

 

Source: Authors’ compilation 

 

6. CONCLUSION 

This study makes a fundamental contribution to achieving the SDGs, 
particularly goals 2 (zero hunger), 12 (sustainable production and 
consumption), and 13 (climate action). The findings highlight that eco-
efficient agricultural production plays a crucial role in promoting 



[87] Khangar and Thangavel 

 

sustainability and mitigating environmental degradation, thereby ensuring 
food security. Food security and environmental preservation are inherently 
interconnected, as efforts to enhance one invariably influence the other. In 
agricultural production, there is often a trade-off between increased yields 
and environmental depletion. This study emphasises the importance of 
striking a balance between productivity and sustainability, as increased crop 
production often leads to greater environmental degradation. 

The DEA–DDF framework adopted in this study focuses on reducing 
input use and emissions while increasing crop production, thereby 
providing a valuable tool for optimizing eco-efficiency. The results 
demonstrate that inefficient production systems can improve their 
performance through peer benchmarking, wherein input usage and the 
environmental impact levels of inefficient crops can be adjusted to match 
efficiency benchmarks. The study also emphasizes that crops such as 
rainfed wheat and maize operate at a scale where optimizing resource use 
can significantly improve efficiency. Additionally, intercropping and crop 
rotation patterns have the potential to enhance resource efficiency.  

To render these findings into action, we offer targeted policy 
recommendations (Figure 4). First, reducing input use and improving 
efficiency should be prioritized through precision agriculture, optimal soil 
nutrient management, and renewable energy–based mechanization to 
sustain yields while minimizing environmental impacts. Second, given the 
prevalence of intercropping in Madhya Pradesh, academia and policymaking 
should focus on identifying the most efficient crop rotation and 
intercropping patterns to enhance resource-use efficiency. Third, block- and 
panchayat-level farmer awareness programmes, facilitated by farmer 
producer organizations, can play a key role in promoting efficient input use, 
crop succession planning, and intercropping systems. Moreover, agricultural 
research institutions and extension services should strengthen their efforts 
to disseminate knowledge on sustainable and climate-resilient farming 
practices. 

Finally, demonstration farms should be established to showcase successful 
eco-efficient farming models and provide hands-on learning opportunities 
for farmers. Encouraging a collaborative farming environment can promote 
knowledge sharing and the adoption of diversified agricultural systems, 
thereby maximizing productivity while minimizing environmental trade-
offs. By implementing these policies, Madhya Pradesh can transition 
towards a more sustainable agricultural system, ensuring efficient resource 
utilization and reduced environmental impacts. 



Ecology, Economy and Society–the INSEE Journal [88] 

 

Figure 4. Policy Framework for Agricultural Economic and Environmental 
Sustainability 

 

Source: Authors’ compilation 

 

 

 

 

7. LIMITATION AND FUTURE RESEARCH DIRECTION 

This study relied on secondary data to estimate the environmental impacts 
of crop production in Madhya Pradesh, using average input use per hectare 
as the functional unit. While this approach allows for broad comparisons 
across cropping systems, it may obscure significant farm-level variations. 
Collecting primary data would allow for the measurement of actual on-farm 
input usage, leading to more accurate estimates of emissions and efficiency. 
This would improve the precision of LCAs by capturing the heterogeneity 
in farming practices that aggregated state-level data cannot reveal. 

Another key limitation is the unavailability of cost data in the secondary 
sources used. As a result, the study could only estimate environmental and 
technical efficiency, but not economic efficiency. If primary data collection 
includes cost-related variables such as input prices, revenues, or profit 
margins, it would be possible to conduct a comprehensive economic–
environmental efficiency analysis, going beyond technical efficiency for a 
more comprehensive assessment of how effectively resources are being 
used. This would be particularly valuable for researchers and policymakers 
seeking to design strategies that balance sustainability with the livelihoods 
of farmers. 

Additionally, while crop peer benchmarking was conducted in this study, it 
is essential to acknowledge that some of the identified peer crops may not 



[89] Khangar and Thangavel 

 

be agronomically or economically feasible for intercropping or crop 
rotation, given the regional conditions of Madhya Pradesh. For example, 
crops requiring high soil moisture or cooler temperatures may not perform 
well in areas with coarse-textured soils and semi-arid climatic conditions 
that are prevalent in parts of the region. Implementing such changes 
without context-specific validation could lead to unintended negative 
consequences. This warrants further exploration through localized studies 
that incorporate both environmental and economic criteria. 

Ethics Statement: This study is based on publicly available secondary data 
and therefore does not require ethical approval from an institutional ethics 
committee. 

Data Availability statement: The input data used for the Life Cycle 
Analysis (LCA) in this study was derived from multiple publicly available 
government sources, including official agricultural package of practices. 
Due to the fragmented nature of these sources and the integration of data 
across several platforms that are not centrally archived, the complete dataset 
is not hosted in a single repository. However, to promote transparency and 
facilitate further research, the processed LCA results and the subsequent 
Data Envelopment Analysis (DEA) outputs will be made available by the 
authors upon reasonable request. 

Conflict of Interest Statement: No potential conflict of interest was 
reported by the authors. 

REFERENCES 

Adams, Richard M, Brian H Hurd, Stephanie Lenhart, and Neil Leary. 1998. 
“Effects of Global Climate Change on Agriculture: An Interpretative Review.” 
Climate Research 11: 19–30. https://doi.org/10.3354/cr011019. 

Allen, Rachel, Antreas D Athanassopoulos, RG Dyson, and Emmanuel 
Thanassoulis. 1997. “Weights Restrictions and Value Judgments in Data 
Envelopment Analysis: Evolution, Development and Future Directions.” Annals of 
Operations Research 73: 13–34. https://doi.org/10.1023/A:1018968909638. 

Amirteimoori, Alireza, Vincent Charles, and Saber Mehdizadeh. 2023. “Stochastic 
Data Envelopment Analysis in the Presence of Undesirable Outputs.” Journal of the 
Operational Research Society 74 (2): 1–14. 
https://doi.org/10.1080/01605682.2023.2172366. 

Arora, Naveen K. 2019. “Impact of Climate Change on Agriculture Production and 
its Sustainable Solutions.” Environmental Sustainability 2: 95–96. 
https://doi.org/10.1007/s42398-019-00078-w. 

Aryal, Jeetendra, Tek Sapkota, ML Jat, and Dalip Bishnoi. 2015. “On-farm 
Economic and Environmental Impact of Zero-Tillage Wheat: A Case of North-



Ecology, Economy and Society–the INSEE Journal [90] 

 

west India.” Experimental Agriculture 51 (1): 1–16. 
https://doi.org/10.1017/S001447971400012X. 

Aslam, Muhammad, Pan Huanxue, Sidra Sohail, Muhammad Tariq Majeed, Saeed 
ur Rahman, and Shoaib Anees. 2021. “Assessment of Major Food Crops 
Production-based Environmental Efficiency in China, India, and Pakistan.” 
Environmental Science and Pollution Research 29: 10091–10100. 
https://doi.org/10.1007/s11356-021-16161-x. 

Bagheri, Pariya, Sahar Abedi, and Farid Sarajug. 2020. “Environmental Efficiency 
in Agricultural Sector.” In The Economics of Agriculture and Natural Resources, edited by 
Masoomeh Rashidghalam. Springer. 

Cao Wei, Wen Zhou, Tao Wu, Xiaochun Wang, and Jinhai Xu. 2022. “Spatial-
temporal Characteristics of Cultivated Land Use Eco-efficiency Under Carbon 
Constraints and its Relationship with Landscape Pattern Dynamics.” Ecological 
Indicators 141: 109–140. https://doi.org/10.1016/j.ecolind.2022.109140. 

Chaloob, Ibrahim Zeghaiton, Razamin Ramli, and Mohd Kamal Mohd Nawawi. 
2018. “Measuring Economic and Environmental Efficiency for Agricultural Zones 
in Iraq Using Data Envelopment Analysis.” International Journal of Information and 
Decision Sciences 10 (3): 235–48. https://doi.org/10.1504/IJIDS.2018.093922. 

Chapagain, Tejendra, Roshan Pudasaini, Bhawana Ghimire, Khem Gurung, Khem 
Choi, Laxmi Rai, Samjhana Magar, Bishnu BK, and Manish Raizada. 2018. 
“Intercropping of Maize, Millet, Mustard, Wheat and Ginger Increased Land 
Productivity and Potential Economic Returns for Smallholder Terrace Farmers in 
Nepal.” Field Crops Research 227: 91–101. https://doi.org/10.1016/j.fcr.2018.07.016. 

Chung, Yang Ho, Rolf Färe, and Shawna Grosskopf. 1997. “Productivity and 
Undesirable Outputs: A Directional Distance Function Approach.” Journal of 
Environmental Management 51(3): 229–40. https://doi.org/10.1006/jema.1997.0146. 

Coluccia, Benedetta, Donatella Valente, Giulio Fusco, Federica De Leo, and 
Donatella Porrini. 2020. “Assessing Agricultural Eco-efficiency in Italian Regions.” 
Ecological Indicators 116: 106483. https://doi.org/10.1016/j.ecolind.2020.106483. 

Cooper, William W, Lawrence Seiford, and Joe Zhu. 2011. “Data Envelopment 
Analysis: History, Models, and Interpretations.” In Handbook on Data Envelopment 
Analysis, edited by William W Cooper, Lawrence Seiford, and Joe Zhu. Springer. 

Ding, Fanglan, Chenghua Liu, and Yanlin Wang. 2023. “Analysis of Grain 
Production Efficiency Differences in China Based on DEA-Malmquist Index.” In 
Proceedings of the 2023 4th International Conference on Management Science and Engineering 
Management (ICMSEM 2023), edited by Suhaiza Hanim Binti Dato Mohamad 
Zailani, Kosga Yagapparaj, and Norhayati Zakuan. Atlantis Press.  

Dyckhoff, Harald, and K Allen. 2001. “Measuring Ecological Efficiency with Data 
Envelopment Analysis (DEA).” European Journal of Operational Research 132 (2): 312–
325. https://doi.org/10.1016/S0377-2217(00)00154-5. 

Erythrina, Erythrina, Susilawati Susilawati, Slameto Slameto, Ni Made Delly 
Resiani, Forita Dyah Arianti, Jumakir Jumakir, Anis Fahri, Andy Bhermana, 
Asmanur Jannah, and Hasil Sembiring. 2022. “Yield Advantage and Economic 



[91] Khangar and Thangavel 

 

Performance of Rice–Maize, Rice–Soybean, and Maize–Soybean Intercropping in 
Rainfed Areas of Western Indonesia with a Wet Climate.” Agronomy 12 (10): 23–26. 
https://doi.org/10.3390/agronomy12102326. 

FAO. 2022. Greenhouse Gas Emissions from Agrifood Systems: Global, Regional and Country 
Trends, 2000–2020. FAOSTAT Analytical Brief 50. Food and Agriculture 
Organization of the United Nations.  

Färe, Rolf, and Shawna Grosskopf. 2004. “Modeling Undesirable Factors in 
Efficiency Evaluation: Comment.” European Journal of Operational Research 157 (1): 
242–45. https://doi.org/10.1016/S0377-2217(03)00191-7. 

Färe, Rolf, Shawna Grosskopf, CAK Lovell, and Carl Pasurka. 1989. “Multilateral 
Productivity Comparisons When Some Outputs are Undesirable: A Nonparametric 
Approach.” The Review of Economics and Statistics 71 (1): 90–98. 
https://doi.org/10.2307/1928055. 

Farmer Welfare and Agriculture Development Department, Madhya Pradesh. n.d.a. 
“Package of Practice, Wheat.” Accessed June 27, 2025. 
https://mpkrishi.mp.gov.in/Englishsite_New/krishi_capsules_wheat_New.aspx 

____. n.d.b. “Package of Practice, Paddy.” Accessed June 27, 2025. 
https://mpkrishi.mp.gov.in/Englishsite_New/krishi_capsules_dhan_New.aspx 

____. n.d.c. “Package of Practice, Makka.” Accessed June 27, 2025. 
https://mpkrishi.mp.gov.in/Englishsite_New/krishi_capsules_makka_New.aspx 

Fusco, Giulio, Francesco Campobasso, Lucio Laureti, Massimo Frittelli, Donatella 
Valente, and Irene Petrosillo. 2023. “The Environmental Impact of Agriculture: An 
Instrument to Support Public Policy.” Ecological Indicators 147: 109961. 
https://doi.org/10.1016/j.ecolind.2023.109961. 

Golany, Boaz, and Yaakov Roll. 1989. “An Application Procedure for DEA.” 
Omega 17: 237–50. https://doi.org/10.1016/0305-0483(89)90029-7. 

Golden, Bruce L, and Qiwen Wang. 1989. “Abstract an Alternate Measure of 
Consistency.” In The Analytic Hierarchy Process, edited by Bruce L Golden, Edward A 
Wasil, and Patrick T Harker. Springer.  

Holman, Jonathan D, Augustine K Obour, and Yared Assefa. 2023. “Forage 
Sorghum Grown in a Conventional Wheat-grain Sorghum-fallow Rotation 
Increased Cropping System Productivity and Profitability.” Canadian Journal of Plant 
Science 103 (1): 61–72. https://doi.org/10.1139/cjps-2022-0171. 

Hsu, Shao-Yun, Chih-Yu Yang, Yueh-Ling Chen, and Ching-Cheng Lu. 2023. 
“Agricultural Efficiency in Different Regions of China: An Empirical Analysis 
Based on Dynamic SBM-DEA Model.” Sustainability 15 (9): 7340. 
https://doi.org/10.3390/su15097340. 

Hua, Zhongsheng, and Yiwen Bian. 2007. “DEA with Undesirable Factors.” In 
Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, 
edited by Joe Zhu and Wade D Cook. Springer. https://doi.org/10.1007/978-0-
387-71607-7_6. 



Ecology, Economy and Society–the INSEE Journal [92] 

 

Huang, Jianhuan, Jiejin Xia, Yantuan Yu, and Ning Zhang. 2018. “Composite Eco-
efficiency Indicators for China Based on Data Envelopment Analysis.” Ecological 
Indicators 85: 674–97. https://doi.org/10.1016/j.ecolind.2017.10.040. 

Huang, Malan, Linlin Zeng, Chujie Liu, Xiaoyun Li, and Hongling Wang. 2022. 
“Research on the Eco-efficiency of Rice Production and its Improvement Path: A 
Case Study from China.” International Journal of Environmental Research and Public 
Health 19 (14): 8645. https://doi.org/10.3390/ijerph19148645. 

Huijbregts, Mark AJ, Zoran JN Steinmann, Pieter MF Elshout, Gea Stam, 
Francesca Verones, Marisa Vieira, Michiel Zijp, Anne Hollander, and Rosalie Van 
Zelm. 2016. “ReCiPe 2016: A Harmonized Life Cycle Impact Assessment Method 
at Midpoint and Endpoint Level.” International Journal of Life Cycle Assessment 22: 
138–47. https://doi.org/10.1007/s11367-016-1246-y. 

ICAR-IIMR. n.d.a. “Recommended Package of Practices: Kharif Sorghum.” Indian 
Council of Agricultural Research–Indian Institute of Millets Research (ICAR-
IIMR). Accessed June 27, 2025. 
https://www.millets.res.in/farmer/Recommended_package_of_Practices_Kharif.p
df. 

____. n.d.b. “Recommended Package of Practices: Pearl Millet.” Indian Council of 
Agricultural Research–Indian Institute of Millets Research (ICAR-IIMR). Accessed 
June 27, 2025. 
https://www.millets.res.in/technologies/Recommended_package_of_practices-
Pearl_millet.pdf. 

Indiastat. n.d. “Agricultural production.” Accessed June 27, 2025. 
https://www.indiastat.com/data/agriculture/agricultural-production.  

IPCC. 2019. Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas 
Inventories. The Intergovernmental Panel on Climate Change (IPCC).  

Iribarren, Diego, Antonino Marvuglia, Paula Hild, Mélanie Guiton, Emil Popovici, 
and Enrico Benetto. 2015. “Life Cycle Assessment and Data Envelopment Analysis 
Approach for the Selection of Building Components According to Their 
Environmental Impact Efficiency: A Case Study for External Walls.” Journal of 
Cleaner Production 87: 707–16. https://doi.org/10.1016/j.jclepro.2014.10.073. 

Janovicek, Ken, Dave Hooker, Alfons Weersink, Richard Vyn, and Bill Deen. 2020. 
“Corn and Soybean Yields and Returns are Greater in Rotations with Wheat.” 
Agronomy Journal 113: 1691–711. https://doi.org/10.1002/agj2.20605. 

Jimmy, Abdun Naqib, Nazmul Ahsan Khan, Muhammed Noor Hossain, and 
Mohammad Sujauddin. 2017. “Evaluation of the Environmental Impacts of Rice 
Paddy Production Using Life Cycle Assessment: Case Study in Bangladesh.” 
Modeling Earth Systems and Environment 3 (4): 1691–705. 
https://doi.org/10.1007/s40808-017-0368-y. 

Khangar, Nihal Singh, T., Mohanasundari, Bisla, Muskan, K., Thomas Felix, A.R. 
Durga. 2025. “Environmental impact analysis of crop residue burning in Madhya 
Pradesh: A multivariate comparison across key crops.” Environ Monit Assess 197, 
211. https://doi.org/10.1007/s10661-025-13655-w. 



[93] Khangar and Thangavel 

 

Khangar, Nihal Singh, and Mohanasundari Thangavel. 2025. “Assessment of the 
Environmental Impacts of Soybean Production Within Fields in Madhya Pradesh: 
A Life Cycle Analysis Approach.” Integrated Environmental Assessment and Management 
21 (3): 688–701. https://doi.org/10.1093/inteam/vjae052. 

____. 2024. “Assessment of Environmental Impacts: A Life Cycle Analysis of 
Wheat and Rice Production in Madhya Pradesh.” Agronomy Research 22 (Suppl 2): 
636–58. https://doi.org/10.15159/AR.24.011. 

____. 2023. “Measuring the Environmental Impact of Maize, Sorghum, and Millets 
Production in Madhya Pradesh: A Comparative Analysis Using Life Cycle 
Assessment.” In Environment and Sustainable Development Proceedings of the 2023 8th 
Asia Conference on Environment and Sustainable Development, edited by Keiji Ujikawa, 
Mikio Ishiwatari, and Eric van Hullebusch. Springer Nature. 

Korhonen, Pekka J, and Mikulas Luptacik. 2004. “Eco-efficiency Analysis of Power 
Plants: An Extension of Data Envelopment Analysis.” Europe Journal of Operational 
Research 154 (2): 437–46. https://doi.org/10.1016/S0377-2217(03)00180-2. 

Kumar, Amit, Ashish Kumar, Khushi Mann, and Thangavel Mohanasundari. 2024. 
“Analysing District-level Climate Vulnerability Pattern in Madhya Pradesh, India, 
Based on Agricultural and So-economic Indicators.” Environmental Monitoring and 
Assessment 196 (6): 528. https://doi.org/10.1007/s10661-024-12646-7. 

Kumar, Amit, Tapas Ray, and Thangavel Mohanasundari. 2024. “Exploring 
Climatic Dynamics in Madhya Pradesh, India Utilizing Long-Term Gridded Data 
(1951–2021): An Integrated Statistical and GIS Modules.” In Integrated Management of 
Water Resources in India: A Computational Approach, edited by Akhilesh Kumar Yadav, 
Kanchan Yadav, and Vijay P Singh. Springer. 

Kumar, Rajesh, S Karmakar, Asisan Minz, Jitendra Singh, Abhay Kumar, and 
Arvind Kumar. 2021. “Assessment of Greenhouse Gases Emission in Maize-
Wheat Cropping System Under Varied N Fertilizer Application Using Cool Farm 
Tool.” Frontiers in Environmental Science 9. 
https://doi.org/10.3389/fenvs.2021.710108. 

Kuosmanen, Timo, and Mika Kortelainen. 2005. “Measuring Eco-efficiency of 
Production with Data Envelopment Analysis.” Journal of Industrial Ecology 9 (4): 59–
72. https://doi.org/10.1162/108819805775247846. 

Kyrgiakos, Leonidas Sotirios, Georgios Kleftodimos, George Vlontzos, and Panos 
M Pardalos. 2023. “A Systematic Literature Review of Data Envelopment Analysis 
Implementation in Agriculture Under the Prism of Sustainability.” Operational 
Research 23 (7). https://doi.org/10.1007/s12351-023-00741-5. 

Lynch, John, Michelle Cain, David Frame, and Raymond Pierrehumbert. 2021. 
“Agriculture’s Contribution to Climate Change and Role in Mitigation Is Distinct 
from Predominantly Fossil CO2-emitting Sectors.” Frontiers in Sustainable Food 
Systems 4: 518039. https://doi.org/10.3389/fsufs.2020.518039. 

Malhi, Gurdeep Singh, Manpreet Kaur, and Prashant Kaushik. 2021. “Impact of 
Climate Change on Agriculture and Its Mitigation Strategies: A Review.” 
Sustainability 13 (3): 13–18. https://doi.org/10.3390/su13031318. 



Ecology, Economy and Society–the INSEE Journal [94] 

 

Masternak-Janus, Aneta, and Magdalena Rybaczewska-Błazejowska. 2017. 
“Comprehensive Regional Ecoefficiency Analysis Based on Data Envelopment 
Analysis: The case of Polish Regions.” Journal of Industrial Ecology 21 (1): 180–90. 
https://doi.org/10.1111/jiec.12393. 

Manogna, RL, and Aswini Kumar Mishra. 2020. “Agricultural Production 
Efficiency of Indian States: Evidence from Data Envelopment Analysis.” 
International Journal of Finance & Economics 27 (4): 1–12. 
https://doi.org/10.1002/ijfe.2369. 

Mohammadi, Ali, Shahin Rafiee, Ali Jafari, Alireza Keyhani, Tommy Dalgaard, 
Marie T Knudsen, Thu Lan T Nguyen, Robert Borek, and John Hermansen. 2015. 
“Joint Life Cycle Assessment and Data Envelopment Analysis for the 
Benchmarking of Environmental Impacts in Rice Paddy Production.” Journal of 
Cleaner Production 106: 521–32. https://doi.org/10.1016/j.jclepro.2014.05.008. 

Nayak, Amaresh Kumar, Rahul Tripathi, Manish Debnath, et al. 2023. “Carbon and 
Water Footprint of Major Crop Productions in India.” Pedosphere 33 (3): 448–62. 
https://doi.org/10.1016/j.pedsph.2022.06.045. 

Orea, Luis, and Alan Wall. 2016. “Measuring Eco-efficiency Using the Stochastic 
Frontier Analysis Approach.” In Advances in Efficiency and Productivity, edited by Juan 
Aparicio, CA Knox Lovell, Jesus T Pastor. Springer.  

National Food Security Mission. n.d. “Package of Practice: Soybean (Edible).” 
Accessed June 27, 2025. 
https://nfsm.gov.in/ReadyReckoner/Oilseeds/PackagePractice_OS2018.pdf. 

Paroda, Raj. 2022. “Crop Diversification for Sustainable Agriculture.” INSEE 
Journal 5 (1): 15–21. https://doi.org/10.37773/ees.v5i1.611. 

Picazo-Tadeo, Andrés, José Gómez-Limón, and Ernest Reig-Martínez. 2011. 
“Assessing Farming Eco-efficiency: A Data Envelopment Analysis Approach.” 
Journal of Environmental Management 92 (4): 1154–64. 
https://doi.org/10.1016/j.jenvman.2010.11.025. 

Pishgar-Komleh, Seyyed H, Tomasz Zylowski, Stelios Rozakis, and Jerzy Kozyra. 
2020. “Efficiency Under Different Methods for Incorporating Undesirable Outputs 
in an LCA + DEA Framework: A Case Study of Winter Wheat Production in 
Poland.” Journal of Environmental Management 260: 110–38. 
https://doi.org/10.1016/j.jenvman.2020.110138. 

Saaty, Thomas L. 1980. The Analytic Hierarchy Process. McGraw-Hill. 

Safa, Majeed, and Sandhya Samarasinghe. 2012. “CO2 Emissions from Farm Inputs 
‘Case Study of Wheat Production in Canterbury, New Zealand’.” Environmental 
Pollution 171: 126–32. https://doi.org/10.1016/j.envpol.2012.07.032. 

Sanjuan, Neus, Javier Ribal, Gabriela Clemente, and Ma Loreto Fenollosa. 2011. 
“Measuring and Improving Eco-efficiency Using Data Envelopment Analysis: A 
Case Study of Mah’on-Menorca Cheese.” Journal of Industrial Ecology 15 (4): 614–28. 
https://doi.org/10.1111/j.1530-9290.2011.00347.x. 



[95] Khangar and Thangavel 

 

Seiford, Lawrence M, and Joe Zhu. 2002. “Modeling Undesirable Factors in 
Efficiency Evaluation.” European Journal of Operational Research 142 (1): 16–20. 
https://doi.org/10.1016/S0377-2217(01)00293-4. 

Song, Jianfeng, and Xiaonan Chen. 2019. “Eco-efficiency of Grain Production in 
China Based on Water Footprints: A Stochastic Frontier Approach.” Journal of 
Cleaner Production 236: 117685. https://doi.org/10.1016/j.jclepro.2019.117685. 

Stoop, WA. 1987. “Adaptation of Sorghum/Maize and Sorghum/Pearl Millet 
Intercrop Systems to the Toposequence Land Types in the North Sudanian Zone 
of the West African Savanna.” Field Crops Research 16 (3): 255–72. 
https://doi.org/10.1016/0378-4290(87)90064-5. 

Syp, Alina, Antoni Faber, Magdalena Borzęcka-Walker, and Dariusz Osuch. 2015. 
“Assessment of Greenhouse Gas Emissions in Winter Wheat Farms Using Data 
Envelopment Analysis Approach.” Polish Journal of Environmental Studies 24 (5): 
2197–203. https://doi.org/10.15244/pjoes/39682. 

Taki, Morteza, Farshad Soheili-Fard, Abbas Rohani, Guangan Chen, and Hasan 
Yildizhan. 2018. “Life Cycle Assessment to Compare the Environmental Impacts 
of Different Wheat Production Systems.” Journal of Cleaner Production 197 (1). 
https://doi.org/10.1016/j.jclepro.2018.06.173. 

Tayefeh, Mandana, Seyyed Mustafa Sadeghi, Seyyed Ali Noorhosseini, Jacopo 
Bacenetti, and Christos Damalas. 2018. “Environmental Impact of Rice Production 
Based on Nitrogen Fertilizer Use.” Environmental Science and Pollution Research 25 (16): 
15885–95. https://doi.org/10.1007/s11356-018-1788-6. 

Toma, Elena, Carina Dobre, Ion Dona, and Elena Cofas. 2015. “DEA Applicability 
in Assessment of Agriculture Efficiency on Areas with Similar Geographically 
Patterns.” Agriculture and Agricultural Science Procedia 6: 704–11. 
https://doi.org/10.1016/j.aaspro.2015.08.127. 

Tyteca, Daniel. 1996. “On the Measurement of the Environmental Performance of 
Firms: A Literature Review and A Productive Efficiency Perspective.” Journal of 
Environmental Management 46 (3): 281–308. 
https://doi.org/10.1006/jema.1996.0022. 

Vetter, Sylvia H, Tek B Sapkota, Jon Hilier, Clare M Stirling, Jennie I Macdiarmid, 
Lukasz Aleksandrowicz, Rosemary Green, Edward JM Joy, Alan D Dangour, and 
Pete Smith. 2017. “Greenhouse Gas Emissions from Agricultural Food Production 
to Supply Indian Diets: Implications for Climate Change Mitigation.” Agriculture, 
Ecosystems & Environment 237: 234-41. https://doi.org/10.1016/j.agee.2016.12.024. 

Yadava, Anup Kumar. 2017. “Current Status of Organic Farming and Role of 
Government Institutions in India.” Zenith International Journal of Multidisciplinary 
Research 7(9): 114–21.  

Zhang, Bing, Jun Bi, Ziying Fan, Zengwei Yuan, and Junjie Ge. 2008. “Eco-
efficiency Analysis of Industrial System in China: A Data Envelopment Analysis 
Approach.” Ecological Economics 68 (1–2): 306–16. 
https://doi.org/10.1016/j.ecolecon.2008.03.009. 



Ecology, Economy and Society–the INSEE Journal [96] 

 

Zhu, Joe. 1996. “DEA/AR Analysis of the 1988-1989 Performance of the Nanjing 
Textile Cooperation.” Annals of Operations Research 66: 311–35. 
https://doi.org/10.1007/BF02188949. 

Zyłowski, Tomasz, and Jerzy Kozyra. 2023. “Crop Cultivation Efficiency and 
GHG Emission: SBM-DEA Model with Undesirable Output Approach.” 
Sustainability 15 (13): 10557. https://doi.org/10.3390/su151310557. 


