Quantitative Evaluation of Sustainable Weed Management Adoption Using Principal Component Analysis: Empirical Evidence from Greek Arable Farmers ^†

Abstract

Environmental degradation combined with the need to ensure food security for a rapidly growing world population has prompted the adoption of Sustainable Weed Management Practices (SWMPs), which are expected to reduce crop losses while preserving natural resources. However, evidence shows that farmers are reluctant to adopt them, and European farming remains dependent on chemical herbicides. The objective of this study is to analyze the adoption of SWMPs in Greece by identifying common factors that may explain the information (variance) included in each of two initial sets of variables, separately describing the following: (i) factors that hinder the adoption of SWMPs in Greece; (ii) factors and strategies to promote the use of SWMPs in the country. To achieve this purpose, 121 farmers cultivating annual arable crops in the Region of Thessaly in Central Greece were surveyed by means of a structured questionnaire. Using Principal Component Analysis (PCA) to reduce both initial sets of variables and categorize farmers’ responses into two smaller sets of uncorrelated components (dimensions) without missing valuable information, the analysis yielded five factors that limit the adoption of SWMPs (“Costs and availability of resources”; “Environment and land ownership”; “Compatibility and easiness of use”; “Economic performance”; “Social capital and education”) as well as four factors to promote their use (“Policy, research and Cooperatives”; “Training and mandatory regulations”; “Technology and networking”; “Targeted approaches”). The derived factors can be used in terms of policy objectives, as each dimension represents different aspects to be considered when developing effective strategies and integrated policies for the evolution and further expansion of SWMPs.

1. Introduction

Increasing yield losses caused by weeds and environmental pollution induced by the excessive use of chemical herbicides have prompted the adoption of sustainable weed management [1], which involves a wide range of options aimed at minimizing weed pressure [2]. This study refers to these options as Sustainable Weed Management Practices (SWMPs), which include crop rotation and crop diversification, changes to sowing dates, sowing density adjustment, the selection of competitive crops and cultivars, false seeding, reduced tillage, cover crops and mechanical treatment as well as new technologies and precision agriculture tools [3]. Combined with targeted herbicide applications, SWMPs can be used effectively to achieve decent crop yields without harming the environment and the provision of ecosystem services [4].

SWMPs have been promoted by the European Union (EU) since 2009 through Directive 2009/128/EC. Nevertheless, their adoption has been limited within European countries—including Greece—due to farmers’ reluctance to switch to non-chemical methods, while herbicides are still considered an easier solution [5]. Indeed, farmers’ decision to adopt sustainable farming practices is a challenging process, as the transition from conventional to sustainable agriculture involves high costs as well as knowledge and behavioral changes for farmers and consumers, which can be barriers that impede its adoption [6].

This study is motivated by the need to achieve a higher adoption rate of SWMPs within Greek agriculture, while taking into consideration that shifting toward sustainable practices constitutes a complicated task. Using data from a questionnaire survey of farmers, this study applies Principal Component Analysis (PCA) to reduce two initial sets of variables describing factors hindering the adoption of SWMPs as well as factors and strategies to overcome these barriers into two smaller sets of uncorrelated components. This procedure is crucial for policy purposes, as the extracted factors can serve as a starting point when designing measures for more widespread SWMP adoption in the country.

2. Materials and Methods

2.1. Survey Profile

This study provides the results of an on-site survey of farmers—engaged in the cultivation of annual arable crops such as cotton and cereals—conducted in the Region of Thessaly in Central Greece. The plain of Thessaly ranks among the largest agricultural plains of the country (covering 15% of Greece’s total cultivated area) and is dominated by arable farming (75% of Thessaly’s total cultivated area) [7]. Data were collected by means of a structured questionnaire aimed at recording farmers’ viewpoints, perspectives and perceptions regarding SWMPs. In particular, the questionnaire contained two sets of questions: (i) factors that hinder the adoption of SWMPs in Greece; (ii) factors and strategies to promote the use of SWMPs in the country. For both sets of questions, a 5-point Likert scale ranging from 1 (=not at all/totally disagree) to 5 (=very much/totally agree) was used. The questionnaire also recorded farmers’ basic sociodemographic characteristics such as gender, age, education level, years of experience, and source of family annual income. The questionnaire items were developed based on the existing literature [8,9,10,11,12,13,14] and modified to fit our study’s research context.

A total of 140 farmers were approached from September 2022 to June 2023, out of which 121 responded anonymously to the questionnaire. Prior to the main survey, 20 farmers from the study area were interviewed as a part of a pilot survey, which was undertaken to ensure the clarity of all questionnaire items and measurement scales. During the pilot survey, minor wording revisions were suggested, with these farmers then being excluded from the main survey.

2.2. Methodological Approach and Data Analysis

In order to examine the internal validity of the survey variables, a Categorical Principal Component Analysis (CatPCA) was employed to analyze farmers’ attitudes regarding factors that hinder the adoption of SWMPs as well as strategies for their future expansion. As a commonly used tool in multivariate analysis [8,10,15], this method aims to reduce an initial set of variables and classify participants’ responses into a smaller set of uncorrelated components (dimensions) that explain the information (variance) included in the initial set [16]. A correlation coefficient (factor loading) is estimated in each dimension for each variable of the initial set, and the value of the coefficient constitutes the criterion under which the dimensions are determined. In particular, a factor loading above 0.7 suggests high correlation between the variable and the specific dimension; nevertheless, in social sciences, lower loadings can also be accepted. Therefore, each dimension can be characterized according to the variables with the highest loadings. Overall, the dimensions play the role of new variables that allow further analysis using fewer variables while maintaining valuable information. The analysis in this paper was carried out using the statistical package SPSS 24.

3. Results

3.1. Descriptive Statistics

Table 1. Sample characteristics.

Characteristic	Category	Frequency (N)	Percentage (%)
Gender	Female	9	7.4
	Male	112	92.6
Age	20–29	10	8.2
	30–39	23	19.0
	40–49	33	27.3
	50–59	34	28.1
	>60	21	17.4
Education level	Primary education	13	10.8
	Secondary education	43	35.5
	Technical after-school education	35	28.9
	Bachelor’s degree or equivalent	18	14.9
	Master’s degree/Doctoral	12	9.9
Years of experience	<5 years	10	8.2
	6–10 years	33	27.3
	11–20 years	26	21.5
	>20 years	52	43.0
Source of income	≥50% from off-farm sources	9	7.4
	≥50% from the farm	38	31.4
	100% from the farm	74	61.2

summarizes the sociodemographic characteristics of the surveyed farmers. Out of 121 respondents, 67 (55.4%) were between 40 and 60 years of age, while male participants accounted for the vast majority (92.6%). Most of the respondents (89.2%) had completed secondary education or higher and a total of 78 (64.5%) respondents had more than 10 years of farming experience. Off-farm activities were the main source of family annual income only for a small proportion of individuals (7.4%).

3.2. Multivariate Analysis

Table 2. Rotated Categorical Principal Component Analysis (CatPCA) table: loadings of variables describing factors that hinder the adoption of Sustainable Weed Management Practices (SWMPs).

	Dimensions
	Component Loadings *
	1	2	3	4	5
Small farm size	0.413	−0.186	−0.376	0.048	0.291
High adoption cost	0.766	0.168	−0.005	0.106	−0.266
Uncertainty about their environmental impact	0.068	0.695	0.009	−0.140	−0.017
Lack of trust in their effectiveness	0.251	0.275	−0.012	0.328	0.606
Lower profits compared to conventional practices	−0.098	−0.274	−0.164	0.557	0.320
Low availability of labor (family and hired)	0.695	−0.244	0.122	−0.219	−0.008
Lack of income from off-farm activities	0.166	0.032	−0.285	0.736	−0.144
Compatibility with local norms	0.014	0.356	0.504	0.372	−0.139
Farmer low experience	0.038	−0.332	0.626	−0.103	0.266
Farmer low education level	−0.088	−0.375	−0.211	0.174	−0.701
High proportion of rented agricultural land	−0.030	0.584	−0.461	−0.196	−0.054
Inadequate training and advisory support	0.848	−0.012	0.121	−0.052	−0.122
Complicated use or high technical requirements	−0.108	0.261	0.546	0.332	−0.231
Cronbach’s-α (rotated matrix)	0.564	0.382	0.351	0.294	0.249
Eigenvalue	2.086	1.545	1.479	1.372	1.298
% of variance explained	16.05	11.88	11.38	10.56	9.98
% of variance explained (Total)			59.85

* Varimax method with Kaiser Transformation: component loadings in bold are the highest and most important in each dimension.

demonstrates the results of the CatPCA on the variables describing factors that limit the adoption of SWMPs within the Greek agricultural sector. In total, 13 variables were grouped into five dimensions with eigenvalues higher than 1, explaining 59.85% of the total variance. Dimension 1.1 was characterized as “Costs and availability of resources”, as it included four variables with high loadings describing barriers such as a high adoption cost, small farm size, low availability of labor and inadequate training and advisory support. Dimension 1.2 was named “Environment and land ownership”, as the two variables with the highest loadings were associated with farmers’ uncertainty regarding the environmental impact of SWMPs and a high proportion of rented agricultural land. Dimension 1.3, “Compatibility and easiness of use”, described constraints related to farmers’ low experience and SWMP complexity as well as compatibility with local norms and was therefore named accordingly. In Dimension 1.4, the highest loadings were detected for two variables which referred to economic performance (lower profits compared to conventional practices and lack of income from off-farm activities). Thus, Dimension 1.4 was titled “Economic performance”. Finally, in Dimension 1.5, two variables had the highest loadings describing barriers such as a lack of trust in the effectiveness of SWMPs and farmers’ low education level. Therefore, it was named “Social capital and education”.

Table 3. Rotated Categorical Principal Component Analysis (CatPCA) table: loadings of variables describing factors and strategies to promote the use of Sustainable Weed Management Practices (SWMPs).

	Dimensions
	Component Loadings *
	1	2	3	4
More incentives to farmers (income support payments)	0.548	0.536	−0.207	0.004
Mandatory regulations—Critical thresholds for herbicide use	0.021	0.722	0.148	−0.178
Better technical/advisory support	0.096	0.815	−0.169	0.095
Better technology (more efficient practices)	0.320	−0.010	0.767	−0.029
On-site demonstrations	0.198	0.220	−0.170	0.765
Policies and measures tailor-made for specific regions	0.784	−0.136	0.143	−0.041
Shift to specific crops and varieties	−0.202	−0.358	−0.083	0.563
Increased awareness and education for the general public	0.949	−0.134	−0.111	0.004
More research on weed management	0.949	−0.122	−0.112	−0.008
Establishment of Cooperatives	0.797	−0.167	0.060	0.032
Better collaboration between farmers and other stakeholders	0.073	−0.229	−0.689	−0.357
Cronbach’s-α (rotated matrix)	0.790	0.483	0.214	0.076
Eigenvalue	3.549	1.782	1.242	1.074
% of variance explained	32.26	16.20	11.29	9.76
% of variance explained (Total)	69.51

* Varimax method with Kaiser Transformation: component loadings in bold are the highest and most important in each dimension.

reports the results of the CatPCA on the variables explaining the enabling factors and strategies toward the adoption and expansion of SWMPs in Greece. The 11 variables included in the initial set were grouped into four dimensions with eigenvalues higher than 1, explaining 69.51% of the total variance. Dimension 2.1 was named “Policy, research and Cooperatives”, as the five variables with the highest loadings focused on policy actions (more incentives to farmers, policies and measures tailor-made for specific regions and increased awareness and education for the general public) and more research on weed management as well as the establishment of Cooperatives. In Dimension 2.2, the highest loadings were reported for variables that referred to the role of technical/advisory support and the enactment of mandatory regulations involving critical thresholds for herbicide use. Thus, it was titled “Training and mandatory regulations”. Dimension 2.3 was named “Technology and networking”, as the variables with the highest loadings described the need for better technology through the development of more efficient practices, but also the need for better collaboration between farmers, scientists and other agricultural stakeholders. In Dimension 2.4, two variables had the highest loadings, both related to targeted approaches (on-site demonstrations and shift to specific crop species and varieties). Therefore, it was named “Targeted approaches”.

4. Discussion

Based on farmers’ responses, the analysis yielded five factors that limit the adoption of SWMPs in Greece as well as four factors to promote their use in the country. The derived factors in both categories can be used to analyze the adoption patterns of SWMPs and characterize specific barriers and key future challenges. This could be particularly important in terms of policy objectives, as each dimension represents different aspects to be considered when elaborating effective strategies and integrated policies for the evolution and further expansion of SWMPs. The main axes for strategic development should include specific policy measures and research initiatives, training and advisory services combined with mandatory regulations and better collaboration between farmers, agronomists and scientists to improve the efficiency of SWMPs as well as targeted approaches. A combination of the aforementioned strategies is necessary to overcome adoption barriers related to SWMP complexity, their economic and environmental performance, the availability of resources and farmers’ education.

Actually, SWMPs can play a crucial role in the context of the new Common Agricultural Policy (CAP) of the period 2023–2027. Indeed, eco-schemes, as a part of the new CAP, are designed to promote agricultural practices that can facilitate the achievement of the EU’s Green Deal targets. Thus, CAP reform offers SWMPs an opportunity to contribute toward meeting one of these major goals, namely, to reduce by 50% the overall use and risk of chemical pesticides by 2030 [17].

5. Conclusions

Despite the need to shift toward sustainable agriculture, the adoption of SWMPs remains limited in Greece. This study found five adoption barriers of SWMPs (“Costs and availability of resources”; “Environment and land ownership”; “Compatibility and easiness of use”; “Economic performance”; “Social capital and education”) as well as four strategies to promote their use (“Policy, research and Cooperatives”; “Training and mandatory regulations”; “Technology and networking”; “Targeted approaches”). The findings provided here aim to offer directions for strategic and policy design toward a more expanded use of SWMPs in the country.

Due to the fact that this study focused on a sample of arable farmers in a specific region, the results may not be generalizable to broader farmer populations in Greece, indicating a limitation. Future research should consider the expansion of the study area by including farmers from all over the country who are involved in the cultivation of other crops. Lastly, the sample size was rather small, but nonetheless sufficient to produce accurate results and provide valuable insights into the adoption of SWMPs in Greece.