https://orcid.org/0000-0003-1602-4819
Figures
Abstract
Rapid uptake of greenhouse gas (GHG) mitigation measures is central to reducing agricultural and land use emissions and meeting the UK Net Zero policy. The socioeconomic challenges and barriers to uptake are poorly understood, with yet unclear structural pathways to the uptake of GHG mitigation measures. Using an online survey of 201 agricultural land managers across the UK, and applying multiple linear regression and stepwise regression analysis, this research established farm and farmers’ factors influencing perceptions and willingness to adopt GHG mitigation measures. The results consistently show that farm sector, farmers’ business perception, and labour availability influence willingness to adopt GHG mitigation measures. Based on the farmers’ qualitative feedback, other barriers to adoption include costs and concerns for profitability, lack of flexibility in land tenancy contracts, poor awareness and knowledge of the application of some GHG mitigation measures, perception about market demand e.g bioenergy crops, and scepticism about the future impacts of adopting varying GHG mitigation measures. In the midst of the ongoing net zero transition, this study identifies existing barriers to the uptake of GHG mitigation measures, and specifically, a substantial gap between farmers and the science of GHG mitigation measures and the need to incentivise a farm and farming community-led policy interventions to promote adoption of GHG mitigation measures.
Citation: Jebari A, Oyetunde-Usman Z, McAuliffe GA, Chivers C-A, Collins AL (2024) Willingness to adopt green house gas mitigation measures: Agricultural land managers in the United Kingdom. PLoS ONE 19(7): e0306443. https://doi.org/10.1371/journal.pone.0306443
Editor: Muhammad Aamer Mehmood, Government College University Faisalabad, PAKISTAN
Received: January 15, 2024; Accepted: June 18, 2024; Published: July 8, 2024
Copyright: © 2024 Jebari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: https://doi.org/10.23637/rothamsted.99058.
Funding: The first Author - AJ, was financially supported by the Science Initiative Catalyst Award (SICA) programme, a Rothamsted Research internal UKRI (UK Research and Innovation) Biotechnology and Biological Sciences Research Council (UKRI-BBSRC) award. GM and ALC acknowledge funding from Rothamsted Research’s Institutional Strategic Programmes ‘Soil to Nutrition’ (S2N) supported by UKRI-BBSRC BBS/E/C/000I0320 & BBS/E/C/000I0330 and ‘Resilient Farming Futures’ supported by UKRI-BBSRC BB/X010961/1. There was no additional external funding received for this study.
Competing interests: The authors declare no known competing interests that could have appeared to influence the work reported in this paper.
1.0 Introduction
Agriculture in the UK accounts for ∼71% of land use, of which grassland and crop production constitute ∼72% and 26%, respectively [1]. The agricultural sector accounts for 11% of total greenhouse gas (GHG) emissions wherein ∼58% of these GHG are generated by livestock in the form of CH4 from enteric fermentation (eructation primarily) and manure management (i.e., storage and subsequent application as organic fertilisers). The varied impacts of GHG emissions in the UK include increasing incidences of drought associated with changing weather and climate [2], distributions of pests and diseases [3], and the occurrence of invasive weeds and non-native species [4], which directly or indirectly impact agricultural productivity, nutrition and fluctuations in agricultural commodity prices [5]. Since agriculture and land use make an important contribution to GHG emissions, improved mitigation in this sector is central to achieving the UK Net Zero policy objectives in 2050 (CCC 2020) [6], and more specifically, in accordance with the UK Committee on Climate Change (CCC), a 64% reduction in GHG emissions from the agriculture and land-use sector (CCC 2020). Policymakers, therefore, face the challenge of developing and implementing effective GHG abatement strategies for agriculture.
Numerous mitigation strategies, both extant and at the prototype stage, are being proposed to tackle climate risk through reducing and/or offsetting emissions and improving production efficiency [7]. Whilst the technically feasible environmental impacts of available and emerging mitigation measures have been reported in existing literature, corresponding implications for energy, food security, and especially farmer attitudes towards the adoption of improved practices have received scant attention [8]. Nonetheless, understanding attitudes, awareness and intentions to change current practices is one of the critical indicators of the uptake of GHG mitigation measures.
Information on the attitudes of agricultural land managers towards different mitigation measures is needed, as agricultural land managers are the ones who ultimately make most of the management decisions for 71% of the UK land area [9]. Understanding how agricultural land managers perceive GHG mitigation measures and how this affects their willingness to adopt them is critical for developing effective climate change response strategies for the agricultural sector. Our study enriches existing empirical evidence [10–12] by undertaking an assessment using an extensive survey comprising key information surrounding mitigation measures (e.g., economic, energy, food security) to address the following objectives: i) understanding farmers’ willingness to adopt GHG mitigation measures, and ii) assessing the critical factors for predicting farmers’ willingness to adopt GHG mitigation measures. In the next section, we discuss the overview of GHG mitigation measures considered in this study, their importance and prospects for tackling climate risk.
2.0 Brief overview of GHG mitigation measures and impacts on emissions reductions and soil organic carbon
Climate change is caused by a range of gases collectively known as GHGs and these are defined as gases that absorb and re-emit heat with a continuous warming effect on the planet’s atmosphere [13]. Nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2) are common emissions from agriculture and land use activities and, respectively, contribute 14.5%, 24.8% and 5.5% to the total UK agricultural emissions [14]. Reducing measures involves maintaining or enhancing existing efficacy for controlling emissions potential. Grasslands are, for example, home to a range of flora and fauna and have the potential to improve biodiversity and reduce GHG emissions due to their high carbon sequestration potential [15]. Certain factors such as land use change and intensification management threaten the multifunctionality of grassland areas. A typical example is the conversion of grassland to cropland for livestock grazing, this usually leads to intensive grazing which affects the depletion of above-ground biomass and the rate and extent of pollination [16]. Also, poor or suboptimal, fertiliser management and livestock performance, on the other hand, can lead to excessive losses of GHG including N2O and CH4 both of which are powerful compounds thought to be around 27 (biogenic CH4) and 270 times (N2O) more warming than carbon dioxide (CO2), respectively [17–19].
Grazing management practices, such as reducing livestock density and application inputs (e.g., nitrogen fertilisation), have significant impact in reducing GHG emissions by up to 65% [20]. Besides impacting emission reductions, grazing management tools such as rotational grazing and herbal ley practices can significantly increase herbage dry matter production and improve animal daily live-weight gain [21].
Enteric fermentation emissions (CH4) and manure management emissions (CH4 and N2O) from livestock production account for up to 231.3, 32.3 and 18.3 Mg of CO2 eq. yr-1 on dairy, sow and pig farms, respectively [22]. Traditional management of slurry and farmyard manure relies on an agronomic approach that is odour-offensive and poses a risk to waterbodies through direct water pollution, emissions of GHG and release of faecal contamination which can endanger human and animal health [23]. Anaerobic digestion technologies provide solutions to traditional slurry management problems (waste and manure management) and are also beneficial for energy generation [24]. For manure management options, anaerobic digestion accelerates the degradation process of manure without negatively impacting the environment [25] and biologically breaks down organic matter into biogas, water and residual matter (digestate) [26]. Biogas is a renewable energy source and its CH4 composition allows its use as fuel [27] and digestate is suitable for use as a fertiliser or soil conditioner [26]. Anaerobic digestion can reduce farm GHG emissions by up to 44% [27]; there are, however, poor incentives for the widespread adoption of manure-based farm-scale anaerobic digestion in the UK [27].
Emission intensity from livestock production contributes to the aggregate agricultural GHG emissions e.g., Methane (CH4) and Ammonia (NH3) ‐ Methane emissions are primarily generated through enteric fermentation of livestock feed such as grazed grass and nutritional produce, and NH3 emissions are generated from urine and faeces [28]. Addressing livestock emissions targets using feeding and breeding practices, can include adapting nutritional strategies that reduce enteric CH4 emissions through the use of feed additive supplementation, such as dietary lipids and nitrates which in the specific case of supplements can reduce CH4 enteric emissions by up to 45% [29]. Feeding supplements when incorrectly adapted can induce a risk of toxicity and impair animal performance.
Soil amendments are crucial to enhancing soil fertility through improving soil organic carbon (SOC) for plant growth and agricultural productivity [30]. Soil amendments (e.g., basalt amendment, biosolids) enhance SOC accumulation by up to 17% in the case of biosolids applications, and 4-fold with basalt rock dust amendment [31,32], and can substantially increase crop yield over what inorganic fertiliser achieves [33]. There are, however, contrasting reported effects arising from organic amendments; for example, the application of varying organic amendments (rapeseed meals, soybean meals and cattle manure) to winter wheat and summer maize shows that soybean meal and cattle manure were more effective at improving soil quality than rapeseed meals [34], Also, in a long-term experiment, organic amendments were found to be slow in affecting yield when applied solely, compared to inorganic fertilisers, but performed excellently when partially combined with inorganic fertilisers [35]. Application of soil amendments, despite their importance, can pose risks to health and the environment; this is because soil amendments contain a range of pollutants and involve the use of microplastics which can have adverse effects on the environment [36].
Planting cover crops and trees are typical offsetting measures to enhance SOC [37]. Cover crops benefit the soil and the environment by reducing nitrogen losses, enhancing water retention, and preventing soil erosion and sediment loss [38], and can also be reimagined as bioenergy crops and serve as sources of renewable natural gas production and carbon storage [39]. Similarly, trees and hedgerows on agricultural land contribute to microclimate amelioration, providing shelter for livestock, and improving the conservation of soil and water [40–45]. In addition, agroforestry is effective in reducing soil erosion by over 50% [46] and can promote carbon sinks and GHG mitigation across arid and semiarid regions [47]. The effects of agroforestry on carbon sinks can, however, vary based on the types; for example, between woodland and silvopastoral agroforestry systems, measures of above-ground carbon indicated an overall increase in SOC, while the silvopastoral system was predicted to achieve a higher level of carbon storage than equivalent areas of separate woodland and pasture [48]. Although the high initial investment is an identified limiting factor, the economic and environmental benefits of agroforestry are numerous and include product diversification (e.g., food, recreation, leisure, foliage, biochar, etc) [49].
Bioenergy crops provide multiple functions through limiting the emissions of GHG whilst also acting as sources of renewable energy. Bioenergy crops include feedstocks (e.g., oil seeds, wheat, maize) and non-feedstock crops (e.g., Miscanthus, Willow crops) used to produce liquid biofuel and biogas through an anaerobic digestion approach. In the UK, Miscanthus is an example of a dedicated energy crop which is non-invasive, with low production costs (low to no addition of fertiliser) for producing energy and improving removal of GHG emissions. Bioenergy is the second largest source of renewable energy in the UK and a viable sustainable alternative to conventional fossil fuel energy sources as it improves energy security [50–52]. The biodiversity impacts of bioenergy crops include habitat, soil and water quality improvement, support for climate regulation [50–53], and improved farm-scale biodiversity on agricultural land [54]. The impact of bioenergy crops on attaining the Net Zero target is well established and it is context-dependent [55]. Non-food bioenergy crops, for example, directly compete with agricultural and ecosystem services and thus emphasise the importance of landscape contexts in scaling bio-energy crops [56]. Although there is some evidence concerning farmers’ preferences and contentment with the economic and environmental benefits of bioenergy crops [57], geographically limited markets due to poorly distributed biomass processing stations and high transportation costs represent barriers to the adoption of bioenergy crops in the UK, also, the use of bioenergy crops is associated with increased claims for land use [58,59].
With varying evidence of the impact of GHG mitigation measures, understanding approaches to scaling adoption is critical. This study contributes to such discussions and provides insight into existing gaps. The next section highlights the examples of factors promoting/limiting the adoption of GHG mitigation strategies as described by past studies; this is followed by a description of the data and estimation strategy adopted in this study.
3.0 Summary of past studies on adoption of mitigation strategies
Mitigation and adaptation practices are the two main responses to climate change recognised by the United Nations Framework Convention on Climate Change (UNFCCC); essential in reducing expected climate change impact on the environment [60]. Across the Global North, mitigation measures are becoming popular as effective approaches to reaching Net Zero policy strategies [61–63]. Identified constraints across economic, innovation-diffusion, and adopters’ perception paradigms to adoption are comprehensively defined [64]. Economic constraints are, for example, one of the prominent limitations; this is because emission reduction practices can have either a positive or negative impact on farmers’ income [65]. Also, farmers’ willingness to adopt is economically incentivised and transitioning happens only if practices are perceived to be more cost-effective than the current practices, they are adopting [66] For capital-intensive mitigation practices, profitability and long-term pay-off periods are major constraints to adoption [67].
In the adoption literature, complexities in the adoption processes of agricultural innovations are subjective [68], and are defined by adopters’ perceptions considering their knowledge, behaviours, beliefs, and attitudes which are intrinsic to motivations to engage in emission reduction practices [68–72]. These factors share a similar stage in defining adoption and can independently, or jointly, define motives for adoption [71,72]. To highlight this, adoption is, for example, defined by farmers’ risk perception, level of risk aversion and the availability and knowledge of strategies to cope with varying risks [72,73]. Also, behaviours and beliefs about mitigation and sustainable agricultural practices are important contributors to farmers’ behavioural intention to adopt [74]. Beliefs are complex and vary considerably and according to a study are intuitively dependent on what farmers attribute their perception of climate change and mitigation practices to [75]. The roles of beliefs and behavioural factors are becoming popular and are found to be important in farmers’ decision-making [76]. Adopters’ perception includes personal and other physical characteristics defined by farm and farmers’ characteristics which can directly impact adoption decisions [77–79]. Constraints to adoption can, for example, include competing use of farm resources between practices, high demand for other resources such as labour, lack of adequate knowledge, and farmers’ unfamiliarity with technology [77]. Table 1 below highlights the examples of adoption studies on factors limiting/promoting the uptake of GHG mitigation measures.
4.0 Data and estimation strategy
4.1 Data
This study utilised a cross-sectional online survey to sample 201 farm households across different farm systems in the UK, to understand perceptions for adoption of GHG measures and the corresponding predictive factors. It included a range of question types such as Likert-scale, multiple choice, and free-text. The survey questions spanned different GHG mitigation measures categorised into reduction measures, offsetting measures and bioenergy crop production. Each of these measures was described in the survey, including their potential impacts on mitigating GHG emissions (see Table 2). The measure of farmers’ willingness to adopt GHG mitigation was described based on a 5-point scale as follows: 1 = very unwilling; 2 = quite unwilling; 3 = neutral, 4 = quite willing and 5 = very willing. The survey data was followed up with open and closed questions to understand more underlying details of farmers’ adoption decisions. Here, questions were included on farm and farmers’ characteristics such as farm sector, farm size, tenure status, number of full-time equivalent workers, farming years of experience, age group, farmers’ perception of business performance, motivation to uptake friendly practices, and measures of willingness to adopt GHG reduction measures. The survey design, supported with consent forms and participant information sheets underwent review and ethical clearance was awarded by the Countryside and Community Research Institute, University of Gloucestershire ethical research committee.
The survey was advertised publicly in the JISC online survey and was live between 02/12/2022-31/01/2023. Conducting the survey during the winter months was beneficial for maximising participation as it is often the quietest time during the farming calendar. Participants were recruited using a paid-for advertisement in a mainstream agricultural news outlet in the UK (Agricultural Land Managers Weekly), through postings across various social media platforms, the Farming Forum, and Word-of-mouth. Participation was incentivised with a £100 voucher prize draw and written/signed consent was obtained from participants.
One limitation of publicly advertised online surveys with a prize draw incentive is the risk of inaccurate or duplicate responses. The data resulting from the survey was therefore critiqued by the research team, with any entries deemed as duplicates or inconsistent removed from the analysis. This resulted in 56 removals, leaving a remaining sample size of 201. The sample data (n = 201) covered the cereals, dairy, general cropping, grazing livestock (less favoured areas), horticulture, lowland grazing livestock, mixed cropping and specialist pigs and poultry sectors across England, Wales and Scotland. The resulting data were largely quantitative, including some discussions in the optional free-text boxes which were fundamental in understanding the underlying factors driving farmer attitudes towards adoption of GHG mitigation measures.
4.2 Estimation strategy
4.2.1 The dependent variable.
In line with existing studies, and specifically [84,85], the dependent variable was derived as the mean scores of scaled responses across all GHG mitigation measures for each respondent. Farmers’ willingness to adopt GHG mitigation measures was described based on a 5-point scale as follows: 1 = very unwilling; 2 = quite unwilling; 3 = neutral, 4 = quite willing and 5 = very willing. This was used to estimate the mean willingness to adopt score for each respondent. For a robust estimate, we further estimated a second dependent variable using Principal Component Analysis (PCA). PCA scores were generated from ranking and the corresponding relative significance of GHG mitigation measures and willingness scales. The PCA scores were estimated using ‘pca’ code in STATA. This estimation generates Eigenvalues considering standardised weights of each GHG mitigation measure as follows:
(1)
where: I represents the identity matrix, λ is the vector of the eigenvalues and W is a p × p dimension matrix containing standardised weight values wij of each GHG mitigation measure.
A total of 14 eigenvalue components were generated. Table 3 provides descriptive statistics of those principal components with eigenvalues of more than 1 (six components). The coefficient in each component shows the percentage contribution of corresponding variables; for example, the first principal component (PC1) explains a contribution of 14.94% for the corresponding GHG mitigation measures. Figs 1 and 2 illustrate the scree plots showing the mean eigenvalues and the corresponding 95% confidence intervals, respectively. Fig 3 shows a matrix of score variables across the six components. The strength of distribution and commonality of components is illustrated by the degrees of clustering as shown across the matrix. The plotting of the first and second components is, however, considered more important since they have the highest explanatory power.
Further, the PCA scores were generated from the weights of variables in each component and standardised variable as follows:
(2)
where j = 1, 2,…n, is the number of observations; k = 1, 2…q, is the number of selected component numbers; p is the number of independent variables; skj is the standardised score value of jth observations in kth components; Zpj is the standardised value of pth variable of jth observation, calculated from
, where xp is the original value of the pth variable, and; tpk the standardised weight of the pth variable in the kth components [86].
Having estimated our dependent variables (Mean willingness score of farmers to adopt and PCA scores (component one) of willingness to adopt GHG mitigation measures), the kernel distribution of both dependent variables is further presented in Figs 4 and 5. The density distribution of the Mean willingness score shows similar skewness to the PCA scores.
To understand the structure of the data collected, we employed descriptive statistics (percentage means and cross-tabulations) (see attached S1 Table), and a test of means (t-test). To assess the factors determining farmers’ willingness to adopt GHG mitigation measures, we modelled a multiple regression framework where the dependent variables (mean willingness to adopt score and PCA score) are respectively modelled as a function of farm and farmers’ characteristics.
4.2.2 Analytical framework for determining key factors for farmer willingness to adopt GHG mitigation measures.
The multiple regression analysis was adapted to establish the relationship between farmers’ willingness to adopt GHG measures and the explanatory variables. The willingness to adopt GHG measures was modelled as a function of explanatory variables and expressed as follows:
(3)
where: yi respectively represents the mean score of farmers’ willingness to adopt GHG measures and PCA scores of farmers’ willingness to adopt GHG mitigation measures, β1…βn represents the vectors of farmers’ socioeconomic and farm characteristics (X1…Xn) to be estimated, β0 is the random intercept, and εn is the random error term.
Although linear regression is effective in establishing a relationship between the dependent and independent variables, it is highly susceptible to multicollinearity and high correlation errors. We further tested the significance of estimates in our main regression model by applying stepwise regression models [87,88]. To reduce the bias in model selection in the stepwise regression, we applied the ‘vselect’ code in Stata 16 to select the best model [89]. The ‘vselect’ ‐ performs variable selection for linear regression through the use of the Furnival-Wilson leaps-and-bounds algorithm [90]. The criteria for best model and variable combinations were measured by the significance of the following criterion: the R^2 adjusted, Mallows’s C, Akaike’s information criterion, Akaike’s corrected information criterion, and Bayesian information criterion for the best regression model at each quantity of predictors [90]. We present the estimates of model selection in the attached S2 Table.
5.0 Results and discussion
5.1 Summary statistics of respondents’ socioeconomic characteristics
Table 4 presents the summary statistics of respondents’ socio-economic characteristics. On average, 72.6% of respondents were <50 years old and approximately 52% have more than 20 years of farm experience. The sample statistics further show that the agricultural land managers (84%) are highly educated and had varying farm businesses spanning crops and livestock farming constituting 28% cereal farmers, 14% livestock farmers, and 15.5% and 15% mixed and general cropping farmers, respectively. The mean farmed land area for each respondent was 146.80 ha, and the corresponding mean rented and owned land areas were 61.5 ha and 87.2 ha, respectively. The data include some details of existing land use management activities and GHG mitigation measures. For example, approximately 85% grow cover crops and 56% undertake anaerobic digestion, as a manure management practice. While 57.9% of respondents perceived their farm business to be doing quite well, 33.3% placed their farm business in the average category and only 8.8% perceived their farm business to be doing poorly. We further discuss the structure of socioeconomic variables in the attached supporting information (see S1 Table).
5.2 Spread of farmers’ mean willingness to adopt GHG mitigation measures
Table 5 illustrates the percentage distribution of respondents’ willingness to adopt GHG measures collapsed into three categorical measures ‐ 1 = unwilling, 2 = neutral, and 3 = willing. Across all the GHG mitigation measures considered, the descriptive results showed an overall higher percentage of farmers’ willingness, to adopt GHG mitigation measures. For example, in Table 4, 56% of respondents are willing to use anaerobic digestate, 25.7% and 16.6% were neutral and unwilling, respectively. In comparison, 68% of respondents were willing to introduce legumes, whereas 12.7% and 7% were neutral and unwilling, respectively.
Table 6 presents estimates of tests of the mean difference of mean willingness scores across farmers’ socioeconomic and farm characteristics using the independent sample t-test. The results show significant differences across variables except livestock ownership and farm experience (21–40 years). While the t-test shows possible relationships between farmers’ socioeconomic characteristics and willingness to adopt GHG mitigation measures, it does not fully establish causal effects. Causality and determining relationships are further discussed in the next section.
5.3 Estimating factors determining farmers’ willingness to adopt GHG mitigation measures
Table 7 presents estimates of multiple linear regression and stepwise regression for two outcome models: mean willingness to adopt scores and PCA scores (see method section). The multiple linear regression models show significant model results at p < 0.01, suggesting that the explanatory variables explain 46.4% and 50.2% of the variations in farmers’ mean willingness to adopt scores and PCA scores, respectively.
Certain predictors were consistently significant across both outcome models. Farm types were, for example, significant determinants of farmers’ willingness to adopt certain GHG mitigation measures. To highlight, agricultural land managers whose dominant production was cereal farming were willing to adopt GHG mitigation measures (p < 0.01); this result is consistent with agricultural land managers whose dominant agricultural practice is general cropping. In contrast, agricultural land managers whose dominant production is livestock grazing were significantly (p < 0.01) unwilling to adopt GHG mitigation measures. An explanation for this difference may be due to the perceived ease of user adoption of GHG reduction measures among cereal and general cropping farmers, compared with livestock production. For livestock farmers, similar findings have highlighted perceived difficulty for the adoption of innovations among livestock farmers in England and Wales [91]. Similarly, [92] reported that livestock farmers’ perception of difficulty in investing in animal health technology influenced their membership in specific technological uptake groups. Likewise, resource constraint beliefs were found to be significant determinants of dairy farmers’ adoption of best management grazing practices in Ireland [93].
Also, agricultural land managers whose farm worker input was less than 2.5fte were less likely to adopt GHG mitigation measures, compared to farm businesses whose worker inputs were >2.5fte; this was significant at p < 0.01. This result suggests that adopting GHG mitigation measures is highly labour-input dependent. In existing adoption literature, demand for labour is an established barrier to adoption of innovations in agriculture [94–97]. Given the persisting issues of labour shortage in the UK agri-food sector, this result offers insights into the current and long-term effect of labour supply changes on emission reduction policies.
The estimates further show that younger agricultural land managers below 50 years were positively and significantly (p < 0.05) more willing to adopt GHG mitigation measures, suggesting that younger agricultural land managers are more flexible and less risk-averse to taking up innovative approaches. A similar study [98] using a representative survey of Scottish farmers, found that younger agricultural land managers were more willing to afforest land. Contrary findings have suggested that the adoption of GHG mitigation measures is more prominent among older agricultural land managers but this was non-significant in some studies [99].
Table 8 presents stepwise regression estimates of the selected best model as described in the method section. In all presented models (except for PCA scores (six predictor model) where age is not significant), estimates are significant and in line with the multiple regression estimates in Table 6 and show the robustness of our regression estimations.
5.4. Understanding farmers’ willingness to adopt GHG measures–drawbacks and incentives
In addition to the statistical estimation explored above, this section provides insights into supportive explanations provided by agricultural land managers on various mitigation measures. We have attached more qualitative insights relating to individual GHG mitigation measures in the S3 Table.
The vital roles of agricultural land managers in protecting the environment and tackling climate change cannot be overemphasised and motivations to adopt GHG mitigation measures vary. From supportive comments, agricultural land managers in most cases are self-aware of the benefits of adopting GHG mitigation measures and this reiterates their responsibilities towards protecting the environment as citizens; a participant explained as follows:
Nitrogen fertiliser is our biggest contribution to GHG emissions and water pollution. I also believe that, if we could find alternative forms of crop nutrition, it could improve our crop health and reduce incidence of pests and diseases and our reliance on pesticides
(Anonymous respondent)
Very open to increasing biodiversity and we are forever gathering acorns, walnuts, pinecones etc and my wife grows the seeds on to whips which we plant. We take local seeds and multiply them up instead of buying in imported species.
(Anonymous respondent)
Also, when asked about their willingness to reduce emissions, some agricultural land managers, for example, articulated their role in cutting down on nitrogen fertilisers to protect the environment and improve water quality through the use of nitrification inhibitors as an important means of reducing farm GHG emissions and ensuring nitrogen use efficiency/reducing pollution. There are also indications of uptake of GHG mitigation measures such as conversion of arable land to pasture, installation of wind turbines, conversion of agricultural waste to bioelectricity, planting many trees and hedgerows and having already introduced mixed legumes into grassland/rotational grazing as explained by one participant:
I am reducing stocking density as more extreme weather is shortening my grazing season for various reasons. I am experimenting with agroforestry (nut trees) as I think it is important to produce nutrient dense foods in a sustainable way.
(Anonymous respondent)
On the other hand, there are indications that agricultural land managers are still largely in the decision-making phase. We summarise paraphrased responses based on each GHG mitigation measure (see S3 Table). Highlights of motivations and drawbacks to adoption are discussed below:
- Cost of adoption and concerns for profitability: Agricultural land managers identified cost as a limitation to the uptake of certain GHG mitigation measures. For example, in the case of anaerobic digestate, agricultural land managers are of the opinion that anaerobic digestate plants are too expensive for small-scale farmers, and where there is willingness to adopt, transitioning requires investment in lots of infrastructure and with low returns; a respondent explained as follows:
We are predominantly a cereal/veg farm, so to move to anaerobic digestate means a considerable investment in infrastructure for which the returns are not there. I would also question the green credentials of AD, is the carbon footprint of the infrastructure manufacturing taken into account anywhere? It is a very energy-intensive operation growing and harvesting the crop and I have serious doubts whether energy produced is greater than total energy consumed.
(Anonymous respondents)
Agricultural land managers showed willingness to try out approaches at a domestic level if the opportunity presented itself and if capital grants were available and existing evidence supported the scope for profit. Incentive policies are effective in promoting capital-intensive GHG mitigation measures, e.g. bioelectricity generation, given the refund of capital investment to a farm [100].
I’m probably not big enough or have enough slurry to make digestion viable in my own right. However, if capital grants were available to build a small-scale digester I’d consider hosting the digester & using slurry from my neighbours.
(Anonymous Respondent)
While cost could be a barrier towards initial investment in GHG mitigation measures, we find that the rising cost of some conventional approaches is driving the switch to GHG mitigation measures; this is especially true for nitrogen fertilisers as explained by some respondents.
We’ve already had to reduce due to price of fertiliser. It hasn’t been great for us this summer although some of that may be the drought. Not having enough fertiliser means we may not have enough food for our stock and could have to buy in from someone who does have enough Fert. I’m not sure that’s a great thing to do. Exploring how much we could reduce without adverse effects would be interesting.
(Anonymous Respondent)
We already have reduced N fertiliser use. We built a large slurry lagoon so that we can store more slurry and then make better use of it, thereby reducing the need for N fertiliser. This was a good move ahead of the increase in fertiliser prices! We are not large users of inorganic N at all, but we’re always looking to only use what we have to.
(Anonymous Respondent)
- b. Uncertainties, and scepticism: From agricultural land managers’ opinions, uncertainties and scepticism about GHG mitigation measures are driven by a number of factors; this includes poor knowledge of use and understanding of short and long-term impact of adoption of GHG mitigation measures. To highlight, some agricultural land managers stated poor knowledge of the use of anaerobic digesters and were not convinced of the evidence on how they impact GHG emission reductions at the farm level. There were similar opinions about nitrification inhibitors, their use and their effect on soil types. We also found that Agricultural land managers’ uncertainties are driven by their beliefs and perceptions about certain GHG mitigation measures. Some agricultural land managers believe that having trees on their farms blocks the sunlight from crops, reduces the fertility of surrounding crops, and affects yield. Also, some agricultural land managers think that GHG mitigation measures are driven towards emission reduction targets with little or no concern about how it impacts profitability. In the case of reducing stocking density, agricultural land managers were of the opinion that although reducing stocking density in grassland management will impact emission reduction, such action may negatively affect their efficiency and profitability and result in poor performance of grassland as a result of undergrazing.
If I lose one, I reduce my income
(Anonymous Respondents)
Reducing organic stocking rates would have no effect on decreasing carbon, in fact, it would reduce carbon sequestration in my soil.
(Anonymous Respondents)
Also, there are conceptions about the long-term impact on certain GHG mitigation measures on the environment–application of biosolids for example, there are opinions that they contain microplastics as explained by some participants:
I don’t have cropped land. I wouldn’t take biosolids as I have concerns about microplastics.
(Anonymous Respondent)
Bio-solids are expensive, application can be problematic, and they contain microplastics, not something I would apply to my land. Acquiring something like basalt which is not a local product would cost more in the mining, processing and transport than the benefit it would result in.
(Anonymous Respondent)
Uncertainty about innovations is expected as farming is perceived to be risky, and adoption of a new technology is highly dependent on the farmers, farm and agronomic conditions among others [101]. From farmers’ discussions, there is need for on field participatory extension of knowledge and advice about GHG mitigation measures as lack of concern for climate change risk and absence of information are likely barriers to action and/or willingness to adopt GHG mitigation measures [102].
- c. Land contracts and market constraints: Land tenure status is a significant determinant of the adoption of agricultural innovations, especially, long-term soil-improvement technologies [103]. According to agricultural land managers, tenure arrangements are a major drawback to adopting GHG mitigation measures; an example is bioenergy crops. Some agricultural land managers stated that local attempts to adopt bioenergy crops are often unsuccessful due to restrictions in contracts and unwilling landlords.
I do not think landlord would consent to this.
(Anonymous Respondent)
Bioenergy crops are perennial crops and take longer to harvest, contradicting a need for quick returns on investment. Thus, promoting the adoption of GHG mitigation measures that are long-term may require improvements to land contracts to increase tenant-farmer stability. Motion to kickstart such an approach can be seen in the Environmental Land Management test and trial report for DEFRA which looked at farmer perceptions towards long-term agreements [104].
Besides land tenure drawbacks, agricultural land managers worry about geographically limited markets and costs of transportation in distributing biomass among processing stations. In line with a similar study reported by [57], agricultural land managers raised concerns about poor market availability, especially for bioenergy crops despite their economic and environmental benefits.
5.5 Limitations
While this study provides important insights into farmers’ perceptions of GHG mitigation measures, there are a few limitations that need to be acknowledged. Firstly, data collected are limited to a point in time with a small sample population which may not have captured the full variation of perception and willingness to adopt GHG measures in the UK. Secondly, this study focused largely on the broad framework of factors of adoption of GHG mitigation emissions within the different agricultural systems using a pooled outcome of perceptions; however, there are certain variations in farm systems and GHG mitigation measure attributes, including endogenous factors that may impact farmers’ adoption decisions; these were not considered. We acknowledge possible weakness in the adopted analytical method; especially the stepwise regression analysis which has potential pitfalls and biased parameter estimation due to inconsistencies in model selection. We tried to minimise this error by testing for the significance of the various models used (see method section and supporting file for details). These current limitations point to the need to extend this research in future studies. Regardless, this current study provides foundational insights into the attitudes towards the current uptake of GHG mitigation measures and the behavioural complexities and barriers to uptake. Our study limitations reiterate the need for further research on different GHG mitigation measures and their adoption constraints. There is the need for long-term assessment of behavioural change in the adoption of GHG mitigation measures using time-varying evaluation techniques to capture the complexities of decision-making in the adoption of innovations.
6.0 Conclusion and recommendation
Knowledge and drivers of farmers’ willingness to adopt different GHG mitigation measures is still limited in the UK. This study provides foundational insights into barriers towards adopting GHG mitigation measures in the agricultural sector, and one of the key highlights of this study is the distinct variations in adoption willingness across crop and livestock sectors, suggesting that policies may have to consider heterogeneities of farm sector needs to deliver rapid adoption of GHG mitigation measures. This study highlighted problems of limiting farm resource factors such as labour. Shortage of agricultural labour is an ongoing challenge in the UK, post departure from the European Union. Our research provides further evidence of the need for long-term impact evaluation of labour shortages on meeting reducing GHG emissions in the agricultural sector and meeting net zero policies. Our research evidenced the need for policy amendments to allow flexible land contracts, as this is central to the uptake of certain GHG mitigation measures, e.g., agroforestry practices and bioenergy crops. This land tenancy factor also extends to on-farm cost-intensive approaches, e.g., the use of anaerobic digesters and long-term soil and organic amendments that require long-term land contracts and flexibility in land use. As a result, a long-term policy promoting incentives may be more beneficial for sustainable uptake of GHG mitigating measures.
This research further underscored limitations associated with poor awareness and knowledge of GHG mitigation measures, including their impact on business-as-usual farming operations. Here, we found evidence of polarisation in knowledge and adoption of GHG mitigation measures, with some agricultural land managers indicating 100% awareness and transition in land use and adoption of GHG mitigation measures, and some respondents stating very limited awareness. There is a need for a mechanism to promote farm-level awareness and participation or on-farm demonstration of each GHG mitigation measure to improve willingness to adopt among farmers. In addition, cost of uptake is, in most cases, a critical barrier, and uncertainty about the outcomes of GHG mitigation measures prevails. Policy can help in varying ways; this can include support for on-farm experimentation by farmers to ease off initial set-up and to facilitate adoption. In addition, a landscape model to facilitate co-adoption is important and further research is needed to understand and design mechanisms that can facilitate co-adoption.
Supporting information
S1 Fig. Figure of the frequency distribution of willingness to try new measures for reducing GHG emissions.
https://doi.org/10.1371/journal.pone.0306443.s001
(DOCX)
S1 Table. Frequency distribution of agricultural land managers’ socioeconomic characteristics.
https://doi.org/10.1371/journal.pone.0306443.s002
(DOCX)
S2 Table. Test for stepwise regression predictors selection.
https://doi.org/10.1371/journal.pone.0306443.s003
(DOCX)
S3 Table. Quotes and paraphrasing of farmers’ explanation of willingness to adopt GHG mitigation measures.
https://doi.org/10.1371/journal.pone.0306443.s004
(DOCX)
References
- 1.
Department for Environment, Food and Rural Affairs Department of Agriculture, Environment and Rural Affairs (Northern Ireland) Welsh Government, Knowledge and Analytical Services The Scottish Government, Rural and Environment Science and Analytical Services.
- 2. Report S. The impact of climate change on severe droughts. 1800.
- 3. Wainhouse D, Inward DJG. The influence of climate change on forest insect pests in Britain. 2016;(March):1–10.
- 4. Government TUK, Postnote T. Invasive non-native species. 2022;(673):14–9.
- 5. Sciences E, Anglia E, Policy H, Sciences E, Anglia E, Policy H, et al. Food and Climate change: A review of the effects of climate change on food within the remit of the Food Standards Agency.:1–111.
- 6.
Working together to achieve Net Zero.
- 7. Co MT, February B, Co M, Co M. No Title. 2020;
- 8. Jebari A, Pereyra-Goday F, Kumar A, Collins AL, Rivero MJ, McAuliffe GA. Feasibility of mitigation measures for agricultural greenhouse gas emissions in the UK. A systematic review. Vol. 44, Agronomy for Sustainable Development. Springer-Verlag Italia s.r.l.; 2024.
- 9.
Agriculture in the UK Evidence Pack. 2022;
- 10. Hamilton-Webb A, Manning L, Naylor R, Conway J. The relationship between risk experience and risk response: a study of farmers and climate change. J Risk Res [Internet]. 2017 Nov 2 [cited 2024 May 20];20(11):1379–93. Available from: https://www.tandfonline.com/doi/abs/10.1080/13669877.2016.1153506.
- 11. Kreft C, Huber R, Wuepper D, Finger R. The role of non-cognitive skills in farmers’ adoption of climate change mitigation measures. Ecological Economics. 2021 Nov 1; 189:107169.
- 12. Tzemi D, Breen J. Climate change and the agricultural sector in Ireland: examining farmer awareness and willingness to adopt new advisory mitigation tools. Climate Policy [Internet]. 2019 May 28;19(5):611–22. Available from: https://doi.org/10.1080/14693062.2018.1546163.
- 13. Brander M. GHGs CO2 CO2e and Carbon What Do These Mean? v2.1. Ecometrica. 2012;(August):2–4.
- 14.
Agri-climate report 2022 ‐ GOV.UK [Internet]. [cited 2024 May 26]. Available from: https://www.gov.uk/government/statistics/agri-climate-report-2022/agri-climate-report-2022.
- 15. Oosting SJ, Udo HMJ, Viets TC. Development of livestock production in the tropics: Farm and farmers’ perspectives. Animal. 2014;8(8):1238–48. pmid:24673769
- 16. Wachiye S, Pellikka P, Rinne J, Heiskanen J, Abwanda S, Merbold L. Effects of livestock and wildlife grazing intensity on soil carbon dioxide flux in the savanna grassland of Kenya. Agric Ecosyst Environ. 2022;325(October 2021):107713.
- 17.
IPCC. IPCC ‐ Synthesis Report. 2021.
- 18.
IPCC. IPCC Guidelines for National Greenhouse Inventories. Prepared by the National Greenhouse Gas Inventories Programme. 2006;20.
- 19. Boval M, Dixon RM. The importance of grasslands for animal production and other functions: A review on management and methodological progress in the tropics. Animal. 2012;6(5):748–62. pmid:22558923
- 20. Thornton PK, Herrero M. Potential for reduced methane and carbon dioxide emissions from livestock and pasture management in the tropics. Proc Natl Acad Sci U S A. 2010;107(46):19667–72. pmid:20823225
- 21. Jordon MW, Willis KJ, Bürkner PC, Petrokofsky G. Rotational grazing and multispecies herbal leys increase productivity in temperate pastoral systems–A meta-analysis. Agric Ecosyst Environ. 2022;337(June):108075.
- 22. Kaparaju P, Rintala J. Mitigation of greenhouse gas emissions by adopting anaerobic digestion technology on dairy, sow and pig farms in Finland. Renew Energy. 2011;36(1):31–41.
- 23. Ma G, Ndegwa P, Harrison JH, Chen Y. Methane yields during anaerobic co-digestion of animal manure with other feedstocks: A meta-analysis. Science of the Total Environment. 2020; 728:138224. pmid:32361106
- 24. Roopnarain A, Rama H, Ndaba B, Bello-Akinosho M, Bamuza-Pemu E, Adeleke R. Unravelling the anaerobic digestion ‘black box’: Biotechnological approaches for process optimization. Renewable and Sustainable Energy Reviews. 2021; 152:111717.
- 25. Li Y, Zhao J, Krooneman J, Euverink GJW. Strategies to boost anaerobic digestion performance of cow manure: Laboratory achievements and their full-scale application potential. Science of the Total Environment. 2021;755. pmid:33348487
- 26.
Ricardo Energy & Environment. Farmyard Manure and Slurry Management and Anaerobic Digestion in Scotland-Practical Application on Farm. ClimateXChange. 2017; 1:1–39.
- 27. Scott A, Blanchard R. The role of anaerobic digestion in reducing dairy farm greenhouse gas emissions. Sustainability (Switzerland). 2021 Mar 1;13(5):1–18.
- 28.
Farm Net Zero: Livestock Factsheet.
- 29. Duthie CA, Troy SM, Hyslop JJ, Ross DW, Roehe R, Rooke JA. The effect of dietary addition of nitrate or increase in lipid concentrations, alone or in combination, on performance and methane emissions of beef cattle. Animal. 2018 Feb 1;12(2):280–7. pmid:28701247
- 30. Dawe D, Dobermann A, Ladha JK, Yadav RL, Bao L, Gupta RK, et al. Do organic amendments improve yield trends and profitability in intensive rice systems? Field Crops Res. 2003;83(2):191–213.
- 31. Kelland ME, Wade PW, Lewis AL, Taylor LL, Sarkar B, Andrews MG, et al. Increased yield and CO2 sequestration potential with the C4 cereal Sorghum bicolor cultivated in basaltic rock dust-amended agricultural soil. Glob Chang Biol. 2020 Jun 1;26(6):3658–76. pmid:32314496
- 32. Nicholson F, Bhogal A, Taylor M, McGrath S, Withers P. Long-term Effects of Biosolids on Soil Quality and Fertility. Soil Sci. 2018;183(3):89–98.
- 33. Albano X, Whitmore AP, Sakrabani R, Thomas CL, Sizmur T, Ritz K, et al. Effect of Different Organic Amendments on Actual and Achievable Yields in a Cereal-Based Cropping System. J Soil Sci Plant Nutr. 2023;23(2):2122–37.
- 34. Xue QY, Dai P Bin, Sun DS, Sun CL, Qi LY, Ostermann A, et al. Effects of rainfall and manure application on phosphorus leaching in field lysimeters during fallow season. J Soils Sediments. 2013;13(9):1527–37.
- 35. LI H, FENG W ting, HE X hua, ZHU P, GAO H jun, SUN N, et al. Chemical fertilizers could be completely replaced by manure to maintain high maize yield and soil organic carbon (SOC) when SOC reaches a threshold in the Northeast China Plain. J Integr Agric. 2017;16(4):937–46.
- 36. Urra J, Alkorta I, Garbisu C. Potential Benefits and Risks for Soil Health Derived From the Use of Organic Amendments in Agriculture. Agronomy 2019, Vol 9, Page 542 [Internet]. 2019 Sep 12 [cited 2024 May 27];9(9):542. Available from: https://www.mdpi.com/2073-4395/9/9/542/htm.
- 37. Alskaf K. CONSERVATION AGRICULTURE FOR SUSTAINABLE LAND USE.
- 38. Jordon MW, Smith P, Long PR, Bürkner PC, Petrokofsky G, Willis KJ. Can Regenerative Agriculture increase national soil carbon stocks? Simulated country-scale adoption of reduced tillage, cover cropping, and ley-arable integration using RothC. Science of the Total Environment. 2022 Jun 15;825. pmid:35189215
- 39. Malone RW, Radke A, Herbstritt S, Wu H, Qi Z, Emmett BD, et al. Harvested winter rye energy cover crop: multiple benefits for North Central US. Environmental Research Letters. 2023;18(7):074009.
- 40. O’Neill C, Lim FKS, Edwards DP, Osborne CP. Forest regeneration on European sheep pasture is an economically viable climate change mitigation strategy. Environmental Research Letters. 2020 Oct 1;15(10).
- 41. Crous-Duran J, Graves AR, de Jalón SG, Kay S, Tomé M, Burgess PJ, et al. Quantifying regulating ecosystem services with increased tree densities on European Farmland. Sustainability (Switzerland). 2020 Aug 1;12(16).
- 42. Jordon MW, Willis KJ, Harvey WJ, Petrokofsky L, Petrokofsky G. Implications of temperate agroforestry on sheep and cattle productivity, environmental impacts and enterprise economics. A systematic evidence map. Forests. 2020 Dec 1;11(12):1–29.
- 43. Dunn C, Burden A, Chamberlain B, Danek S, Evans C, Freeman C, et al. Nature-based solutions for climate change in the UK-Peatlands Nature-based solutions for climate change in the UK. 2021.
- 44. Pagella T, Whistance L; Silvopasture. 2019.
- 45. Nworji J. Bangor University DOCTOR OF PHILOSOPHY Physical and bioeconomic analysis of ecosystem services from a silvopasture system [Internet]. Available from: http://www.llgc.org.uk/index.php?id=4676.
- 46. Muchane MN, Sileshi GW, Gripenberg S, Jonsson M, Pumariño L, Barrios E. Agroforestry boosts soil health in the humid and sub-humid tropics: A meta-analysis. Agric Ecosyst Environ. 2020;295(March):106899.
- 47. Chatterjee N, Nair PKR, Chakraborty S, Nair VD. Changes in soil carbon stocks across the Forest-Agroforest-Agriculture/Pasture continuum in various agroecological regions: A meta-analysis. Agric Ecosyst Environ. 2018;266(August):55–67.
- 48. Upson MA, Burgess PJ, Morison JIL. Soil carbon changes after establishing woodland and agroforestry trees in a grazed pasture. Geoderma. 2016; 283:10–20.
- 49. Jose S. Environmental Impacts and Benefits of Agroforestry. Oxford Research Encyclopedia of Environmental Science [Internet]. 2019 Oct 30 [cited 2024 May 27]; Available from: https://oxfordre.com/environmentalscience/display/10.1093/acrefore/9780199389414.001.0001/acrefore-9780199389414-e-195.
- 50.
BEIS 2021.
- 51. Robertson AD, Davies CA, Smith P, Stott AW, Clark EL, McNamara NP. Carbon Inputs from Miscanthus Displace Older Soil Organic Carbon Without Inducing Priming. Bioenergy Res. 2017 Mar 1;10(1):86–101.
- 52. Yesufu J, McCalmont JP, Clifton-Brown JC, Williams P, Hyland J, Gibbons J, et al. Consequential life cycle assessment of miscanthus livestock bedding, diverting straw to bioelectricity generation. GCB Bioenergy. 2020 Jan 1;12(1):39–53.
- 53. Harris ZM, Alberti G, Viger M, Jenkins JR, Rowe R, McNamara NP, et al. Land-use change to bioenergy: grassland to short rotation coppice willow has an improved carbon balance. GCB Bioenergy. 2017 Feb 1;9(2):469–84.
- 54. Donnison C, Holland RA, Harris ZM, Eigenbrod F, Taylor G. Land-use change from food to energy: Meta-analysis unravels effects of bioenergy on biodiversity and cultural ecosystem services. Environmental Research Letters. 2021;16(11).
- 55. Donnison C, Holland RA, Harris ZM, Eigenbrod F, Taylor G. Land-use change from food to energy: meta-analysis unravels effects of bioenergy on biodiversity and cultural ecosystem services. Environmental Research Letters [Internet]. 2021 Nov 11 [cited 2024 May 26];16(11):113005. Available from: https://iopscience.iop.org/article/10.1088/1748-9326/ac22be.
- 56. Immerzeel DJ, Verweij PA, van der Hilst F, Faaij APC. Biodiversity impacts of bioenergy crop production: A state-of-the-art review. GCB Bioenergy. 2014;6(3):183–209.
- 57. von Hellfeld R, Hastings A, Kam J, Rowe R, Clifton-Brown J, Donnison I, et al. Expanding the Miscanthus market in the UK: Growers in profile and experience, benefits and drawbacks of the bioenergy crop. GCB Bioenergy. 2022;14(11):1205–18. pmid:36632359
- 58. Mayorga MAM, Martínez EI, Gómez NC. Jatropha suppliers as contributors to the sustainability of the production of bioelectricity in Ecuador. Sustainability (Switzerland). 2017;9(11).
- 59. Daioglou V, Wicke B, Faaij APC, van Vuuren DP. Competing uses of biomass for energy and chemicals: Implications for long-term global CO2 mitigation potential. GCB Bioenergy. 2015;7(6):1321–34.
- 60. Klein RJ, Denton F, Biagini GEF B, Burch S, Studd K, Taylor A, et al. Inter-relationships between adaptation and mitigation Coordinating Lead Authors: Lead Authors: Contributing Authors: to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
- 61.
Ireland’ s Long -term Strategy on Greenhouse Gas Emissions Reduction.
- 62. Becker PM, Van Wikselaar PG, Franssen CR, De Vos RCH, Hall RD, Beekwilder J. Evidence for a hydrogen-sink mechanism of (+) catechin-mediated emission reduction of the ruminant greenhouse gas methane. Metabolomics. 2014; 10:179.
- 63. Matos JS, de Aráujo LP, Allaman IB, Lôbo IP, de Oliva ST, Tavares TM, et al. Evaluation of the reduction of methane emission in swine and bovine manure treated with black soldier fly larvae (Hermetia illucens L.). Environ Monit Assess [Internet]. 2021;193(8). Available from: pmid:34240260
- 64.
Prager K; PH. SOCIO-ECONOMIC FACTORS INFLUENCING FARMERS’ADOPTION OF SOIL CONSERVATION PRACTICES IN EUROPE. Human Dimensions of Soil and Water Conservation [Internet]. [cited 2024 Apr 16]; Available from: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=60df3e846207711639c5b468d3c20e1ff80d3624.
- 65. De Laporte A, Schuurman D, Weersink A. Costs and Benefits of Effective and Implementable On-Farm Beneficial Management Practices that Reduce Greenhouse Gases.
- 66. Feliciano D, Hunter C, Slee B, Smith P. Climate change mitigation options in the rural land use sector: Stakeholders’ perspectives on barriers, enablers and the role of policy in North East Scotland. Environ Sci Policy. 2014 Jan 1; 44:26–38.
- 67. Hou Y, Velthof GL, Case SDC, Oelofse M, Grignani C, Balsari P, et al. Stakeholder perceptions of manure treatment technologies in Denmark, Italy, the Netherlands and Spain. J Clean Prod. 2018 Jan 20; 172:1620–30.
- 68. Meijer SS, Catacutan D, Ajayi OC, Sileshi GW, Nieuwenhuis M. The role of knowledge, attitudes and perceptions in the uptake of agricultural and agroforestry innovations among smallholder farmers in sub-Saharan Africa. Int J Agric Sustain. 2015 Jan 2;13(1):40–54.
- 69. Bartkowski B, Bartke S. Leverage points for governing agricultural soils: A review of empirical studies of European farmers’ decision-making. Sustainability (Switzerland). 2018 Sep 5;10(9).
- 70. Howley P. Examining farm forest owners’ forest management in ireland: The role of economic, lifestyle and multifunctional ownership objectives. J Environ Manage. 2013 Jul 5; 123:105–12. pmid:23590944
- 71. Bopp C, Engler A, Poortvliet PM, Jara-Rojas R. The role of farmers’ intrinsic motivation in the effectiveness of policy incentives to promote sustainable agricultural practices. J Environ Manage. 2019 Aug 15; 244:320–7. pmid:31129464
- 72. O’Connor RE, Bard RJ, Fisher A. Risk Perceptions, General Environmental Beliefs, and Willingness to Address Climate Change. Risk Analysis. 1999 Jun;19(3):461–71.
- 73. Sulewski P, Kłoczko-Gajewska A. Farmers’ risk perception, risk aversion and strategies to cope with production risk: An empirical study from Poland. Studies in Agricultural Economics. 2014;116(3):140–7.
- 74. Liu T, Bruins RJF, Heberling MT. Factors influencing farmers’ adoption of best management practices: A review and synthesis. Sustainability (Switzerland). 2018 Feb 7;10(2). pmid:29682334
- 75. Arbuckle JG, Morton LW, Hobbs J. Understanding Farmer Perspectives on Climate Change Adaptation and Mitigation: The Roles of Trust in Sources of Climate Information, Climate Change Beliefs, and Perceived Risk. Environ Behav. 2015 Feb 19;47(2):205–34. pmid:25983336
- 76. Dessart FJ, Barreiro-Hurlé J, Van Bavel R. Behavioural factors affecting the adoption of sustainable farming practices: A policy-oriented review. European Review of Agricultural Economics. 2019 Jul 1;46(3):417–71.
- 77. Mishra B, Gyawali BR, Paudel KP, Poudyal NC, Simon MF, Dasgupta S, et al. Adoption of Sustainable Agriculture Practices among Farmers in Kentucky, USA. Environ Manage. 2018 Dec 1;62(6):1060–72. pmid:30244372
- 78.
Feliciano D. Factors influencing the adoption of sustainable agricultural practices: the case of seven horticultural farms in the United Kingdom. Scottish Geographical Journal [Internet]. 2022 Oct 2 [cited 2024 Apr 17];138(3–4):291–320. Available from: https://www.tandfonline.com/doi/abs/10.1080/14702541.2022.2151041.
- 79. Jones AK, Jones DL, Cross P. Developing farm-specific marginal abatement cost curves: Cost-effective greenhouse gas mitigation opportunities in sheep farming systems. Land use policy. 2015 Dec 1; 49:394–403.
- 80. Haden VR, Niles MT, Lubell M, Perlman J, Jackson LE. Global and Local Concerns: What Attitudes and Beliefs Motivate Farmers to Mitigate and Adapt to Climate Change? PLoS One. 2012 Dec 29;7(12). pmid:23300805
- 81. Barnes AP, McMillan J, Sutherland LA, Hopkins J, Thomson SG. Farmer intentional pathways for net zero carbon: Exploring the lock-in effects of forestry and renewables. Land use policy. 2022 Jan 1; 112:105861.
- 82. van Dijk WFA, Lokhorst AM, Berendse F, de Snoo GR. Collective agri-environment schemes: How can regional environmental cooperatives enhance farmers’ intentions for agri-environment schemes? Land use policy. 2015 Jan 1; 42:759–66.
- 83. Feliciano D, Hunter C, Slee B, Smith P. Selecting land-based mitigation practices to reduce GHG emissions from the rural land use sector: A case study of North East Scotland. J Environ Manage. 2013 May 5; 120:93–104. pmid:23507248
- 84. Abu Jubain M, Alobaidi H, Bholah S, Kanani F, Koghar R, Sitch A, et al. Willingness and attitudes of the general public towards the involvement of medical students in their healthcare. Can Med Educ J. 2012;3(2): e118–26. pmid:26451181
- 85. Liu XL, Cheng HL, Lam SC. Predicting undergraduate nursing students’ willingness to care for older adults: A multicenter cross-sectional study in Asia. Nurs Open. 2022;9(2):1402–11. pmid:33957016
- 86. Çamdevýren H, Demýr N, Kanik A, Keskýn S. Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. Ecol Modell. 2005;181(4):581–9.
- 87. Badu-Apraku B, Talabi AO, Fakorede MAB, Fasanmade Y, Gedil M, Magorokosho C, et al. Yield gains and associated changes in an early yellow bi-parental maize population following genomic selection for Striga resistance and drought tolerance. BMC Plant Biol. 2019;19(1):1–18.
- 88. Liu Y, Heuvelink GBM, Bai Z, He P, Xu X, Ding W, et al. Analysis of spatio-temporal variation of crop yield in China using stepwise multiple linear regression. Field Crops Res. 2021;264(February):108098.
- 89. Lasso S, Manual R. stata.com.:1–11.
- 90. Lindsey C, Sheather S. Variable selection in linear regression. Stata J. 2010;10(4):650–69.
- 91. Lima E, Hopkins T, Gurney E, Shortall O, Lovatt F, Davies P, et al. Drivers for precision livestock technology adoption: A study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales. PLoS One. 2018;13(1). pmid:29293617
- 92. Liu J, Toma L, Barnes AP, Stott A. Farmers’ Uptake of Animal Health and Welfare Technological Innovations. Implications for Animal Health Policies. Front Vet Sci. 2019;6(November):1–10.
- 93. Hyland JJ, Heanue K, McKillop J, Micha E. Factors influencing dairy farmers’ adoption of best management grazing practices. Land use policy. 2018; 78:562–71.
- 94. Wubeneh NG, Sanders JH. Farm-level adoption of sorghum technologies in Tigray, Ethiopia. Agric Syst. 2006;91(1–2):122–34.
- 95. Teklewold H, Kassie M, Shiferaw B. Adoption of multiple sustainable agricultural practices in rural Ethiopia. J Agric Econ. 2013;64(3):597–623.
- 96. Lalani B, Dorward P, Holloway G, Wauters E. Smallholder farmers’ motivations for using Conservation Agriculture and the roles of yield, labour and soil fertility in decision making. Agric Syst. 2016; 146:80–90.
- 97. Ojo TO, Baiyegunhi LJS, Adetoro AA, Ogundeji AA. Adoption of soil and water conservation technology and its effect on the productivity of smallholder rice farmers in Southwest Nigeria. Heliyon. 2021;7(3): e06433. pmid:33763609
- 98. Lima E, Hopkins T, Gurney E, Shortall O, Lovatt F, Davies P, et al. Drivers for precision livestock technology adoption: A study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales. PLoS One [Internet]. 2018 Jan 1 [cited 2024 May 29];13(1): e0190489. Available from: pmid:29293617
- 99. Schirmer J, Bull L. Assessing the likelihood of widespread landholder adoption of afforestation and reforestation projects. Global Environmental Change. 2014 Jan 1;24(1):306–20.
- 100. Zhao N, You F. Dairy waste-to-energy incentive policy design using Stackelberg-game-based modeling and optimization. Appl Energy. 2019; 254:113701.
- 101. Huffman WE. Human Capital and Adoption of Innovations: Policy Implications. Appl Econ Perspect Policy [Internet]. 2020 Mar 1 [cited 2024 May 29];42(1):92–9. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/aepp.13010.
- 102.
Hamilton-Webb by. Living on the edge: using cognitive filters to appraise environmental risk. J Risk Res [Internet]. 2017 [cited 2024 May 29]; Available from: https://doi.org/10.1080/13669877.2017.1378249.
- 103. Abdulai A, Huffman W. The Adoption and Impact of Soil and Water Conservation Technology: An Endogenous Switching Regression Application. Land Econ. 2015;90(1):26–43.
- 104.
Tests and Trials Evidence Report Schemes for environmental land management. 2022 [cited 2024 May 29]; Available from: www.gov.uk/defra.