Crop diversification is a major weapon to meet food security by improving soil fertility, reducing the risk of drought, water shortages and reducing erosion. The adoption of crop diversification heavily depends on the behavioural intentions of farmers. The article attempts to analyse the factors affecting the behavioural intention to adopt crop diversification using the theory of planned behaviour (TPB). Primary data were collected from 753 farmers from all the blocks in the Jalpaiguri district of West Bengal. Various statistical tools, involving factor analysis and structural equation modelling (SEM), have been used for this study. The result indicates attitudes and subjective norms (SN) have a negative effect, whereas perceived behavioural control (PBC) and perceived benefits of crop diversification have a positive effect on the behavioural intention of crop diversification. The study recommends that the promotion of crop diversification should focus on enhancing PBC and highlighting the tangible benefits of diversification, even if the farmers have negative attitudes or negative SNs.
AjzenI. (1985). From intentions to actions: A theory of planned behaviour. In Action control: From cognition to behaviour (pp. 11–39). Springer Berlin Heidelberg.
2.
AjzenI. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
3.
AliM., & AbedullahD. (2002). Nutritional and economic benefits of enhanced vegetable production and consumption. Journal of Crop Production, 6(1–2), 145–176.
4.
AmareD., & DarrD. (2023). Farmers’ intentions toward sustained agroforestry adoption: An application of the theory of planned behaviour. Journal of Sustainable Forestry, 42(9), 869–886.
5.
AsfawS., ShiferawB., SimtoweF., & LipperL. (2012). Impact of modern agricultural technologies on smallholder welfare: Evidence from Tanzania and Ethiopia. Food Policy, 37(3), 283–295.
6.
BagheriA., BondoriA., AllahyariM. S., & DamalasC. A. (2019). Modeling farmers’ intention to use pesticides: An expanded version of the theory of planned behaviour. Journal of Environmental Management, 248, 109291. https://doi.org/10.1016/j.jenvman.2019.109291
7.
BambergS., & MoserG. (2007). Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. Journal of Environmental Psychology, 27(1), 14–25. https://doi.org/10.1016/j.jenvp.2006.12.002
8.
BarghoutiS., KaneS., SorbyK., & AliM. (2004). Agricultural diversification for the poor: Guidelines for practitioners. Agriculture and rural development discussion paper 1. World Bank.
9.
BarnesA. P., HanssonH., Manevska-TasevskaG., ShresthaS. S., & ThomsonS. G. (2015). The influence of diversification on long-term viability of the agricultural sector. Land Use Policy, 49, 404–412.
10.
BedoussacL., JournetE. P., Hauggaard-NielsenH., NaudinC., Corre-HellouG., JensenE. S., PrieurL., & JustesE. (2015). Ecological principles underlying the increase of productivity achieved by cereal-grain legume intercrops in organic farming. A review. Agronomy for Sustainable Development, 35, 911–935.
11.
BentlerP. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238.
12.
BentlerP. M., & BonettD. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588.
13.
BirthalP. S., JoshiP. K., RoyD., & ThoratA. (2013). Diversification in Indian agriculture toward high-value crops: The role of small farmers. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie, 61(1), 61–91.
14.
BirthalP. S., KhanT., NegiD. S., & AgarwalS. (2014). Impact of climate change on yields of major food crops in India: Implications for food security. Agricultural Economics Research Review, 27(2), 145–155.
15.
BirthalP. S., RoyD., & NegiD. S. (2015). Assessing the impact of crop diversification on farm poverty in India. World Development, 72, 70–92.
16.
BonkeV., & MusshoffO. (2020). Understanding German farmer’s intention to adopt mixed cropping using the theory of planned behaviour. Agronomy for Sustainable Development, 40(6), 48.
17.
BorgesA. V., DarchambeauF., LambertT., BouillonS., MoranaC., BrouyèreS., HakounV., JuradoA., TsengH.-C., DescyJ.-P., & RolandF. A. (2018). Effects of agricultural land use on fluvial carbon dioxide, methane and nitrous oxide concentrations in a large European river, the Meuse (Belgium). Science of the Total Environment, 610, 342–355.
18.
BorgesJ. A. R., LansinkA. G. O., RibeiroC. M., & LutkeV. (2014). Understanding farmers’ intention to adopt improved natural grassland using the theory of planned behaviour. Livestock Science, 169, 163–174.
19.
BrowneM. W., & CudeckR. (1993). Alternative ways of assessing model fit. In BollenK. A. & LongJ. S. (Eds), Testing structural equation models (pp. 136–162). Sage Publications.
BurtonR. J. F. (2004). Reconceptualising the ‘behavioural approach’ in agricultural studies: A socio-psychological perspective. Journal of Rural Studies, 20, 359–371. https://doi.org/10.1016/j.jrurstud.2003.12.001
22.
CaoH., LiF., ZhaoK., QianC., & XiangT. (2022). From value perception to behavioural intention: Study of Chinese smallholders’ pro-environmental agricultural practices. Journal of Environmental Management, 315, 115179.
23.
ChandR. (1996). Diversification through high-value crops in the Western Himalayan region: Evidence from Himachal Pradesh. Indian Journal of Agricultural Economics, 41(4), 652–663.
24.
ChurchillG. A.Jr (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73.
25.
DashG., & PaulJ. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092.
26.
DavisS. C., PartonW. J., GrossoS. J. D., KeoughC., MarxE., AdlerP. R., & DeLuciaE. H. (2012). Impact of second-generation biofuel agriculture on greenhouse-gas emissions in the corn-growing regions of the US. Frontiers in Ecology and the Environment, 10(2), 69–74.
27.
De LauwereC., Van AsseldonkM., Van’t RietJ., De HoopJ., & Ten PierickE. (2012). Understanding farmers’ decisions with regard to animal welfare: The case of changing to group housing for pregnant sows. Livestock Science, 143(2-3), 151–161.
28.
DengJ., SunP., ZhaoF., HanX., YangG., & FengY. (2016). Analysis of the ecological conservation behaviour of farmers in payment for ecosystem service programs in eco-environmentally fragile areas using social psychology models. Science of the Total Environment, 550, 382–390. https://doi.org/10.1016/j.scitotenv.2016.01.152
29.
DespotovićJ., RodićV., & CaraccioloF. (2019). Factors affecting farmers’ adoption of integrated pest management in Serbia: An application of the theory of planned behaviour. Journal of Cleaner Production, 228, 1196–1205. https://doi.org/10.1016/j.jclepro.2019.04.149
30.
Di FalcoS., & ChavasJ. P. (2009). On crop biodiversity, risk exposure, and food security in the highlands of Ethiopia. American Journal of Agricultural Economics, 91(3), 599–611.
31.
FieldingK. S., TerryD. J., MasserB. M., BordiaP., & HoggM. A. (2005). Explaining landholders’ decisions about riparian zone management: The role of behavioural, normative, and control beliefs. Journal of Environmental Management, 77(1), 12–21.
32.
FieldingK. S., McDonaldR., & LouisW. R. (2008). Theory of planned behaviour, identity and intentions to engage in environmental activism. Journal of Environmental Psychology, 28(4), 318–326.
33.
FoleyK. M. (2013). Examining the voluntary adoption of agricultural conservation practices in northern Malheur County, Oregon.
34.
GaoH., YanC., LiuQ., DingW., ChenB., & LiZ. (2019). Effects of plastic mulching and plastic residue on agricultural production: A meta-analysis. Science of the Total Environment, 651, 484–492.
35.
GaoL., WangS., LiJ., & LiH. (2017). Application of the extended theory of planned behaviour to understand individuals’ energy-saving behaviour in workplaces. Resources, Conservation and Recycling, 127, 107–113. https://doi.org/10.1016/j.resconrec.2017.08.030
36.
GarbachK., MilderJ. C., DeClerckF. A., Montenegro de WitM., DriscollL., & Gemmill-HerrenB. (2017). Examining multi-functionality for crop yield and ecosystem services in five systems of agroecological intensification. International Journal of Agricultural Sustainability, 15(1), 11–28.
37.
GarforthC., RehmanT., McKemeyK., TranterR., CookeR. J., YatesC. M., ParkJ., & DorwardP. (2004). Improving the design of knowledge transfer strategies by understanding farmer attitudes and behaviour. Journal of Farm Management, 12(1), 17–32.
38.
GarforthC., McKemeyK., RehmanT., TranterR., CookeR., ParkJ., DorwardP., & YatesC. (2006). Farmers’ attitudes towards techniques for improving oestrus detection in dairy herds in South West England. Livestock Science, 103(1-2), 158–168.
39.
GassonR., & PotterC. (1988). Conservation through land diversion: A survey of farmers attitudes. Journal of Agricultural Economics, 39(3), 340–351.
40.
GeY., FanL., LiY., GuoJ., & NiuH. (2023). Gender differences in smallholder farmers’ adoption of crop diversification: Evidence from Shaanxi Plain, China. Climate Risk Management, 39, 100482.
41.
GeelsF. W. (2011). The multi-level perspective on sustainability transitions: Responses to seven criticisms. Environmental Innovation and Societal Transitions, 1(1), 24–40.
42.
GkargkavouziA., HalkosG., & MatsioriS. (2019). Environmental behaviour in a private-sphere context: Integrating theories of planned behaviour and value-belief-norm, self-identity, and habit. Resources, Conservation & Recycling, 148, 145–156. https://doi.org/10.1016/j.resconrec.2019.01.039
43.
Government of India. (n.d.). District map[Map]. In MGNREGA, Jalpaiguri. Retrieved October 26, 2022, fromhttps://nregajalpaiguri.com/mgnrega/district_map.php
44.
GreinerR. (2015). Motivations and attitudes influence farmers’ willingness to participate in biodiversity conservation contracts. Agricultural Systems, 137, 154–165.
45.
HairJ. F.Jr, BabinB. J., & KreyN. (2017). Covariance-based structural equation modeling in the Journal of Advertising: Review and recommendations. Journal of Advertising, 46(1), 163–177.
46.
HairJ. F.Jr, HowardM. C., & NitzlC. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110.
47.
HanssonH., FergusonR., & OlofssonC. (2012). Psychological constructs underlying farmers’ decisions to diversify or specialise their businesses: An application of theory of planned behaviour. Journal of Agricultural Economics, 63(2), 465–482.
48.
HaroM. M., SilveiraL. C. P., & WilbyA. (2018). Stability lies in flowers: Plant diversification mediating shifts in arthropod food webs. PLOS ONE, 13(2). https://doi.org/10.1371/journal.pone.0193045
49.
HattS., BoeraeveF., ArtruS., DufrêneM., & FrancisF. (2018). Spatial diversification of agroecosystems to enhance biological control and other regulating services: An agroecological perspective. Science of the Total Environment, 621, 600–611. https://doi.org/10.1016/j.scitotenv.2017.11.296
50.
HattamC. (2006). Adopting organic agriculture: An investigation using the theory of planned behaviour. International Association of Agricultural Economics Conference.
51.
HondradeR. F., HondradeE., ZhengL., ElazeguiF., DuqueJ. A. L. J. E., MundtC. C., Vera CruzC. M., & GarrettK. A. (2017). Cropping system diversification for food production in Mindanao rubber plantations: A rice cultivar mixture and rice intercropped with mungbean. PeerJ, 2017(2). https://doi.org/10.7717/peerj.2975
52.
HooperD., CoughlanJ., & MullenM. (2008, September). Evaluating model fit: A synthesis of the structural equation modelling literature. In 7th European Conference on Research Methodology for Business and Management Studies (Vol. 2008, pp. 195–200). https://doi.org/10.1016/j.techfore.2018.02.015
53.
HuL. T., & BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
54.
HufnagelJ., RecklingM., & EwertF. (2020). Diverse approaches to crop diversification in agricultural research. A review. Agronomy for Sustainable Development, 40(2), 14.
55.
IqbalT., & El-GoharyE. (2014). An attempt to understand e-marketing: An information technology prospective. International Journal of Business and Social Science, 5(4), 234–256.
56.
JöreskogK. G., & SörbomD. (1993). LISREL 8: Structural Equation Modeling with the Simplis Command Language. Scientific Software International.
57.
JöreskogK. G., OlssonU. H., & WallentinF. Y. (2016). Multivariate analysis with LISREL. Springer.
58.
JoshiP. K., GulatiA., BirthalP. S., & TewariL. (2004). Agriculture diversification in South Asia: Patterns, determinants, and policy implications. Economic & Political Weekly, 2457–2467.
59.
JoshiP. K., Ashok GulatiP. S. Birthal, & LaxmiTewari. (2003). Agricultural Diversification in South Asia: Patterns, Determinants, and Policy Implications[MSSD Discussion Paper No. 57]. International Food Policy Research Institute.
60.
JudgeM., Warren-MyersG., & PaladinoA. (2019). Using the theory of planned behaviour to predict intentions to purchase sustainable housing. Journal of Cleaner Production, 215, 259–267. https://doi.org/10.1016/j.jclepro.2019.01.029
61.
KhatazaR. R. B., DooleG. J., KragtM. E., & HailuA. (2018). Information acquisition, learning, and the adoption of conservation agriculture in Malawi: A discrete-time duration analysis. Technological Forecasting and Social Change, 132, 299–307.
62.
KlineR. B. (2015). The mediation myth. Basic and Applied Social Psychology, 37(4), 202–213.
63.
KnowlerD., & BradshawB. (2007). Farmers’ adoption of conservation agriculture: A review and synthesis of recent research. Food Policy, 32(1), 25–48.
64.
KremenC., IlesA., & BaconC. (2012). Diversified farming systems: An agroecological, systems-based alternative to modern industrial agriculture. Ecology and Society, 17(4). http://www.jstor.com/stable/26269193
65.
KumarR., ShambhaviS., BeuraK., KumarS., & SinghR. G. (2017). Soil microbial budgeting as influenced by contrasting tillage and crop diversification under rice-based cropping systems in Inceptisol of Bihar. Journal of Pure and Applied Microbiology, 11(1), 539–547. https://doi.org/10.22207/JPAM.11.1.71
66.
KumarB. (2012). Theory of planned behaviour approach to understand the purchasing behaviour for environmentally sustainable products(No. WP2012-12-08). Indian Institute of Management Ahmedabad, Research and Publication Department.
67.
Kyere-DuoduK., & YamoahD. D. (2011). Adoption of internet banking among Ghanaian consumers: A study using the decomposed theory of planned behaviour(Master’s thesis). Lulea University of Technology.
68.
LalaniB., DorwardP., HollowayG., & WautersE. (2016). Smallholder farmers’ motivations for using Conservation Agriculture and the roles of yield, labour, and soil fertility in decision making. Agricultural Systems, 146, 80–90. https://doi.org/10.1016/j.agsy.2016.04.002
69.
LäppleD., & KelleyH. (2013). Understanding the uptake of organic farming: Accounting for heterogeneities among Irish farmers. Ecological Economics, 88, 11–19.
70.
LeeD. R. (2005). Agricultural sustainability and technology adoption: Issues and policies for developing countries. American Journal of Agricultural Economics, 87(5), 1325–1334.
71.
LemkenD., KniggeM., MeyerdingS., & SpillerA. (2017). The value of environmental and health claims on new legume products: A non-hypothetical online auction. Sustainability, 9(8), 1340.
72.
LiaoC., ZhaoD., & ZhangS. (2018). Psychological and conditional factors influencing staff’s takeaway waste separation intention: An application of the extended theory of planned behaviour. Sustainable Cities and Society, 41, 186–194. https://doi.org/10.1016/j.scs.2018.05.046
73.
LuH., ZhangP. W., HuH., XieH. L., YuZ. N., & ChenS. (2019). Effect of the grain-growing purpose and farm size on the ability of stable land property rights to encourage farmers to apply organic fertilizers. Journal of Environmental Management, 251, 109621. https://doi.org/10.1016/j.jenvman.2019.109621
74.
MakateC., WangR., MakateM., & MangoN. (2016). Crop diversification and livelihoods of smallholder farmers in Zimbabwe: Adaptive management for environmental change. SpringerPlus, 5, 1–18.
75.
MaleksaeidiH., & KeshavarzM. (2019). What influences farmers’ intentions to conserve on-farm biodiversity? An application of the theory of planned behaviour in Fars province, Iran. Global Ecology and Conservation, 20, e00698. https://doi.org/10.1016/j.gecco.2019.e00698
76.
MalézieuxE., CrozatY., DuprazC., LauransM., MakowskiD., Ozier-LafontaineH., RapidelB., de TourdonnetS., & Valantin-MorisonM. (2009). Mixing plant species in cropping systems: Concepts, tools and models. Agronomy for Sustainable Development, 29(1), 43–62. https://doi.org/10.1051/agro:2007057
77.
MalhotraN. K., & DashS. (2011). Marketing research: An applied orientation (6th ed., pp. 126–133). Pearson-Dorling Kindersley.
78.
MangoN., MakateC., MapembaL., & SopoM. (2018). The role of crop diversification in improving household food security in central Malawi. Agriculture & Food Security, 7(1), 1–10.
79.
Martínez-GarcíaC. G., DorwardP., & RehmanT. (2013). Factors influencing adoption of improved grassland management by small-scale dairy farmers in central Mexico and the implications for future research on smallholder adoption in developing countries. Livestock Science, 152(2–3), 228–238.
80.
Maydeu-OlivaresA., ShiD., & RosseelY. (2018). Assessing fit in structural equation models: A Monte-Carlo evaluation of RMSEA versus SRMR confidence intervals and tests of close fit. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 389–402.
81.
MbowC., Van NoordwijkM., LuedelingE., NeufeldtH., MinangP. A., & KoweroG. (2014). Agroforestry solutions to address food security and climate change challenges in Africa. Current Opinion in Environmental Sustainability, 6, 61–67.
82.
MenozziD., SogariG., & MoraC. (2015). Explaining vegetable consumption among young adults: An application of the theory of planned behaviour. Nutrients, 7(9), 7633–7650.
MeynardJ. M., CharrierF., FaresM. H., Le BailM., MagriniM. B., CharlierA., & MesséanA. (2018). Socio-technical lock-in hinders crop diversification in France. Agronomy for Sustainable Development, 38, 1–13.
85.
MichelsM., BonkeV., & MusshoffO. (2020). Understanding the adoption of smartphone apps in crop protection. Precision Agriculture, 21, 1209–1226.
86.
MilesJ., & ShevlinM. (2007). A time and a place for incremental fit indices. Personality and Individual Differences, 42(5), 869–874.
87.
MinotN., & RoyD. (2007). Impact of high-value agriculture and modern marketing channels on poverty: An analytical framework. International Food Policy Research Institute.
88.
MuellerR. O., & HancockG. R. (2018). Structural equation modeling. In The reviewer’s guide to quantitative methods in the social sciences (pp. 445–456). Routledge.
89.
MulaikS. A., JamesL. R., Van AlstineJ., BennettN., LindS., & StilwellC. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430.
90.
NjeruE. M. (2013). Crop diversification: A potential strategy to mitigate food insecurity by smallholders in sub-Saharan Africa. Journal of Agriculture, Food Systems, and Community Development, 3(4), 63–69.
91.
OcejaL., & BerenguerJ. (2009). Putting text in context: The conflict between pro-ecological messages and anti-ecological descriptive norms. Spanish Journal of Psychology, 12, 657–666.
92.
OrdenD., ToreroM., & GulatiA. (2004, March). Agricultural markets and the rural poor. Draft background paper for the workshop of the Poverty Reduction Network (POVNET) (Vol. 5).
93.
PingaliP. L., & RosegrantM. W. (1995). Agricultural commercialisation and diversification: Processes and policies. Food Policy, 20(3), 171–186.
94.
PonisioL. C., & EhrlichP. R. (2016). Diversification, yield and a new agricultural revolution: Problems and prospects. Sustainability, 8(11), 1118.
95.
PowerJ. D., SchlaggarB. L., Lessov-SchlaggarC. N., & PetersenS. E. (2013). Evidence for hubs in human functional brain networks. Neuron, 79(4), 798–813.
96.
PriceJ. C., & LevistonZ. (2014). Predicting pro-environmental agricultural practices: The social, psychological and contextual influences on land management. Journal of Rural Studies, 34, 65–78. https://doi.org/10.1016/j.jrurstud.2013.10.001
97.
ProkopyL. S., FloressK., Klotthor-WeinkaufD., & Baumgart-GetzA. (2008). Determinants of agricultural best management practice adoption: Evidence from the literature. Journal of Soil and Water Conservation, 63(5), 300–311.
98.
RahimniaF., & HassanzadehJ. F. (2013). The impact of website content dimension and e-trust on e-marketing effectiveness: The case of Iranian commercial saffron corporations. Information & Management, 50(5), 240–247.
99.
RaoP. P., BirthalP. S., & JoshiP. K. (2006). Diversification towards high value agriculture: Role of urbanisation and infrastructure. Economic & Political Weekly, 2747–2753.
100.
RaseduzzamanM. D., & JensenE. S. (2017). Does intercropping enhance yield stability in arable crop production? A meta-analysis. European Journal of Agronomy, 91, 25–33.
101.
RecklingM., BergkvistG., WatsonC. A., StoddardF. L., ZanderP. M., WalkerR. L., PristeriA., TonceaI., & BachingerJ. (2016). Trade-offs between economic and environmental impacts of introducing legumes into cropping systems. Frontiers in Plant Science, 7, 669.
102.
RehmanT., McKemeyK., YatesC. M., CookeR. J., GarforthC. J., TranterR. B., ParkJ. R., & DorwardP. T. (2007). Identifying and understanding factors influencing the uptake of new technologies on dairy farms in SW England using the theory of reasoned action. Agricultural Systems, 94, 281–293. https://doi.org/10.1016/j.agsy.2006.09.006
103.
ReimerA. P., WeinkaufD. K., & ProkopyL. S. (2012). The influence of perceptions of practice characteristics: An examination of agricultural best management practice adoption in two Indiana watersheds. Journal of Rural Studies, 28, 118–128. https://doi.org/10.1016/j.jrurstud.2011.09.005
104.
RenardD., & TilmanD. (2019). National food production stabilized by crop diversity. Nature, 571(7764), 257–260.
105.
RivisA., & SheeranP. (2003). Descriptive norms as an additional predictor in the theory of planned behaviour: A meta-analysis. Current Psychology, 22, 218–233.
106.
Roesch-McNallyG. E., ArbuckleJ. G., & TyndallJ. C. (2018). Barriers to implementing climate resilient agricultural strategies: The case of crop diversification in the US Corn Belt. Global Environmental Change, 48, 206–215.
RoussyC., RidierA., & ChaibK. (2017). Farmers’ innovation adoption behaviour: Role of perceptions and preferences. International Journal of Agricultural Resources, Governance and Ecology, 13(2), 138–161.
109.
RoyD., & ThoratA. (2007). Diversification in Indian agriculture towards high-value crops.
110.
RyanJ. G., & SpencerD. C. (2001). Future challenges and opportunities for agricultural R&D in the semi-arid tropics. International Crops Research Institute for the Semi-Arid Tropics.
111.
SadafA., NewbyT. J., & ErtmerP. A. (2012). Exploring factors that predict preservice teachers’ intentions to use Web 2.0 technologies using decomposed theory of planned behaviour. Journal of Research on Technology in Education, 45(2), 171–196.
112.
SavariM., & GharechaeeH. (2020). Application of the extended theory of planned behaviour to predict Iranian farmers’ intention for safe use of chemical fertilizers. Journal of Cleaner Production, 263, 121512. https://doi.org/10.1016/j.jclepro.2020.121512
113.
SenaviratnaN. A. M. R., & CoorayT. M. J. (2019). Diagnosing multicollinearity of logistic regression model. Asian Journal of Probability and Statistics, 5(2), 1–9.
114.
SengerI., BorgesJ. A. R., & MachadoJ. A. D. (2017). Using the theory of planned behaviour to understand the intention of small farmers in diversifying their agricultural production. Journal of Rural Studies, 49, 32–40.
115.
ShiD., & Maydeu-OlivaresA. (2020). The effect of estimation methods on SEM fit indices. Educational and Psychological Measurement, 80(3), 421–445.
116.
ShresthaN. (2020). Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39–42.
117.
SooraniF., & AhmadvandM. (2019). Determinants of consumers’ food management behaviour: Applying and extending the theory of planned behaviour. Waste Management, 98, 151–159. https://doi.org/10.1016/j.wasman.2019.08.025
118.
SteigerJ. H., & LindJ. C. (1980, May). Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA.
119.
TamaR. A. Z., YingL., YuM., HoqueM. M., AdnanK. M., & SarkerS. A. (2021). Assessing farmers’ intention towards conservation agriculture by using the Extended Theory of Planned Behaviour. Journal of Environmental Management, 280, 111654.
120.
TanakaJ. S. (1993). Multifaceted conceptions of fit in structural equation models. In BollenK. A. & LongJ. S. (Eds), Testing structural equation models (pp. 10–39). Sage Publications.
121.
TeklewoldH., KassieM., ShiferawB., & KöhlinG. (2013). Cropping system diversification, conservation tillage and modern seed adoption in Ethiopia: Impacts on household income, agrochemical use and demand for labor. Ecological Economics, 93, 85–93.
122.
TeranoR., MohamedZ., ShamsudinM. N., & LatifI. A. (2015). Factors influencing intention to adopt sustainable agriculture practices among paddy farmers in Kada, Malaysia. Asian Journal of Agricultural Research, 9(5), 268–275.
123.
TuckerL. R., & LewisC. (1973). The reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.
124.
UrrutyN., Tailliez-LefebvreD., & HuygheC. (2016). Stability, robustness, vulnerability and resilience of agricultural systems: A review. Agronomy for Sustainable Development, 36, 1–15.
125.
van DijkW. F., LokhorstA. M., BerendseF., & De SnooG. R. (2016). Factors underlying farmers’ intentions to perform unsubsidised agri-environmental measures. Land Use Policy, 59, 207–216.
126.
VeisiH., & ToulabiS. B. (2012, January). Factors influencing pro-environmental behaviour in agriculture. In International conference on ecological, environmental and biological sciences (pp. 346–349). Environmental Sciences Research Institute, Shahid Beheshti University Dubai.
127.
VelardeV. D. V. (2012). Determinants of online purchasing behaviour: An empirical investigation using an extension of the Theory of Planned Behaviour. Aarhus University.
128.
von BraunJ. (1995). Agricultural commercialization: Impact on income and nutrition and implications for policy. Food Policy, 20(3), 187–202.
129.
WangY., YangJ., LiangJ., QiangY., FangS., GaoM., FanX., YangG., ZhangB., & FengY. (2018). Analysis of the environmental behaviour of farmers for non-point source pollution control and management in a water source protection area in China. Science of The Total Environment, 633, 1126–1135. https://doi.org/10.1016/j.scitotenv.2018.03.273
130.
WautersE., & MathijsE. (2014). The adoption of farm-level soil conservation practices in developed countries: A meta-analytic review. International Journal of Agricultural Resources, Governance and Ecology, 10(1), 78–102.
131.
WautersE., BieldersC., PoesenJ., GoversG., & MathijsE. (2010). Adoption of soil conservation practices in Belgium: An examination of the theory of planned behaviour in the agri-environmental domain. Land Use Policy, 27(1), 86–94.
132.
WeinbergerK., & LumpkinT. A. (2007). Diversification into horticulture and poverty reduction: A research agenda. World Development, 35(8), 1464–1480.
133.
WezelA., CasagrandeM., CeletteF., VianJ.-F., FerrerA., & PeignéJ. (2014). Agroecological practices for sustainable agriculture: A review. Agronomy for Sustainable Development, 34(1), 1–20. https://doi.org/10.1007/s13593-013-0180-7
134.
WijantoS. H. (2008). Structural equation modeling with LISREL 8.8: Konsep & tutorial. Graha Ilmu.
135.
WillockJ., DearyI. J., Edwards-JonesG., GibsonG. J., McGregorM. J., SutherlandA., DentJ. B., MorganO., & GrieveR. (1999). The role of attitudes and objectives in farmer decision making: Business and environmentally-oriented behaviour in Scotland. Journal of Agricultural Economics, 50(2), 286–303.
136.
WollniM., LeeD. R., & ThiesJ. E. (2010). Conservation agriculture, organic marketing, and collective action in the Honduran hillsides. Agricultural Economics, 41(3–4), 373–384.
137.
YamanoT., RajendranS., & MalabayabasM. (2013, October). Determinants of psychological constructs toward agricultural technology adoption: Evidence from Eastern India. In 87th Annual Conference, Warwick University, Coventry, UK from Agricultural Economics Society. Retrieved February (Vol. 17, p. 2016).
138.
YazdanpanahM., HayatiD., Hochrainer-StiglerS., & ZamaniG. H. (2014). Understanding farmers’ intention and behavior regarding water conservation in the Middle-East and North Africa: A case study in Iran. Journal of Environmental Management, 135, 63–72.
139.
YudiawanA., SunarsoB., & SariF. (2021). Successful online learning factors in COVID-19 era: Study of Islamic higher education in West Papua, Indonesia. International Journal of Evaluation and Research in Education, 10(1), 193–201.
140.
ZhouH. H., RomeroS. B., & QinX. (2016). An extension of the theory of planned behavior to predict pedestrians’ violating crossing behavior using structural equation modeling. Accident Analysis & Prevention, 95, 417–424.
141.
ZschockeT., BeniestJ., AissétouD. Y., & ChakeredzaS. (2013). Readiness to use e-learning for agricultural higher education in Sub-Saharan Africa: Results from a survey of faculty members. Journal of Agricultural Informatics, 4(1), 37–47. http://www.magisz.org/journal.
