Factors Influencing Adoption of the PlantVillage Nuru Application for Cassava Mosaic Disease Diagnosis Among Farmers in Benin
Abstract
:1. Introduction
2. Materials and Methods
2.1. Theoretical Framework for the Adoption of the PlantVillage Nuru Application
- Relative Advantage: This is the degree to which an innovation is perceived as superior to the idea it replaces. In the context of our study, this translates to the perceived benefits of adopting the PlantVillage Nuru application compared to traditional methods of identifying symptoms and managing cassava diseases. The Nuru application provides a significant advantage by allowing farmers to quickly and accurately diagnose plant diseases, such as cassava mosaic disease, using just their smartphones [10]. This technology can reduce crop losses and increase yields, making it a major economic benefit.
- Compatibility: This is the extent to which an innovation is perceived as being compatible with existing values, past experiences and the needs of potential adopters. This could be explored by examining how the application integrates into existing agricultural practices and the value systems of farmers.
- Complexity: This is the degree to which an innovation is perceived as difficult to understand and use. This could be evaluated by examining the ease of use of the application and the availability of training and support. The Nuru application has been designed with the intention of being user-friendly, even for farmers with limited technological skills. The user interface is intuitive, and the instructions are clear, which serves to reduce the perceived complexity [11].
- Trialability: This is the extent to which an innovation is subjected to a limited scale trial prior to its full adoption. In order to assess the trialability of an innovation, it is essential to ascertain whether farmers are able to implement it on a small scale prior to making a definitive commitment to it. To illustrate this, in the case of the Nuru application for the management of cassava diseases, farmers could utilize it to diagnose diseases on a limited portion of their field. They could then compare the results obtained with those derived from traditional methods. This approach would permit them to evaluate the efficacy of the application before implementing it on a larger scale.
- Observability: This refers to the degree to which the results of an innovation are visible to others. It can be assessed by examining how the positive effects of using the application are shared and observed by other farmers. In the case of the Nuru application, neighboring farmers who utilize the app may notice a decrease in disease symptoms and an overall improvement in plant health. This visibility makes the benefits of the Nuru application readily observable.
2.2. Study Area
2.3. Data Collection
2.4. Econometric Model
2.5. Explanatory Variable of the Econometric Model
2.6. Test of Multicollinearity
2.7. Data Analysis
3. Results
3.1. Characteristics of Farmers
3.2. Factors Determining the Adoption of the PlantVillage Nuru Application
4. Discussion
4.1. Analysis of the Proportion of Adopters and Non-Adopters of the PlantVillage Nuru Application
4.2. Analysis of the Determinants of the Adoption of the PlantVillage Nuru Application
4.2.1. Participation in Training and Awareness-Raising Sessions
4.2.2. Possession of an Android Smartphone
4.2.3. Education Level
4.2.4. Knowledge About CMD
4.2.5. Crop Association
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ng, H.F.; Lin, C.-Y.; Chuah, J.H.; Tan, H.K.; Leung, K.H. Plant Disease Detection Mobile Application Development Using Deep Learning. In Proceedings of the 2021 International Conference on Computer & Information Sciences (ICCOINS), Kuching, Malaysia, 13–15 July 2021; pp. 34–38. [Google Scholar]
- Fauquet, C. African Cassava Mosaic Virus: Etiology, Epidemiology, and Control. Plant Dis. 1990, 74, 404. [Google Scholar] [CrossRef]
- Patil, B.L.; Fauquet, C.M. Cassava Mosaic Geminiviruses: Actual Knowledge and Perspectives. Mol. Plant Pathol. 2009, 10, 685–701. [Google Scholar] [CrossRef] [PubMed]
- Ally, H.M.; Hamss, H.E.; Simiand, C.; Maruthi, M.N.; Colvin, J.; Omongo, C.A.; Delatte, H. What Has Changed in the Outbreaking Populations of the Severe Crop Pest Whitefly Species in Cassava in Two Decades? Sci. Rep. 2019, 9, 14796. [Google Scholar] [CrossRef] [PubMed]
- Houngue, J.A.; Houédjissin, S.S.; Ahanhanzo, C.; Pita, J.S.; Houndénoukon, M.S.E.; Zandjanakou-Tachin, M. Cassava Mosaic Disease (CMD) in Benin: Incidence, Severity and Its Whitefly Abundance from Field Surveys in 2020. Crop Prot. 2022, 158, 106007. [Google Scholar] [CrossRef] [PubMed]
- Houngue, J.A.; Pita, J.S.; Cacaï, G.H.T.; Zandjanakou-Tachin, M.; Abidjo, E.A.E.; Ahanhanzo, C. Survey of Farmers’ Knowledge of Cassava Mosaic Disease and Their Preferences for Cassava Cultivars in Three Agro-Ecological Zones in Benin. J. Ethnobiol. Ethnomed. 2018, 14, 29. [Google Scholar] [CrossRef]
- Chikoti, P.C.; Shanahan, P.; Melis, R. Evaluation of Cassava Genotypes for Resistance to Cassava Mosaic Disease and Agronomic Traits. AJPS 2016, 7, 1122–1128. [Google Scholar] [CrossRef]
- Eni, A.O.; Efekemo, O.P.; Onile-ere, O.A.; Pita, J.S. South West and North Central Nigeria: Assessment of Cassava Mosaic Disease and Field Status of African Cassava Mosaic Virus and East African Cassava Mosaic Virus. Ann. Appl. Biol. 2021, 178, 466–479. [Google Scholar] [CrossRef]
- Kreuze, J.; Adewopo, J.; Selvaraj, M.; Mwanzia, L.; Kumar, P.L.; Cuellar, W.J.; Legg, J.P.; Hughes, D.P.; Blomme, G. Innovative digital technologies to monitor and control pest and disease threats in root, tuber, and banana (RT&B) cropping systems: Progress and prospects. In Root, Tuber and Banana Food System Innovations. Value Creation for Inclusive Outcomes, 1st ed.; Thiele, G., Friedmann, M., Campos, H., Polar, V., Bentley, J.W., Eds.; Springer International Publishing: Cham, Switzerland, 2022; Volume 1, pp. 261–288. [Google Scholar]
- Mrisho, L.M.; Mbilinyi, N.A.; Ndalahwa, M.; Ramcharan, A.M.; Kehs, A.K.; McCloskey, P.C.; Murithi, H.; Hughes, D.P.; Legg, J.P. Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava–CMD and CBSD. Front. Plant Sci. 2020, 11, 590889. [Google Scholar] [CrossRef]
- Adjei, E.A.; Traore, K.; Amoakon, W.J.L.; Kouassi, N.K.I.; Kouassi, K.M.; Pita, J.S. Perception and adoption by cassava farmers of the PlantVillage Nuru application disseminated in the agricultural environment of Côte d’Ivoire: A case study in the departments of Dabou, Bouaké and Man. Front. Agron. 2024, 6, 1433204. [Google Scholar] [CrossRef]
- Amon-Armah, F.; Domfeh, O.; Baah, F.; Owusu-Ansah, F. Farmers’ Adoption of Preventive and Treatment Measures of Cocoa Swollen Shoot Virus Disease in Ghana. J. Agric. Food Res. 2021, 3, 100112. [Google Scholar] [CrossRef]
- Bandi, M.M.; Mahimba, M.B.; Mbe Mpie, P.M.; M’vubu, A.R.N.; Khasa, D.P. Adoption of Agroforestry Practices in and around the Luki Biosphere Reserve in the Democratic Republic of the Congo. Sustainability 2022, 14, 9841. [Google Scholar] [CrossRef]
- Piot-Lepetit, I.; Florez, M.; Gauche, K. Digitalisation des exploitations agricoles—Déterminants et impacts de l’adoption des innovations numériques. TechInn 2023, 8, 1–15. [Google Scholar] [CrossRef]
- Rogers, E.M.; Adhikarya, R. Diffusion of Innovations: An Up-to-Date Review and Commentary. Ann. Int. Commun. Assoc. 1979, 3, 67–81. [Google Scholar] [CrossRef]
- Available online: https://dsa.agriculture.gouv.bj/statistics/vegetale (accessed on 18 May 2024).
- Yabi, J.A.; Bachabi, F.X.; Labiyi, I.A.; Ode, C.A.; Ayena, R.L. Déterminants socio-économiques de l’adoption des pratiques culturales de gestion de la fertilité des sols utilisées dans la commune de Ouaké au Nord-Ouest du Bénin. Int. J. Biol. Chem. Sci. 2016, 10, 779. [Google Scholar] [CrossRef]
- Hurlin, C. Econométrie des Variables Qualitatives, Polycopié de Cours. Master’s Thesis, Université d’Orléans, Orléans, France, 2003; pp. 10–12. [Google Scholar]
- Sale, A.; Folefack, D.; Obwoyere, G.; Lenah Wati, N.; Lendzemo, W.; Wakponou, A. Changements climatiques et déterminants d’adoption de la fumure organique dans la région semi-aride de Kibwezi au Kenya. Int. J. Biol. Chem. Sci. 2014, 8, 680. [Google Scholar] [CrossRef]
- Jogo, W.; Karamura, E.; Tinzaara, W.; Kubiriba, J.; Rietveld, A. Determinants of Farm-Level Adoption of Cultural Practices for Banana Xanthomonas Wilt Control in Uganda. J. Agric. Sci. 2013, 5, 70. [Google Scholar] [CrossRef]
- Acheampong, P.P.; Acheampong, L.D. Analysis of adoption of Improved cassava (Manihot esculenta) varieties in Ghana: Implications for agricultural technology disseminations. Int. J. Food Agric. Econ. 2020, 8, 233–246. [Google Scholar]
- Alkire, S.; Foster, J. Counting and Multidimensional Poverty Measurement. J. Public Econ. 2011, 95, 476–487. [Google Scholar] [CrossRef]
- Daoud, J.I. Multicollinearity and Regression Analysis. J. Phys. Conf. Ser. 2017, 949, 012009. [Google Scholar] [CrossRef]
- Foucart, T. Colinéarité et régression linéaire. Math. Soc. Sci. Hum. 2006, 173, 5–25. [Google Scholar] [CrossRef]
- Kouassi, J.-L.; Diby, L.; Konan, D.; Kouassi, A.; Bene, Y.; Kouamé, C. Drivers of Cocoa Agroforestry Adoption by Smallholder Farmers around the Taï National Park in Southwestern Côte d’Ivoire. Sci. Rep. 2023, 13, 14309. [Google Scholar] [CrossRef] [PubMed]
- Johnston, R.; Jones, K.; Manley, D. Confounding and Collinearity in Regression Analysis: A Cautionary Tale and an Alternative Procedure, Illustrated by Studies of British Voting Behaviour. Qual. Quant. 2018, 52, 1957–1976. [Google Scholar] [CrossRef] [PubMed]
- Teno, G.; Lehrer, K.; Koné, A. Les facteurs de l’adoption des nouvelles technologies en agriculture en Afrique Subsaharienne: Une revue de la littérature. Afr. J. Agric. Resour. Econ. 2018, 13, 140–151. [Google Scholar]
- Menghistu, H.T.; Abraha, A.Z.; Tesfay, G.; Mawcha, G.T. Determinant Factors of Climate Change Adaptation by Pastoral/Agro-Pastoral Communities and Smallholder Farmers in Sub-Saharan Africa: A Systematic Review. Int. J. Clim. Chang. Strateg. Manag. 2020, 12, 305–321. [Google Scholar] [CrossRef]
- Ullah, S.; Agyei-Boapeah, H.; Kim, J.R.; Nasim, A. Does National Culture Matter for Environmental Innovation? A Study of Emerging Economies. Technol. Forecast. Soc. Chang. 2022, 181, 121755. [Google Scholar] [CrossRef]
- Abebaw, L.; Birru, W.T.; Alemu, D. Determinants of Commercializing Crop Outputs of Smallholder Farmers in West Gojjam Zone, North-Western Ethiopia. East Afr. J. Sci. 2023, 17, 19–32. [Google Scholar]
- Thar, S.P.; Ramilan, T.; Farquharson, R.J.; Pang, A.; Chen, D. An Empirical Analysis of the Use of Agricultural Mobile Applications among Smallholder Farmers in Myanmar. Electron. J. Inf. Syst. Dev. Ctries. 2021, 87, e12159. [Google Scholar] [CrossRef]
- Michels, M.; Fecke, W.; Feil, J.-H.; Musshoff, O.; Pigisch, J.; Krone, S. Smartphone Adoption and Use in Agriculture: Empirical Evidence from Germany. Precis. Agric. 2020, 21, 403–425. [Google Scholar] [CrossRef]
- Islam, S.M.; Grönlund, Å.G. Factors Influencing the Adoption of Mobile Phones among the Farmers in Bangladesh: Theories and Practices. Int. J. Adv. ICT Emerg. Reg. 2012, 4, 4–14. [Google Scholar] [CrossRef]
- Frimpong, N.B.; Oppong, A.; Prempeh, R.; Appiah-Kubi, Z.; Abrokwah, A.L.; Mochiah, B.M.; Lamptey, N.J.; Manu-Aduening, J.; Pita, S. Farmers’ knowledge, attitudes and practices towards management of cassava pests and diseases in forest transition and Guinea savannah agro-ecological zones of Ghana. Gates Open Res. 2020, 4, 101. [Google Scholar] [CrossRef]
- Touré, O.Y.; Sanni Worogo, J.S.B.; Tchemadon, G.C.; Nebie, B.; Afouda, L.A.C.; Achigan Dako, E.G. Farmers’ Knowledge and Perception on Kersting’s Groundnut (Macrotyloma geocarpum) Diseases and Pests in Benin. Plant Dis. 2023, 107, 1861–1866. [Google Scholar] [CrossRef] [PubMed]
- Duchene, O.; Vian, J.-F.; Celette, F. Intercropping with Legume for Agroecological Cropping Systems: Complementarity and Facilitation Processes and the Importance of Soil Microorganisms. A Review. Agric. Ecosyst. Environ. 2017, 240, 148–161. [Google Scholar] [CrossRef]
- Parmar, A.; Sturm, B.; Hensel, O. Crops That Feed the World: Production and Improvement of Cassava for Food, Feed, and Industrial Uses. Food Secur. 2017, 9, 907–927. [Google Scholar] [CrossRef]
- Weerarathne, L.V.Y.; Marambe, B.; Chauhan, B.S. Does Intercropping Play a Role in Alleviating Weeds in Cassava as a Non-Chemical Tool of Weed Management?—A Review. Crop Prot. 2017, 95, 81–88. [Google Scholar] [CrossRef]
- Bellotti, A.C.; Arias, B. Host Plant Resistance to Whiteflies with Emphasis on Cassava as a Case Study. Crop Prot. 2001, 20, 813–823. [Google Scholar] [CrossRef]
- Delaquis, E.; de Haan, S.; Wyckhuys, K.A.G. On-Farm Diversity Offsets Environmental Pressures in Tropical Agro-Ecosystems: A Synthetic Review for Cassava-Based Systems. Agric. Ecosyst. Environ. 2018, 251, 226–235. [Google Scholar] [CrossRef]
- Fondong, V.N.; Thresh, J.M.; Zok, S. Spatial and Temporal Spread of Cassava Mosaic Virus Disease in Cassava Grown Alone and When Intercropped with Maize and/or Cowpea. J. Phytopathol. 2002, 150, 365–374. [Google Scholar] [CrossRef]
- Night, G.; Asiimwe, P.; Gashaka, G.; Nkezabahizi, D.; Legg, J.P.; Okao-Okuja, G.; Obonyo, R.; Nyirahorana, C.; Mukakanyana, C.; Mukase, F.; et al. Occurrence and Distribution of Cassava Pests and Diseases in Rwanda. Agric. Ecosyst. Environ. 2011, 140, 492–497. [Google Scholar] [CrossRef]
PDA | Department | Commune | Number of Sampled Farmers |
---|---|---|---|
PDA 5 (Zou–Couffo) | Couffo | Aplahoué | 13 |
Djakotomey | 11 | ||
Dogbo | 11 | ||
Klouékanmey | 10 | ||
Zou | Agbangnizoun | 6 | |
Covè | 10 | ||
Ouinhi | 14 | ||
Zagnanado | 12 | ||
PDA 6 (Plateau) | Plateau | Adja-Ouèrè | 13 |
Ifangni | 10 | ||
Kétou | 14 | ||
Pobè | 16 | ||
Sakété | 17 | ||
PDA 7 (Ouémé–Atlantique–Mono) | Atlantique | Abomey-Calavi | 12 |
Kpomassè | 11 | ||
Toffo | 11 | ||
Tori-Bossito | 14 | ||
Zè | 11 | ||
Ouémé | Adjohoun | 11 | |
Akpro-Missrété | 11 | ||
Adjarra | 9 | ||
Avrankou | 11 | ||
Bonou | 13 | ||
Dangbo | 9 | ||
Mono | Comè | 12 | |
Houéyogbé | 13 | ||
Total | 305 |
Variables | Description and Justification for the Variable Selected | Expected Signs |
---|---|---|
Dependent variables | ||
Adoption of the intelligent application PlantVillage Nuru (ADOPTNURU) | 0—Non-adoption of the intelligent application PlantVillage Nuru and 1—Adoption of the PlantVillage Nuru application | |
Independent Variables | ||
Age of the farmer (AGE) | The age of the farmer is a continuous variable. It is possible that younger farmers may exhibit risk aversion and be less inclined to adopt new technologies. Conversely, older farmers may possess greater experience and resources that could facilitate the adoption process [21]. This implies that age may be a significant factor in the adoption of new technologies, such as the PlantVillage Nuru application, among farmers. | +/− |
Gender (GEN) | Dichotomous variable (0 = Female; 1 = Male). Gender plays a significant role in technology adoption due to disparities in access to information and extension services between men and women. However, our hypothesis is that men are more likely to adopt the Nuru application than women. This belief is based on the premise that women often have limited access to smartphones and the internet compared to men, which could potentially hinder their ability to use applications like PlantVillage Nuru [11]. | − |
Education level (EDULEVEL) | Discrete variable with four categories: (i) Unschooled; (ii) Primary; (iii) Secondary; and (iv) Higher/Tertiary. Education plays a pivotal role in enabling farmers to effectively acquire and synthesize information and knowledge about the problem and technologies, which is a crucial factor in the adoption of technologies [17]. It should be noted that the intelligent application PlantVillage Nuru requires a minimum level of understanding of the French language. | + |
Marital status (MARSTATUS) | The marital status of an individual can influence their access to resources, decision-making abilities and the distribution of work within the household. These factors can subsequently affect the adoption of new technologies. In this context, the authors of Ref. [13] have demonstrated that marital status exerts a positive influence on the adoption of agroforestry. | +/− |
Possession of an Android phone (ANDROID) | This is a dichotomous variable defined as a categorical variable with two possible values (0 = No and 1 = Yes). PlantVillage Nuru is an intelligent application that is only compatible with Android devices [10]. | + |
Distance between the field and the house (FIELDDIST) | This is a continuous variable. The distance between the field and the house may have a positive or negative influence on the adoption of the PlantVillage Nuru application. Indeed, if the field is situated in close proximity to the house, it may be more convenient for the farmer to conduct regular monitoring of their field and utilize the application to identify diseases. Additionally, the quality of the internet connection may vary depending on the location of the field. | +/− |
Cassava production area (CASPRODAREA) | The cassava production area is a continuous variable. It is plausible to suggest that farmers with a larger cassava production area may be more inclined to adopt the application to monitor and manage CMD in their fields. This would permit them to conserve time by promptly identifying cassava diseases on their farms. Consequently, the extent to which the cassava production area may have a positive or negative influence on the adoption of the PlantVillage Nuru application remains to be seen. | +/− |
Crop association or intercropping (CROPASSOC) | Dichotomous variable (0 = No and 1 = Yes ). The practice of crop association has been identified as a potential risk factor that could potentially increase the incidence of CMD in cassava fields [5]. Consequently, farmers who have previously engaged in intercropping may be more likely to adopt the PlantVillage Nuru application. | + |
General knowledge about CMD (CMDKNOWLEDGE) | The general knowledge of CMD (continuous variable) was quantified by calculating the ratio between the score obtained and the total possible score. The score was determined by aggregating the following sub-components: identification of symptoms, knowledge of the name of the disease, knowledge of the cause of the disease, knowledge of the mode of disease propagation and knowledge of disease prevention strategies [22]. Consequently, the final score represents the overall level of disease knowledge. It can be reasonably assumed that farmers with a high level of disease knowledge are more likely to adopt disease control and management measures [12]. Furthermore, a comprehensive grasp of CMD could empower farmers to more effectively interpret the information provided by the application, thereby facilitating informed decision-making regarding the management of their crops. This suggests that the general knowledge about CMD could play a significant role in the use of the PlantVillage Nuru application. | + |
Participation in training and awareness sessions (TRAINPARTIC) | This is a dichotomous variable (0 = No and 1 = Yes). The participation of farmers in awareness and training campaigns on the use of the PlantVillage Nuru application allows them to become aware of the application, its use and its importance in the management of CMD. This indicates that participation in such campaigns could be a pivotal factor in the adoption of the PlantVillage Nuru application. | + |
Qualitative Variables | Modalities | Percentage | p-Value | ||
---|---|---|---|---|---|
Entire Sample (n = 305) | Adoption of Nuru (n = 43) | Non-Adoption of Nuru (n = 262) | chi2 | ||
Gender | Male | 82.95 | 95.35 | 80.92 | 0.020 |
Female | 17.05 | 4.65 | 19.08 | ||
Marital status | Single | 3.28 | 4.65 | 3.06 | 0.537 |
Married | 95.74 | 93.02 | 96.18 | ||
Widowed | 0.98 | 2.33 | 0.76 | ||
Education level | Unschooled | 33.11 | 11.64 | 36.64 | 0.001 |
Primary | 20 | 18.6 | 20.23 | ||
Secondary | 34.75 | 44.18 | 33.21 | ||
Higher | 12.14 | 25.58 | 9.92 | ||
Number of years in cassava production | 1 to 5 years | 13.77 | 11.63 | 14.12 | 0.327 |
6 to 10 years | 23.61 | 32.56 | 22.14 | ||
11 years plus | 62.62 | 55.81 | 63.74 | ||
Possession of an Android phone | Yes | 73.11 | 97.67 | 69.08 | 0.000 |
No | 26.89 | 2.33 | 30.92 | ||
Member of a cassava association | Yes | 60.66 | 72.09 | 58.78 | 0.098 |
No | 39.34 | 27.91 | 41.22 | ||
Quantitative Variables | Mean (Standard error) | t-test | |||
General knowledge about CMD (score) | 5.47 (0.19) | 9.56 (0.24) | 4.79 (0.18) | 0.000 | |
Age of farmers (years) | 44.92 (0.68) | 43.83 (1.83) | 45.09 (0.73) | 0.518 | |
Size of households | 8.25 (0.28) | 8.09 (0.75) | 8.28 (0.30) | 0.813 | |
Cassava production area (ha) | 2.63 (0.23) | 3.21 (061) | 2.53 (0.24) | 0.295 | |
Distance field–house (km) | 7.25 (0.35) | 7.18 (1.13) | 7.26 (0.36) | 0.931 |
Independent Variables | Coef. | Odds Ratio | St. Err. | t-Value | p-Value | [95% Conf | Interval] | Sig |
---|---|---|---|---|---|---|---|---|
TRAINPARTIC | 1.977 | 7.218 | 0.599 | 3.30 | 0.001 | 0.803 | 3.15 | *** |
CMDKNOWLEDGE | 0.604 | 1.829 | 0.138 | 4.38 | 0.000 | 0.334 | 0.874 | *** |
ANDROID | 2.914 | 18.436 | 1.176 | 2.48 | 0.013 | 0.609 | 5.219 | ** |
EDULEVEL | 0 | 1 | - | - | - | - | - | |
Primary | 0.104 | 1.11 | 0.776 | 0.13 | 0.893 | −1.416 | 1.625 | |
Secondary | 1.266 | 3.547 | 0.739 | 1.71 | 0.087 | −0.182 | 2.714 | * |
Higher | 1.326 | 3.766 | 0.957 | 1.39 | 0.166 | −0.55 | 3.202 | |
GEN | 0 | 1 | - | - | - | - | - | |
Female | −0.572 | 0.564 | 0.988 | −0.58 | 0.563 | −2.509 | 1.365 | |
CROPASSOC | 0 | 1 | - | - | - | - | - | |
Crop association | 1.528 | 4.608 | 0.621 | 2.46 | 0.014 | 0.311 | 2.744 | ** |
AGE | −0.018 | 0.983 | 0.026 | −0.68 | 0.494 | −0.068 | 0.033 | |
MARSTATUS | 0 | 1 | - | - | - | - | - | |
Married | −0.69 | 0.502 | 1.138 | −0.61 | 0.545 | −2.921 | 1.541 | |
Widowed | 5.056 | 156.912 | 8.73 | 0.58 | 0.563 | −12.055 | 22.166 | |
FIELDDIST | −0.0032 | 0.969 | 0.035 | −0.90 | 0.37 | −0.101 | 0.037 | |
CASPRODAREA | 0.048 | 1.049 | 0.042 | 1.14 | 0.255 | −0.035 | 0.131 | |
Constant | −10.516 | 0 | 2.496 | −4.21 | 0 | −15.408 | −5.624 | *** |
Mean dependent var | 0.141 | SD dependent var | 0.349 | |||||
Pseudo r-squared | 0.537 | Number of obs | 305 | |||||
Chi-square | 133.154 | Prob > chi2 | 0.000 | |||||
Akaike crit. (AIC) | 142.961 | Bayesian crit. (BIC) | 195.045 |
Coeff. | Marginal Effect (dy/dx) | ) | p > z | [95% | ||
---|---|---|---|---|---|---|
TRAINPARTIC | 1.977 | 0.115 | 7.218 | 0.878 | 0.056 | 0.175 |
CMDKNOWLEDGE | 0.604 | 0.035 | 1.829 | 0.647 | 0.022 | 0.049 |
ANDROID | 2.914 | 0.170 | 18.436 | 0.946 | 0.043 | 0.297 |
EDULEVEL | 0 | 1 | ||||
Primary | 0.104 | 0.005 | 1.11 | 0.53 | −0.072 | 0.083 |
Secondary | 1.266 | 0.073 | 3.547 | 0.78 | −0.005 | 0.151 |
Higher | 1.326 | 0.077 | 3.766 | 0.79 | −0.033 | 0.187 |
CROPASSOC | 0 | 1 | ||||
Crop association | 1.528 | 0.085 | 4.608 | 0.822 | 0.026 | 0.144 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ahoya, D.K.D.; Sawadogo-Compaore, E.M.F.W.; Yabi, J.A.; Zandjanakou-Tachin, M.; Houngue, J.A.; Houedjissin, S.S.; Pita, J.S.; Ahanhanzo, C. Factors Influencing Adoption of the PlantVillage Nuru Application for Cassava Mosaic Disease Diagnosis Among Farmers in Benin. Agriculture 2024, 14, 2001. https://doi.org/10.3390/agriculture14112001
Ahoya DKD, Sawadogo-Compaore EMFW, Yabi JA, Zandjanakou-Tachin M, Houngue JA, Houedjissin SS, Pita JS, Ahanhanzo C. Factors Influencing Adoption of the PlantVillage Nuru Application for Cassava Mosaic Disease Diagnosis Among Farmers in Benin. Agriculture. 2024; 14(11):2001. https://doi.org/10.3390/agriculture14112001
Chicago/Turabian StyleAhoya, Dèwanou Kant David, Eveline Marie Fulbert Windinmi Sawadogo-Compaore, Jacob Afouda Yabi, Martine Zandjanakou-Tachin, Jerome Anani Houngue, Serge Sètondji Houedjissin, Justin Simon Pita, and Corneille Ahanhanzo. 2024. "Factors Influencing Adoption of the PlantVillage Nuru Application for Cassava Mosaic Disease Diagnosis Among Farmers in Benin" Agriculture 14, no. 11: 2001. https://doi.org/10.3390/agriculture14112001
APA StyleAhoya, D. K. D., Sawadogo-Compaore, E. M. F. W., Yabi, J. A., Zandjanakou-Tachin, M., Houngue, J. A., Houedjissin, S. S., Pita, J. S., & Ahanhanzo, C. (2024). Factors Influencing Adoption of the PlantVillage Nuru Application for Cassava Mosaic Disease Diagnosis Among Farmers in Benin. Agriculture, 14(11), 2001. https://doi.org/10.3390/agriculture14112001