Assessing the Factors Influencing the Intent to Utilize the Information Generated by the Rice Crop Manager Digital Application in Pila, Laguna, Philippines

Date

3-2024

Abstract

Rice is the primary choice of Filipinos despite the presence of other staple foods such as noodles and bread (Pelegrino, 2020). According to Statista (2022), the per capita rice utilization in the Philippines was at 136 kilograms (kg) per year. This value increased compared to the per capita rice utilization in the Philippines in 2016 which was at 108 kg/year.

While rice serves as a major and staple crop of the country, the production of the commodity faces challenges in view of various factors (e.g., drought, flooding, among others) resulting to low rice yield and production. According to the Department of Agriculture- Bureau of Agricultural Research (DA-BAR), 2016 posed as one of the lowest recorded palay production in the Philippines due to the El Nino phenomenon. On another note, it was also mentioned by Mataia et al. (2020), as cited by Department of Agriculture- Bureau of Agricultural Research & University of the Philippines Los Baños (2022), that the production constraints faced by the Philippine rice production were 1) low yield, 2) high production cost, 3) inadequate water supply, 4) climate change, 5) limited drying and storage facilities, 6) mismatch of farmer’s preference for high-quality variety seeds, 7) pests and diseases, 8) low adoption of new crop management technologies, 9) low access to low cost credit, 10) crop insurance, and 11) unstable price resulting to low farm income. In response to the challenges mentioned, the National Agriculture and Fisheries Research for Development and Extension Agenda (NAREA 2023-2028) laid down priority research areas containing the expected technologies to mitigate and address the challenges mentioned. One of the research areas identified was on the field of precision and digital farming (Department of Agriculture- Bureau of Agricultural Research & University of the Philippines Los Baños, 2022).

The Rice Crop Manager (RCM) was developed as a digital agriculture platform. Its capability includes the provision of reliable and credible nutrient and crop management recommendation for the reference of the rice farmer. In partnership with the Department of Agriculture, through the Philippine Rice Research Institute (PhilRice), the International Rice Research Institute (IRRI) co- developed the technology to provide location-specific fertilizer management recommendations for the rice farmers nationwide disseminated through an Android application in a mobile device. Through the active participation of the Agricultural Extension Workers (AEWs) within Local Government Units (LGUs) and rice farmers, the mobile application has approximately distributed two (2) million recommendations. In general, the increase in yield averaged to about 392 kg/ha/cropping season and has provided an average additional net income of US $122 per ha/cropping season (International Rice Research Institute & Department of Agriculture- Philippine Rice Research Institute, 2021).

The RCM mobile application was be shown to meet its yearly targeted recommendations. However, the International Rice Research Institute & Department of Agriculture- Philippine Rice Research Institute in 2021 still cited issues and challenges in terms of its use and adoption. Specifically, the case study report by the International Rice Research Institute & Department of Agriculture- Philippine Rice Research Institute, (2021) stated that the adoption rate was only 27%. The identified gaps include the lack of information and data on the behavioral and social profiles of the intended users relative to the use of the RCM generated recommendations. To address the gaps identified by the IRRI and PhilRice, this study explored the use of the UTAUT by Venkatesh et al. (2012) as a tool in determining and analyzing the factors affecting the behavioral intention of rice farmers to use the RCM recommendations. This includes measuring the 1) Performance expectancy (PE), 2) Effort expectancy (EE), 3) Social influence (SI), 4) Facilitating conditions (FC), 5), Hedonic motivation (HM), and 6) Habit (HB) among rice farmers in Pila, Laguna and its effect towards the behavioral intention to use the RCM recommendations.

The research employed a descriptive and causal type of research. Furthermore, through simple random sampling, self-administered questionnaires (SAQs) were disseminated among 60 rice farmers in Pila, Laguna. The SAQs, with a four-point Likert scale, was used to measure the rice farmers’ responses relative to the independent variables (e.g., performance expectancy, effort expectancy, among others) and the dependent variable (i.e., behavioral intention). On the other hand, key informant interviews (KIIs) were conducted with five key staff from the DA-Regional Field Office IV-A and Municipal Agriculturists (MAO) of Pila, Laguna to further validate and enhance the quantitative data obtained from the SAQs.

The study described the profile of the farmers and their respective farm areas in Pila, Laguna. The results of the KIIs showed that there are a total of 962 farmers registered in Pila, Laguna. With respect to the total population, only 60 rice farmers had valid responses within the questionnaires disseminated. In terms of age classification, all the respondents of the survey fall into the older age category ranging to 35-79 years old. Additionally, the number of female and male respondents for the study was 20 and 40 respectively.

Relative to farm characteristics, the total land area of registered rice farmers at Pila, Laguna is 1,298 hectares. Rice varieties being used are Tubigan 17 (216), Mabango 3 (218), and GSR 8 (480). Based on the interview with the agricultural technicians, the rice production in the area can go as high as 120 MT for the dry season while 80 to 100 MT during the wet season. In terms of traditional fertilizer management practices, the agricultural technicians mentioned that most farmers rely on rough estimates for nitrogen, phosphorus, and potassium (NPK) before the utilization of the RCM generated recommendations.

The study used Partial Least Squares-Structural Equation Modelling (PLS- SEM), through the Smart PLS application, to determine and analyze the significant x factors affecting the behavioral intention to use the recommendations (i.e. the information) generated by the RCM technology and determine the reliability and validity of the model developed. Based on the results generated, the outer loadings of the items relative to the independent variables were higher than 0.70, a standard value according to Cinzi et al. (2010). This means the items (e.g., job-fit) within the questionnaire can accurately measure the constructs (e.g., performance expectancy) they are trying to measure. Through the Smart PLS application, path coefficient analysis was also done to determine the significant and non-significant factors affecting behavioral intention to use the RCM recommendations within rice farmers in Pila, Laguna. The results of the path coefficient analysis through PLS-SEM accepted hypotheses HA5 and HA4 which were for independent variables namely 1) Hedonic motivation (HM) and 2) Facilitating conditions (FC) respectively. On the other hand, hypotheses HA6, HA1, HA3, HA2 for the independent variables namely 1) Habit (HB), 2) Performance expectancy (PE), 3) Social influence, and 4) Effort expectancy respectively was rejected.

HM and FC with p-values of 0.005 and 0.0332 respectively are identified to be significant factors affecting the behavioral intention of rice farmers to use the RCM recommendations. The intensive support system provided and established by the Municipal Agriculturist Office (MAO) of Pila, Laguna and the DA-Regional Field Office (DA-RFO) IV-A enabled the rice farmers in Pila, Laguna to effectively use the RCM recommendations. This included having enabling mechanisms through the provision of input subsidies and technical support on the use of the recommendations. On the other hand, the realization of the nutrient and fertilizer related benefits promised by the RCM recommendations compared to traditional fertilizer management practices provided higher extrinsic and intrinsic motivation for the farmers. Consequently, the presence of extrinsic rewards (i.e. higher yields due to accurate fertilizer recommendation application) and other enabling factors (i.e. presence of technical assistance and one-on-one interviews) contributed to the ease of using the technology and realization of its benefits relative to rice farming. In return, the higher extrinsic motivation higher enthusiasm and joy from realizing the perceived benefits increased the rice farmers’ respective motivation and eventually their behavioral intentions to use the RCM recommendations.

On the other hand, non-significant factors identified relative to behavioral intention to use the RCM recommendations were PE, EE, SI, and HB. The identified factors contributing to the insignificance of these variables relative to BI can be attributed to the 1) the technology’s lack of features to address problems from other external factors (e.g. pest and disease, drought) aside from areas concerning nutrient and fertilizer management 2) overfamiliarity of the rice farmers in Pila, Laguna with respect to the use of the RCM recommendations, 3) independent decision making and voluntary use of RCM recommendations, and lastly 4) high cost of fertilizers and the presence of culturally integrated farm management practices for rice.

The study provided information on the respective behavioral and social profiles of the rice farmers in Pila, Laguna as users of the RCM recommendations. As such, various recommendations addressed to different stakeholders were drafted to further strengthen the operationalization of the technology. The first recommendation highlights the need to provide more inclusive and participative features within the RCM application. Through the development of a recognition and rewards system, Agriculture Technicians could capitalize on the joy and enthusiasm of rice farmers to further improve the process of generating RCM recommendations. Specifically, this system could increase the participation of rice farmers in providing relevant, accurate, up to date, and complete baseline information regarding their rice production which is needed to generate the RCM recommendations. The development of features showcasing the formulation of alternative fertilizers based on locally found materials could also be done to decrease the rice farmer’s difficulty and expanding their options in order to meet the recommendations of the RCM due to the presence of high-cost commercial fertilizers.

The second recommendation revolves around the study’s alignment with the results gained by Manalo et al. (2019). Specifically, the aforementioned study states that the engagement of the youth as infomediaries can strengthen the dissemination and utilization of the RCM recommendations. This is in view of the student’s higher hedonic motivation relative to the use of technology, in this case the RCM recommendations. By deeply engaging younger individuals [i.e. in this case the children of the rice farmers during the critical stages of the generation of the RCM recommendations (e.g., activities on the conduct of baseline interviews.)], increased uptake and use of RCM recommendations will be realized within rice farms in Pila, Laguna.

The third recommendation focuses in capitalizing on the rice farmers’ enthusiasm by revisiting and enhancing the format and style of the recommendations. This includes adjusting the font sizes and including more images to make the document more visually appealing and interactive for the rice farmers. With this, users may also further increase their appreciation on the information in the RCM recommendation generated, hence their increased behavioral intention to use the RCM recommendations.

The fourth and last recommendation focuses on integrating the RCM technology with other rice production technologies to increase perceived benefits by rice farmers relative to their rice production. Through an integrated and harmonized digital rice management platform, higher expectations of rice farmers relative to the benefits brought by the RCM technology will not be offset by other problems brought by various factors such as drought, flooding, pests and diseases to the rice production.

Document Type

Master Thesis

Degree

Master of Management major in Agribusiness Management and Entrepreneurship

College

College of Economics and Management (CEM)

Adviser/Committee Chair

Associate Professor Jeanette Angeline B. Madamba

Committee Member

Juan Paulino T. Junior, Mar B. Cruz, Dinah Pura T. Madamba, Melodee Marciana E. De Castro

Language

English

LC Subject

Rice trade

Location

UPLB College of Economics and Management (CEM)

Notes

Award: Best Field Study

Not available for general public. Accessible for consultation with the author and the thesis adviser and bound by confidentiality agreement.

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