Application of Systems Analysis Methodology to Dingras District, Ilocos Norte, Philippines

Application of systems analysis methodology to Dingras district, Ilocos Norte, Philippines

E.O. Agustin1, R.P. Roetter2, C.G. Acosta1,*, I.B. Galdores1, R.P. Villacillo1, A.B. Alcoy1, M.P. Caluya1, D.S. Bucao1, C.M. Balisacan1, S.G. Aquino1, J.I. Rosario1, L.M. Tute1, C.B. Julian1, F.B. Asia, H. Van Keulen3, M.K. Van Ittersum3, A.G. Laborte4, and M. Van Den Berg5

1 Mariano Marcos State University, Batac 2906, Ilocos Norte, Philippines 2 Alterra, Soil Science Centre, Wageningen UR, P.O. Box 47, 6700 AA Wageningen, The Netherlands 3 Plant Production Systems, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands 4 International Rice Research Institute (IRRI), DAPO, Box 7777, Metro Manila, Philippines 5 Development Economics, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, The Netherlands

Abstract Concerns about the rapid increase of population stimulated the need to find efficient land use and management systems that improve the well being of people in the agricultural sector, while at the same time protect the environment. Farm household producers are faced with concerns on the suitability of new technologies while considering the profitability of agricultural production as affected by other factors such as land availability, high input costs, intensification and diversification of crops, household consumption, and capital and credit limitations. In this study, a farm household model is used to evaluate the adoption behaviour of four representative farmers in Dingras, Ilocos Norte Province, Philippines. Three alternative technologies wereconsidered: current farmers’ practice (CFP), integrated pest management (IPM), and combined IPM and site specific nutrient management (SSNM). In addition, changes in household behaviour due to the removal of water constraints and changes in prices of biocide and fertilizer were evaluated. The results show that both the adoption of combined IPM and SSNM and the removal of the irrigation system result

* Corresponding author: E-mail address: [email protected]

Paper presented at First Asia-Europe Workshop on Sustainable Resource Management and Policy Options for Rice Ecosystems (SUMAPOL 2005), 11-14 May 2005, Hangzhou, Zhejiang Province, P.R. China 1 in a significant increase in farmers’ income combined with a decrease in environmental impact. On the other hand, the effect of changes in input prices is limited. The paper argues that the presented methodology and results can help in the assessment of existing policies and in the formulation of policies for the improvement of the well being of the farmers and the sustainability of production.

Keywords: Farm household modelling; Cropping systems; Integrated nutrient management; IPM; Dingras; Philippines

1. Introduction

Rice-based ecosystems in Asia are challenged by population growth, urbanization and industrialization. Increased population results in an increased demand for food while increased industrialization (and hence income) increases demand for alternative land uses. In other words, recent developments have led to increased competition for scarce natural resources particularly land and water (Roetter et al., 2000), which calls for a basic redirection of land use concepts such as crop intensification and diversification (PPDO, 2002). The basic questions to be answered are how to meet the increasing demand for income and food of the fast growing population given decreasing/degrading natural resources, and how to conserve natural resources while at the same time increasing agricultural productivity and farmers’ income (Cramer et al., 1997). Land use planning under multiple conflicting development goals with increasing competition for scarce resources is complex and needs to address the limitations of farmers in terms of capital and credit and fast increasing prices of agricultural inputs, especially biocides and fertilizer. The present agricultural system of Ilocos Norte province in the Phillipines can be characterized as highly diversified and intensified. Farmers intensify production by applying greater amounts of inorganic fertilizers, irrigation and pesticides, especially to cash crops. Excessive use of fertilizers, particularly nitrogen, has been shown to pollute the groundwater resource due to NO3 leaching, whereas excessive pesticide use harms human health and biodiversity. While farmer income is the main concern of policy makers, safeguarding the environment is also a critical factor that planners need to look into, as expressed during a stakeholder meeting in 2003 (IRMLA Report, 2003). For assessing possible alternatives, planners need tools such as models and expert systems to help make issues transparent and identify feasible solutions. This paper highlights the farm household model (FHM) approach (Singh et al., 1986) that was

2 adopted by the project ‘Systems Research for Integrated Resource Management and Land Use Analysis’ (IRMLA) for Dingras District, Ilocos Norte. In this paper, a farm household model is used to determine the suitability of three production technologies, i.e. current farmers’ practice (CFP), integrated pest management (IPM), and combined IPM and site specific nutrient management (SSNM) at the farm level (MAO Dingras, 2002) and to analyse changes in household behaviour due to the removal of water constraints and changes in prices of biocide and fertilizer.

2. The case study area

The province of Ilocos Norte, Philippines is geographically located between 17°48’ and 18°29’ N latitude and 120°5’ and 120°58’ E longitude occupying the coastal plain in the northwestern corner of the island of Luzon. It has a total land area of 0.34 million ha, more than one-third of which (129,650 ha) can be classified as agricultural land. The climate is characterized by a distinct dry and wet season, from November to April and from May to October, respectively, with a mean annual rainfall of about 2000 mm. Ilocos Norte consists of one city and 22 municipalities among which is the municipality of Dingras. Dingras is composed of 32 barangays or villages with 6,921 households. The municipality of Dingras is mainly agricultural and located within the central lowlands of Ilocos Norte. Its total land area is 17,962 ha, of which 9,860 (55%) ha is classified as agricultural land while 1,670 (9%) ha is forest land (MPDO, 2001). Rice, the staple food, is grown on most agricultural land in the wet season, as most cropping patterns are rice-based. In areas that are surface-irrigated throughout the year, crop choice is very limited, and triple rice or double rice, possibly followed by an upland crop, are grown. In the upland area, rice is followed by upland crops such as garlic, tobacco, corn, sweet potato, sweet pepper, mungbean, eggplant, and bittergourd. The local government of Dingras aims to maintain the municipality as the rice granary in Ilocos Norte by stimulating the adoption of sustainable farm practices and improved productivity. Consequently, the adoption of IPM and combined IPM and SSNM is encouraged at the farm level. In 2001, a survey was held among 164 households from 15 villages in the municipality of Dingras .The farm households are quite diversified. Often, non- agricultural activities contribute greatly to household income. Most farmers cultivate rented land as well as their own land. Sharing of produce between landlord and farmer is 75:25. The number of economically active members per household was calculated based on the following: children below 10 years old are not accounted for; youth

3 between 10 and 15 years old and students are considered as half-time workers, and persons between 16 and 70 years old, who are not students as fill-time workers. The households were classified into four farm types using cluster analysis. The following variables were used to cluster the households: total hectares of farm land; proportion of farm with surface irrigation throughout the year; proportion of farm area with good drainage; total value of farm assets; and number of economically active members of the family (Bi and Pradel, 2003). Table 1 shows the characteristics of the four farm types. The table reflects the following; (1) farm type A has just under 1 ha land area, most of which is rainfed, well drained; (2) farm type B has slightly more than 1 ha land area, most of which is surface irrigated, poorly drained; (3) farm type C has the largest in terms of land area (1.63 ha), most of which is surface irrigated, poorly drained; and (4) farm type D is relatively the poorest of the farm types because it has only 0.83 ha, most of which is rented and surface irrigated throughout. Adoption of technology and variations on constraints were analysed for these four farm types.

3. The farm household model

A farm household model was developed for Dingras to optimize resource use for each of the four farm types. The model maximizes discretionary income, i.e. income after minimum requirements for the consumption of crop and animal products have been satisfied (Castaño, 2001), subject to the resource endowments of each farm type such as labour, water, land, capital and credit access. The model employs a linear programming approach using General Algebraic Modelling Systems (GAMS) to allocate resources in the best optimum way (Brooke et al., 1998). The decision patterns in the model include area allocation by land use types (LUT) and land rental for agricultural activities, buying and selling of crop and livestock products (cattle, pig and chicken), allocation of family labour to crop and animal production as well as farm and non-farm wage employment, and the use of family capital and credit. The constraints in the model include available land area, water, family labour, and family capital, subsistence requirements, and limits on wage employment and credit. Subsistence requirements can be met through own production or market purchase. Data were obtained from the survey, discussions with local experts and from BAS, 2001 (minimum consumption requirements). Labour and capital balances are computed per dekad (10 days). Available capital is a major constraint to farming. Family capital is computed as 15% of the production in the previous year plus remittances from relatives abroad. Credit can be obtained at a rate of 16% per annum. Material inputs and irrigation fees are assumed to be obtained at the start of the planting season. Family capital is

Table 1. Characteristics of representative farm households, Dingras, Ilocos Norte, Philippines. FARM TYPE PARAMETER FTA FTB FTC FTD Total farm area (ha) 0.92 1.07 1.63 0.83 Own farm (ha) 0.24 0.22 0.23 0.05 Share cropped (ha) 0.68 0.85 1.4 0.78 Surface irrigation - Availability (% of total area) Surface irrigation throughout the year 14.88 62.65 84.93 99.03 Surface irrigation wet season only, groundwater during the dry season 21.85 13.27 8.73 0.97 Rainfed-groundwater used as supplemental irrigation both in the wet and dry seasons 63.27 24.08 6.34 0 Well-drained area (% of total area) 93.76 17.56 97.12 97.56 Worker (no. of economically active members) 3.6 3.6 5.0 2.5 Farm assets (000 pesos) 53.71 63.84 99.19 62.90 Number of samples 39 28 39 40 % of total samples 27 19 27 27

replenished and loans are repaid at the end of the harvesting dekad. Production cost for contract tomato growing is given by the Northern Foods Corporation (NFC) and is therefore not accounted for in the capital balances of the model. The model includes 20 crops and 21 land use types which are grown in three cropping seasons. Land was classified into six categories: surface irrigation throughout the year, well drained; surface irrigation throughout the year, poorly drained; surface irrigation wet season only, well drained; surface irrigation wet season only, poorly drained; groundwater irrigated, well drained; and groundwater irrigated, poorly drained. Two production technologies were defined and evaluated against the current farmers’ practice (CFP): integrated pest management (IPM) and combined IPM and site-specific nutrient management (SSNM). Table 2 describes the characteristics of the technologies included in the model in terms of yield, nutrient and pest management and labour use (Laborte et al., 2005). Yield estimates given by the MAO Dingras are classified as low yield and high yield are set 15% higher. A technical coefficient generator, specifically TechnoGIN (Ponsioen et al., 2003), was used to generate the associated input data, which was included as production coefficients for the farm household model.

Table 2. Characteristics of the three different production technologies in Dingras, Ilocos Norte, Philippines. Production technologies Characteristics CFP IPM IPM + SSNM1 Target yields Low Low High Amount of fertilizer Current amount Current amount 15% higher recovery than CFP Recovery fraction of Current amount 15% higher 15% higher applied fertilizer nutrients recovery than CFP recovery than CFP Labour requirements for Current practice 10% higher in crop 5% higher in land crop management establishment than preparation than CFP; 20% higher in CFP; 100% in crop crop management management than than CFP; about CFP; 50% decrease current practice for in crop harvest harvest and land preparation Machine and fuel use Current practice Current practice Current practice Biocide use Current practice 70% reduction in 50% reduction in pesticide compared pesticide, fungicide, to CFP; 15% herbicide uses reduction in compared to CFP fungicide and 50% reduction in herbicide compared to CFP 1 CFP is current farmer’s practice; SSNM is site-specific nutrient management; IPM is integrated pest and diseases management

Exploration of farm possibilities where done using scenarios focusing on three different issues: (1) the introduction of new technologies; (2) water scarcity and surplus; and (3) changes in the prices of external inputs. The results of these scenarios and the base-run simulations are discussed in Section 4.

4. Results

4.1 Base-run scenario

The base run indicates that the optimum income was higher than the actual income by 75%. The gap may be attributed to the non-inclusion of the labour income of the households derived from non-farm and off-farm activities. As expected, FTC has the highest income, since it has the largest land holdings. Farm types A and B were relatively close in terms of income. Farm type D, which has the smallest land holding, got the lowest income. Farm types C and D, having the largest land holdings in surface irrigated throughout the year, were growing triple crops. Land holdings of FTA were all used up while small portion of land for FTB, FTC and FTD were not cultivated. Average biocide use and nitrogen use ranges from 210–400 kg/ha for all farm types.

Table 3. Base run results of the farm household model for Dingras. Farm type Income Disc. Incomea RiceProd N use BioIndex Flabour Used land (000 PhP) (000 PhP) (kg) (kg) (kg a.i.) (ha) H1 173,843 144,007 4,137 127 120 219 0.92 H2 162,758 147,519 8,845 453 249 299 1.05 H3 189,329 172,239 12,996 566 324 430 1.46 H4 102,748 95,210 8,047 411 213 213 0.77 a Dincome: discretionary income; RiceProd: rice production; N use: nitrogen use; FLabour: family labour.

4.2. Improved technologies

The simulation results of alternative technologies are shown in Table 4. These results are discussed in comparison with the base run which used farmers’ current practice. In IPM, highest income and discretionary income can be derived by FTC. This is due to the higher rice production, lower biocide use and more efficient use of family labour. With the combination of IPM and SSNM, similar trends are observed in the income and discretionary income of FTC. It is also noted that a reduction in rice production in FTD can be due to the shifting of land use types that require more fertilizer and biocide use. In addition, it can be deduced that the adoption of combined IPM and SSNM would significantly decrease fertilizer use specifically nitrogen and biocide use.

Table 4. Simulated impact of new technologies on the farm types’ income, discretionary income, production, land allocation, family labour and environmental factors. Farm Technology1 Income Disc. Rice N Biocide Family Land type Income Prod. Fertilizer use labour used (103 PhP) (103 PhP) (ton) (kg) (kg a.i.) (ha) FTA CFP 173.8 144 4.1 127 119 219 0.92 IPM 0% -1% 0% 0% -66% 5% 0% IPM + SSNM 8% 11% 17% 36% -8% 15% 0% FTB CFP 162.7 147.5 8.8 452 249 298 1.05 IPM 0% 0% 0% 0% -69% 7% 0% IPM + SSNM 15% 18% 17% -62% -5% 18% 2% FTC CFP 189.3 172.2 12.9 565 324 429 1.46 IPM 0% 0% 3% 2% -68% 7% 2% IPM + SSNM 16% 20% 31% -27% 2% 15% 11% FTD CFP 102.7 95.2 8 411 119 213 0.77 IPM 1% 1% 0% 0% -45% 5% -1% SSNM 15% 17% -8% 11% 43% 7% -19% 1 CFP is current farmer’s practice; SSNM is site-specific nutrient management; IPM is integrated pest and diseases management.

Consequently, adoption of this technology would increase farmer income while at the same time lower the use of pesticides and fertilizer.

4.3 Water scarcity and surplus

In the dry season, production is limited on land with only groundwater irrigation. When this water constraint is removed from the model (through drilling depper pumpholes), the results shown in Table 5 reveal that income and the discretionary income of FTA, FTB, FTC increases by 2 to 5%, while income for FTD did not. This is because 99% of the land holding of FTD is surface irrigated throughout the year and thus not dependent on groundwater irrigation. On the other hand, FTC, which has the largest land holdings, decreases rice production, nutrient and biocide use, labour and land allocation due to changes in land use types and a reduction in area cultivated.

All of the farm types have some area that is surface irrigated throughout the year. This restricts crop choice to triple rice or double rice possibly followed by an upland crop. It is interesting to explore farm behaviour given removal of the irrigation system.

Table 5. Effects of scenario setting when water, labour, water and labour and irrigation system are not limiting/constraints on the farm household model for Dingras. Farm type Income Dincome RiceProd N use BioIndex Flabour Used land No water constraint FTA 5% 5% 0% 6% 0% −3% 0% FTB 3% 2% 1% 1% 1% 1% 2% FTC 2% 2% −4% −2% −3% −2% −4% FTD 0% 0% 0% 0% 0% 0% 0% No irrigation system FTA 0% 1% 13% 28% 10% 7% 0% FTB 15% 6% −38% −52% −37% −11% 2% FTC 33% 23% −41% −45% −33% −10% 11% FTD 38% 24% −58% −60% −53% -8% 9% a Dincome: discretionary income; RiceProd: rice production; N use: nitrogen use; FLabour: family labour.

Table 4 clearly shows that income significantly increases for FTB, FTC, and FTD, which have high shares of area in the irrigation system. Rice production for these farm types decreases because land use becomes more diversified as the model farmers select high value crops. In the base run, all farm types were growing rice-rice-tobacco on their fully surface-irrigated land. Now the three most affected farm types decrease the share of rice in production. Nutrient and biocide uses decreased significantly as upland crops require fewer nutrients and biocides. Labour inputs were also reduced as decrease in rice production means less labour. Land areas of the different farm types were all used up.

4.4 Changes in input prices

Tables 6-8 show that the effects of price increases in fertilizers and biocides on farmers’ income and input use are limited. In a medium farm that is well drained (FTA), a 10% increase in fertilizer and biocide prices does not change the income, rice production, fertilizer and biocide use and farm area (Table 6). Likewise, no changes are observed when prices increase by 20% and 30% but for a 1% decrease in income for a 30% increase in either price (Tables 7 and 8). In a relatively large farm (FTC), a increased input prices would lead to relatively small decreases in income, rice production, and input use. A 30% increase in fertilizer price, causes an income and input decrease of 3% only, while the 30% price increase in biocide leads to a reduction

Table 6. Results of the farm household simulations for Dingras on variations of fertilizer and biocide prices using 10% increase. Income Rice N ferti- Bio- Land CFP IPM IPM lizer cides Area and SSNM (103 pesos) (ton) (kg) (kg a.i.) (ha) (ha) (ha) (ha) Medium farm-well drained Base run 173.8 4.1 127 119 0.92 0% 0% 0% 10% increase in fertilizer prices 0% 0% 0% 0% 0% 0% 0% 0% 10% increase in biocide prices 0% 0% 0% 0% 0% 0% 0% 0% Large farm Base run 189.3 12.9 565 324 1.46 −9% −8% 0% 10% increase in fertilizer prices −1% −5% −3% −3% 0% −2% −2% 0% 10% increase in biocide prices −1% −5% −3% −3% 0% −2% −2% 0% Small irrigated farm Base run 102.7 8.04 411 213 0.83 −7% −8% −25% 10% increase in fertilizer prices −2% −2% −2% −2% 0% −17% −10% −25% 10% increase in biocide prices −1% −1% −1% −1% 0% −8% −8% −24%

in income of 5%, N fertilizer use of 6%, biocide use of 5% and cultivated area of 3%. Changes are of similar magnitude for the small, irrigated farm (FTD): a 30% increase in the price of fertilizers or biocides leads to a reduction of income and input use of 3- 4% only.

5. Conclusions and recommendations

From the analyses made based on the results of the farm household model for Dingras, the following conclusions are drawn: (1) new technologies such as IPM and combined IPM and SSNM can be adopted by the farmers to improve their production income while decreasing pesticide and nutrient residues that are a risk to the environment; (2) the abolishment of the irrigation system of Dingras would open

10 promising land use options to farmers, namely a shift from irrigated rice to upland high value crops; (3) changes in fertilizer and biocide costs affect the production income and land utilization of the farmers only slightly.

Table 7. Results of the farm household simulations for Dingras on variations of fertilizer and biocide prices using 20% increase. Rice N ferti- Bio- Land CFP IPM IPM Income lizer cides Area and SSNM (103 pesos) (ton) (kg) (kg a.i.) (ha) (ha) (ha) (ha) Medium farm-well drained Base run 173.8 4.1 127 119 0.92 0% 0% 0% 20% increase in fertilizer prices 0% 0% 0% 0% 0% 0% 0% 0% 20% increase in biocide prices −1% −0% 0% 0% 0% 0% 0% 0% Large farm Base run 189.3 12.9 565 324 1.62 −9% −8% 0% 20% increase in fertilizer prices −2% −5% −3% −3% 0% −6% −5% 0% 20% increase in biocide prices −2% −2% −3% −3% 0% −6% −2% 0% Small irrigated farm Base run 102.7 8.04 411 213 0.83 −7% −8% −25% 20% increase in fertilizer prices −4% −3% −4% −4% 0% −11% −11% −29% 20% increase in biocide prices −3% −2% −3% −2% 0% −10% −3%8 −25%

Based on the results, we give the following recommendations: (1) the local government of Dingras should look closer into the land options possible for improving the agricultural activities of the farmers such as limiting irrigation to areas where more diversified cropping can be adopted. (2) there is a need to rationalize and re-focus the target of the policy makers with regards to improving rice production; (3) the municipal agricultural office can be a strong vehicle in information campaign on the proper adoption of new technologies by closer supervision of technicians; (4) the farm household model, after validation, could be utilized as benchmark in making policies related to agriculture.

Table 8. Results of the farm household simulations for Dingras on variations of fertilizer and biocide prices using 30% increase. Income Rice N ferti- Land CFP IPM IPM lizer Bio- area and cides SSNM (103 pesos) (ton) (kg) (kg a.i.) (ha) (ha) (ha) (ha) Medium farm-well drained Base run 173.8 4.1 127 119 0.92 0% 0% 0% 30% increase in fertilizer prices −1% 0% 0% 0% 0% 0% 0% 0% 30% increase in biocide prices −1% -0% 0% 0% 0% 0% 0% 0% Large farm Base run 189.3 12.9 565 324 1.62 −9% −8% 0% 30% increase in fertilizer prices −3% −5% −3% −3% 0% −14% −15% −3% 30% increase in biocide prices −5% −5% −6% −5% 0% −14% −2% 0% Small irrigated farm Base run 102.7 8.04 411 213 0.83 −7% −8% −25% 30% increase in fertilizer prices −4% −3% −4% −4% 0% −12% −12% −31% 30% increase in biocide prices −4% −3% −4% −4% 0% −10% −8% −25%

Acknowledgments We would like to express our deepest gratitude to Dr. Huib Hengsdijk for helping us calibrate the data for TechnoGIN; Dr. Robert P. Castro, Municipal Mayor of Dingras, for his support to this project; Mr. Cesar Derrada, Municipal Agricultural Officer, for his accommodation and help in validating the data used as inputs; farmers and agricultural technicians who attended the stakeholders meetings; and the farmers of Dingras who shared much information we need in completing the model. Thank you very much.

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