ADOPTION OF AGROFORESTRY FARM MODELS IN BUKIDNON: ITS IMPLICATION TO ECOLOGICAL SERVICES Jupiter V. Casas1,*, Rico A. Marin2, Angela Grace Toledo-Bruno2, Laarni Lacandula 2, Rodriga G. Aguinsatan 2 ABSTRACT

Agroforestry is a land use management system that could address the challenges of sustainable development and climate change. Thus, there is a need to assess this system focusing on ecological conservation. This study combines qualitative and quantitative methods in assessing the ecological services of three agroforestry farm models in Bukidnon. These farms show differences in farmer adopters’ practices, area and biophysical conditions. However, data reveal that these agroforesty farms show evidences of their contribution to ecological services such as biodiversity, carbon sequestration, and soil erosion. The provisioning service of biodiversity is the priority for agroforestry adopters. Diversity of species planted are for food, timber, domestic and income uses. Agroforestry farms with high diversity have high carbon stock. Farms with higher number of years of existence also have favorable soil conditions for plant growth. Agroforestry farm with the highest number of years has a higher carbon stock for undergrowth and litter. Findings also reveal that soil erosion rates of the three agroforestry farms are within the tolerable limit. Results substantiate the need for LGUs and government institutions to provide support to agroforestry to sustain its contribution to biodiversity and natural resources conservation and sustain the ecological services of agroforestry in Bukidnon. Keywords: Agroforestry system, ecological services, adoption, biodiversity, carbon sequestration

1,*

Faculty and Dean College of Forestry & Environmental Science, Central Mindanao University, Musuan, Bukidnon, (Corresponding Author: [email protected]) 2 Faculty Members, College of Forestry & Environmental Science, Central Mindanao University, Musuan, Bukidnon

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INTRODUCTION Agroforestry is considered as the most suitable land-use technology in the uplands that are experiencing a problem of land degradation such as soil erosion, soil acidity, landslides, and the like due to the prevailing population pressure (Lasco & Visco, 2003). In social forestry project interventions, agroforestry is used as a tool for rural development and transformation. Moreover, it is seen as an innovative and environmentally sound response to declining availability of tree products (Cater, 1994). Agroforestry combines woody perennials and agricultural crops/ livestock simultaneously and sequentially in temporal and spatial arrangement for the twin purpose of ecological protection and socioeconomic improvement (Lundgren & Raintree, 1982). Agroforestry is claimed to have various functions such as biodiversity conservation and restoration, rehabilitation of degraded lands, creation of sustainable rural communities and recently, climate change mitigation (Tolentino, et al 2009). The combination of agricultural and woody components in the piece of land makes agroforestry a potential land-use technology that accounts for the three essential elements of sustainable land-use development, namely, economic, social, and environmental. SEARCA (1995) identifies the variables essential for sustainable land-use technology (e.g. agroforestry) such as food resource security, biological diversity, soil and water conservation, economic, social, and cultural aspects. Oades and Walters (1994) prescribed two major steps in determining whether a particular land-use/agroforestry system is sustainable or not. The first step is an evaluation of the current status of food, water, soil, social, and economic well-being of the community to generate base-line data or information. The second step is an assessment of the changes of these variables as a result of the land-use technology in place. These variables are grouped into economic viability, social, and environmental services of agroforestry farm models. Ecological services are commonly termed as ecological viability of a certain land-use system. MEA (2005) identifies the ecological services of biodiversity such as provisioning, regulating, supporting, and cultural services. Ormo (1996) explored the environmental services of the two agroforestry systems, namely, multi-cropping, and home garden using the following attributes: biodiversity, input and internal resource generation, soil surface protection, organic matter accumulation, multi-storey cropping system, and soil disturbance. The study found that monocrop plantation had low biodiversity, high productivity, and low sustainability because of its poor efficiency in resource utilization and poor stability. Dolom (2001) used the following attributes in attempting to understand the environmental 66

services of agroforestry component of the Community-based Forest Management (CBFM) program in Ilagan, Isabela such as reduced open degraded lands, improved water supply, increased species diversity, improved soil fertility, adoption of soil erosion measures, and enhancement of protection of the CBFM area. The study reveals that, generally, all of these attributes were improved due to the introduction of the agroforestry component. In Bukidnon, Garrity, et al (2002) found out that agroforestry practices like tree farming, conservation farming for annual cropping on slopes, and natural vegetative contour strips are effective in enhancing environmental services of the buffer zones of Mt. Kitanglad Range Natural Park in the Manupali watershed. In addition, Garrity et al. (2002) noted that the production of fruit and timber trees dramatically increased, re-establishing tree cover in the buffer zone, soil erosion and runoff declined, and the buffer strips increased maize yields by an average of 0.5 t/ha on hill-slope farms. Similarly, Guinto and Mugot (1990) found out that the Slope Agricultural Land Technology in the municipality of Maramag, Bukidnon had been effective in controlling soil erosion. Consequently, the technology had increased farm production, obtaining an Economic Rate of Return of 24.57%, which is higher than the consumption rate of interest of 10-15% accepted by the DENR. Recently, there is a growing interest to quantify the function of agroforestry in climate change mitigation, particularly in the amount of carbon sequestered due to adoption of agroforestry technology. This is another potential contribution of agroforestry to resolve challenges of climate change. Labata, et al (2012) explored the carbon sequestration potentials of three agroforestry systems in the municipalities of Maramag and Lantapan in Bukidnon include mixed multistorey system, tangaya agroforestry sytem, and falcate-coffee multistory system. Results indicate that these agroforestry systems had the capacity to store carbon in trees, herbaceous vegetation, litter, and soil. The largest amount of carbon was stored in the soil component. The mixed multi-storey system had the greatest carbon storing potential among the three types of agroforestry system because of its good soil condition which is conducive for plant growth. The appreciation of agroforestry as a land-use management system for enhancing ecological stability heightened the interest of agriculture and forestry schools (Lasco & Visco, 2003), private individuals, funding institutions, and various government agencies toestablish agroforestry demonstration farm models. Model farms showcase the viability of agroforestry technology geared towards addressing sustainable forest development and climate change. 67

As a landlocked and watershed province, Bukidnon is suitable for upland agroforestry land-use technology. In various upland community development projects, government agencies, non government organizations, and individuals use agroforestry as a land-use technology in addressing the degraded mountainous and steep land areas in Bukidnon. In 1980s, the Department of Environment and Natural Resources (DENR) of Bukidnon, through its Integrated Social Forestry Program (ISFP), first introduced agroforestry technology to address food security and ecological conservation by requiring upland farmer participants to adopt contour hedgerow farming and by raising a combination of agricultural crops and tree crops in the ISF project areas (Casas, 1996). In early 1990s, Community-Based Forest Management Program (CBFMP) was introduced in Bukidnon in which, agroforestry is one of the major land-use technologies. In 1995, the Japan Bank for International Cooperation (JBIC) allotted Php 152 million in Bukidnon to support the DENR’s CBFMP for the Pulangi Watershed Rehabilitation Sub-project (Casas, 2009). Around 30% of the financial support was for the agroforestry component in the comprehensive site development project of the participating People’s Organizations. Recently, agroforestry land-use technology is integrated in the Comprehensive Land-use Plans (CLUP) of the municipal local government units of Bukidnon for the development of the upland and forest land areas in the respective municipalities.

Private institutions and individuals have also initiated the adoption of agroforestry land-use technology in Bukidnon for production and ecological conservation. For instance, the International Research Centre for Agroforestry Research (ICRAF) conducted researches on contour hedgerow farming system and the management strategies that address the technical and institutional constraints of the system (Catacutan, Mercado, & Patindol, 2000). In mid-1990s, ICRAF adopted the Landcare approach in Lantapan, Bukidnon to address the said constraints. Landcare approach centers on the formation of community-Landcare groups through partnerships with government and nongovernmental agencies (Catacutan et al., 2000). Agroforestry practices, in particular, the contour hedgerow farming is the land-use technology used by ICRAF-Landcare project participants. Meanwhile, some individual upland farmers in Bukidnon have also taken the initiatives of venturing into production and ecological conservation of agroforestry farming using the knowledge generated from the trainings and seminars attended.

68

While a substantial amount of effort, time, financial support, incentives, and recognitions are extended to these agroforestry models, the need to determine the ecological services of these farm models in Bukidnon province is still wanting. This study examined the adoption of agroforestry and its contribution to ecological services of the selected agroforestry models in Bukidnon. Specifically, it evaluated the biophysical characteristics of the agroforestry farm models; examined variables that lead to adoption of agroforestry among farmers; compared the types of agroforestry systems/practices of the agroforestry farm models; and assessed the ecological services of these agroforestry models. The in-depth knowledge of the ecological impacts of these agroforestry models in Bukidnon provides important information which is indispensable in all present and future upland development endeavors that espouse agroforestry technology intervention. METHODOLOGY Location of the Study The three agroforestry (AF) models in the different municipalities of Bukidnon were selected based on the following criteria: recipient of at least a local recognition or award; AF is in its advanced stage; existing and functional; location and different management mechanisms employed for comparison. The AF models were evaluated from June 2011 to May 2012, which include the Rellita farm at Purok 13, Sur, Don Carlos; Capistrano farm at Himaya, Colambogon, Maramag; and Rosalita farm at Kilangi, Lurugan, Valencia City (Figure 1). Agroforestry Models Used in the Study Rosalita AF has the biggest farm area (Figure 2). In the Rosalita AF farm, only 3.80 ha. (25%) of the total area of is devoted to agroforestry while the remaining 11.57 ha. is for monocrop. On the other hand, Capistrano AF farm has 72.26% allotted for agroforestry farming.. For the Rellita AF farm, most of its meager area is devoted to agroforestry farming. As to the land tenure, which is one of the important factors in agroforestry adoption (Casas, 1996), the three farm owners have secured land tenurial instrument.

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Figure 1. Map of Bukidnon province showing the location of the three agroforestry farm models

70

Figure 2. Land-use maps of the three agroforestry farm models 71

Research Design This case study utilized descriptive and analytical methods in collecting, analyzing, and interpreting data to provide in-depth analysis (Table 1). Field measurements were done to assess the environmental services of the three AF models. Descriptive statistics complemented with the qualitative information for logical inferences to reinforce the analysis and interpretation of the numerical data. Furthermore, numerical standards from current literatures were used to serve as a yardstick in the analysis and comparison of the three AF models. Table 1. Methods Used in Data Gathering

Research Objectives

Data/Variables/Information

1. To evaluate the biophysical characteristics of the agroforestry farm models

a. Background information of the AF farm practitioner: location; name of practitioner; age; ethnicity; place of origin; educational attainment; household size; No. of household members involved in agroforestry; trainings attended; No. of years engage in AF; awards received; b. Description of the land area of the AF farm models:

Data Gathering Techniques Key informant interview

Key informant interview Mapping using GIS

Total farm size; area devoted to AF; area devoted to other land-uses; farm aspect; and tenurial instrument. 2. To examine variables that lead to adoption of agroforestry among farmers

Description of variables for agroforestry adoption: number of year s practiced; main reasons of adopting; sources of information of the AF farm model; individuals/agencies that introduced the AF farm model

72

Key informant interview

Table 1. (Continuation)

3. To compare the types of agroforestry systems/ practices of the agroforestry farm models

Description of the agroforestry/ practices employed in the AF farm model: number of year s engage in AF; types of AF practices; area devoted for each AF practices; species planted in the farm; bases/preference of planting; major crops planted; animals/livestock/ poultry integrated; bases of raising; mode of raising; support technologies employed in the AF farm; procedures used in implementing; major problems encountered in implementing the AF; action made on the problems encountered; major outputs derived from the farm; concerns/comments in the practice of AF.

Key informant interview; actual farm observation; mapping using GIS

4. To assess ecological services of the agroforestry farm models

Ecological attributes of the AF farm model: plant biodiver sity; soil physical and chemical properties, rate of soil erosion; and carbon sequestration potential

Field measurement of plant biodiversity; soil chemical and physical determination; rate of soil erosion; biomass estimation; measurement of Carbon (C) sequestered

Species diversity determination. Plants were categorized into trees/ perennial, intermediate, and undergrowth. Trees/perennial plants have above 5 cm diameter, with 1.0 meter height or higher; intermediate plants are plants with below 5 cm diameter and with 1.0 meter height or higher; and undergrowth plants include all plants less than 1.0 meter height, regardless of diameter. To determine species diversity, a 5m x 40m plot was established. Inside this plot, three 5m x 10m subplots were established at a distance of 5 meters between plots. All trees/perennial plant species found inside these subplots were identified, counted, and diameter at breast height (dbh) were measured. 73

The 3 x 3m subplots were established at the center of each 5m x 10m subplot. All intermediate plant species inside the subplots were recorded and counted. For the undergrowth species, 1m x 1m subplots were set up at the center of each 3m x 3m subplots. All undergrowth plant species and frequencies were determined. Soil chemical and physical properties. Soil samples were collected at strategic locations of every AF farm model. Soil samples were taken by digging a 30 cm depth V-shaped hole and slicing, with bolo, vertically the side of the hole. Soil samples were placed in properly labeled plastic bag and became a composite sample for each AF farm model. These composite samples were air dried, thoroughly mixed, and pulverized by hand. About 1 kg of the composite soil sample from each AF farm model was taken to the Soil and Plant Analysis Laboratory of the Department of Soil Science, CMU -College of Agriculture for laboratory analysis of the chemical and physical properties which include pH, organic matter (OM), phosphorus (P), potassium (K), bulk density, particle density, and soil texture. Plant carbon sequestration. Two 5x40 m (400 m2) quadrats were established per AF farm model. Trees having 5 cm dbh up to 30 cm dbh were measured within the 2.5 m of each side of the 40 m centerline. A quadrat of 20x100 m (2000 m2) was established for trees with dbh of 30 cm and up.

Measurement of carbon stocks of the undergrowth and litters used destructive method. Four subplots within the 5x40 m quadrat were established with a dimension of 1x1 m. All vegetation less than 5 cm dbh were harvested. Forest litters within the 1x1 m plots were also collected. Collected undergrowth and litters were weighed separately to get its total fresh weight. These were sun dried and oven dried to determine biomass. Soil carbon estimation. Composite soil samples were obtained within the 5x40 quadrat to determine soil organic content and bulk density. Soil erosion rate. Sheet erosion was measured using a modified erosion bar used by Ramirez (1988). The method used a modified 2.5-meter long aluminum bar with 10 holes spaced at 15 cm apart throughout the whole length of the bar.The bar was laid on predetermined points and rested on top of the wooden stake. In determining the soil loss, two measurements were obtained, i.e. before and after the rainy season of every year. The difference between the two measurements per year served as the amount of soil lost.

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Data Analysis Non-statistical method was used to analyze a number of information considering the exploratory, descriptive and qualitative nature of the study. A comparative analysis was used to generate information on the dynamics of the agroforestry, and to emulate the positive practices of the three AF models. Plant species diversity analysis. Given the actual field measurements, the following diversity values were computed: species importance value (IV) and Shannon Weiner Index of General Diversity (H) using the following formulas: Species Importance Value (IV) = Relative Frequency + Relative Density + Relative Dominance Where: 

Absolute Frequency (AF) = No. of plots in which species i occurs Total No. of plots



Relative Frequency (RF) =

AF of species i AF of all species



Density (D) =

No. of species i

Total area of the plots 

Relative Density (RD) = Density of species i Density of all species



Relative Dominance (Rdo) = Total Basal Area (BA) of species i Total Basal Area (BA) of all species

Shannon Weiner Index of General Diversity (H): n H

= -∑

ni/N log ni/N

i=1

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Where: H = Shannon index of general diversity ni = Importance value of species i N = Sum of importance value of all species The plant species diversity (H) was analyzed based on the following standard ratings (McCarty, 2008): Diversity Index (H)

Descriptive Rating

≥ 3.5 3.0 – 3.49 2.5 – 2.99 2.0 – 2.49 1 – 1.99

Very High Diversity High Diversity Moderate High Diversity Low Diversity Very Low Diversity

Analysis of soil chemical and physical properties. Soil chemical property was determined through routinary analysis (pH, OM, P and K). For physical properties, the analyses were done on bulk density, particle density, and soil texture. Plant carbon sequestration analysis. The analysis was based on the tree and undergrowth forest litter biomass obtained in the sample plots. Tree biomass. The tree biomass was calculated using the allometric equation (Brown, Gillespie, & Lugo, 1989): AGB = exp{-3.1141+0.9719*ln(DBH2*H)}; Recommended for broadleaf tree species (5-30 cm DBH) in areas with annual rainfall of 1500-4000 mm.

Where: AGB = Above Ground Biomass DBH = Diameter at Breast Height H = Total Tree Height

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In estimating the carbon stock per tree, tree biomass was multiplied with 0.45 (45%), which was the estimated carbon in tropical trees given the biomass. The below ground biomass of the trees were obtained using another allometric equation (Cairns et al., 1997): BGB = exp(-1.0587+0.8836*lnAGB); Recommended for tropical trees Where: BGB = Below Ground Biomass AGB = Above Ground Biomass Undergrowth and forest litter biomass. Collected undergrowth and litters were weighed separately to get its total fresh weight. These were sun dried and oven dried to determine its biomass using the formula below: ODWt = TFW – TFW*(SFW-SODW)) SFW Where: ODW = total oven dry weight TFW = total fresh weight SFW = sample fresh weight SODW = sample oven dry weight The calculated biomass was multiplied by 0.45 (45%), which was the estimated carbon content for tropical plants. Soil carbon analysis. The amount of soil organic carbon (SOC) per sample was computed using the formula: % SOC = % Organic Matter 1.724

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In order to express the amount of SOC from percent to tons C/ha, the following formula were used: Weight of soil/ha = 10,000m2 x 0.15m x bulk density Ton C in soil/ha = % SOC x wt of soil in kg/ha x 1 ton/1000kg Soil erosion rate analysis. The data of soil loss wer e converted into tons per hectare by determining first the volume using the formula: Vplot = (depth of soil washed) x (length of plot) x (width of plot) The values of soil particle density and bulk density per treatment plot were used to determine the percent solid space. These percent solid spaces were multiplied by the computed soil volume loss giving the value of solid space (m3). Using the conversion figures formulated by the Range and Management Division of the Ecosystems Research and Development Bureau (ERDB), the volume of solid particles were computed to its equivalent according to the particle size distribution: 1 cu. m of clay 1 cu. m of silt 1 cu. m of sand

= = =

483 kilograms 1,046 kilograms 1,497 kilograms

Total erosion in tons per hectare were determined by adding the computed soil loss (tons/ha) of the three particle size distribution (clay, silt, and sand).

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RESULTS AND DISCUSSION Characterization of the AF Farm Models Table 2 presents the different characteristics of each of the AF farm model. Forester Antonio Rellita, the adopter of the Rellita AF farm has attended a total of seven trainings related to various agroforestry activities such as soil and water conservation and various silvicultural practices. The Rellita AF farm model is the lowest in terms of area size. Most of its meager area is devoted to agroforestry farming except for the area for deep well and house. The farm is partly facing west and east that makes it suitable for sun-loving agroforestry plant species. On the other hand, Mr. Lazorada Capistrano, the developer of the Capistrano has attended four trainings/seminars on agroforestry. One of those was about contour farming or the Slope Agricultural Land Technology (SALT). He had been engaged in agroforestry when SALT project (funded by the Ford Foundation) was introduced in their community in coordination with the Integrated Social Forestry Project (ISFP) of the DENR-CENRO Pangantucan. The Capistrano AF farm was regarded as the best ISFP model recognized by the DENR and Ford Foundation. The farm area allotted 72.26% to agroforestry. Some land-uses include the house and pastureland. The farm, which is generally facing south, is suitable for raising shade-loving agroforestry plant. Mrs. Mercedes Rosalita is the owner of Rosalita AF farm who had participated six trainings/seminars on agroforestry practices. Her family had been engaging in agroforestry since they started occupying their farm. However, only 25% of the area of Rosalita AF farm is devoted to agroforestry. The remaining 11.57 ha is allotted for monocrop. Most of its area is devoted to sugarcane production, corn production, and gmelina plantation. Remaining areas are grasslands, coffee plantation, mango plantation, and the house and its surrounding. Rosalita farm is facing north east, which is suitable for raising shade-loving agroforestry plant species. All the three AF farm owners have secured land tenurial instrument.

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Table 2. Characteristics of the agroforestry farm models AF Farm Models Variables Location

Practitioner

Capistrano AF Farm Himaya, Colambogon, Maramag, Bukidnon

Rellita AF Farm P-13, Sur, Don Carlos Bukidnon

Rosalita AF Farm Kilangi, Lurugan, Valencia, City

Antonio Rellita

Lazorada Capistrano

Mercedes Rosalita

46

75

66

Bohol

Mizamis Oriental

Cebuano

BSF Graduate

Grade IV

High School Graduate

Household Size

4

8

7

No. of Household Member Involved in AF Farm

2

4

7

Rubber plantation establishment; nursery Production Care & Maintenance; Pesticide Handling; Goat Raising; Vermiculture Production; Soil & Water Conservation; Smallscale Tilapia Feed Formulation; Production of Quality Seedlings; Rainforestation

Rubber Production; Carabao Production Management; Corn Farming on Hilly Lands; Agroforestry/ Upland Farming Technique

Mindanao Agriculture Forum; Corn-husk Based Handicraft Making & Utilization; Leadership & Skills Training on Bonsai Culture & Floral Arrangement; Sustainable Freshwater Culture Production; Crop Production; Mushroom Production

Age Pace of Origin

Educational Attainment

Trainings Attended

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Table 2. (Continuation) AF Farm Models Variables

Capistrano AF Farm

Rellita AF Farm Awards Received

Rosalita AF Farm

First Place Establishment of Go, Grow, & Glow Foods, Household Category 2008; Pinaka-Tilapya Kaamulan 2012

Best ISFP participant agroforestry adopter.

Most Outstanding Farmer, Integrated Farming System; National Awardee, Outstanding Family Farmer

Farm Area (in hectares)

0.60

4.6 5

15.37

Area devoted to AF (in hectares)

0.53

3.36

3.80

partly facing west & east

generally facing south

facing north east

Tax Declaration

Certificate of Stewardship Contract (CSC)

Original Certificate of Title (OCT)

Farm Aspect Tenurial instrument

Agroforestry Systems and Practices Employed in the Three AF Farm Models The number of years of AF practice might have influenced the types of AF practices employed by the AF practitioners in the farm. Each AF farm model has developed a typical AF practice which is obviously dependent on the biophysical condition of the area, trainings attended and outside project intervention. The most visibly developed AF practices in Rosalita AF farm are fruit orchard, taungya, and livestock integration (goatery) which take a longer period to develop. The Rellita AF farm model has a good multi-storey integrated with fishpond. The adoption of the practice was due to the meager land area, the presence of the creek, and the readily available supply of water. The typical AF practice in the Capistrano AF farm model is the contour hedgerow farming because it has a relatively vast sloping area. The 25-year old multi-storey system consists of 2.62 hectares that is on its advanced stage evidenced by the visible plant strata. 81

The land area and the number of years engaged in AF did not hinder the extensiveness and intensity of adopting agroforestry systems as shown in the number of plant species planted in the AF farm models. Despite the very meager land area, the Rellita AF farm has 15 different species of fruit trees, 18 forest tree species, and 13 agricultural/horticultural crop species. Its major crop raised is fruit trees. Although Rosalita farm has bigger land area and has long been engaged in agroforestry, it has relatively fewer species. AF farm adopters also have different preferences of plant species planted as shown in Table 3. Table 3. Agroforestry systems/practices employed in the AF farm model Variables Rellita AF Farm 6 yrs

AF Farm Models Capistrano AF Farm 28 yrs

Rosalita AF Farm 43 yrs

No. of Years Engage in AF

Types of AF Practices

Type and No. of Species Planted

Preference of Planting Major Crops Planted Livestock / Poultry Integrated

Multi-storey/ homegardens (0.494 ha.) Fishpond besides multi-storey (0.036 ha)

Alley cropping (0.028 ha.) Fishpond (0.00085 ha.) Multi-storey (2.62 ha.) Rubber plantation with Livestock (0.51 ha.)

Ranch/fodder crops (0.756 ha.) Home garden (0.217 ha.) Orchard mango (0.50 ha.) Taungya (2.288 ha.) Goatery beside orchard ( 0.0367 ha.)

Fruit trees = 15 Forest trees = 18 Agricultural/ Horticultural crops = 13

Fruit trees = 19 Forest trees = 16 Agricultural/ Horticultural crops = 5

Fruit trees = 15 Forest trees = 2 Agricultural/ Horticultural crops = 2 and various ornamental plants

Rarity, availability of planting materials, variety of the species (e.g. fruit trees)

Rarity, easily propagated, exotic species

For food, beautification, with economic value

Rubber, coffee, bamboo

Sugarcane, coffee, corn

Goat, Carabao, Chicken

Goats, Cow

Fruit trees

Pig

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Table 3. (Continuation) AF Farm Models Variables Rellita AF Farm

Capistrano AF Farm

Rosalita AF Farm

Support Technologies Employed

Vermi composting, crop diversification

Contour farming, farm diversification, cover cropping during the fallow period, tree planting

Alley cropping, vermicomposting, planting of leguminous trees & shrubs along boundaries

Main Reasons for Adopting

To augment income, for food security & good nutrition (e.g. vegetable)

To preserve land for the children; conserve soil; avoid the use of organic fertilizer

To increase income; to have fruits to eat

Sources of Information of the AF Farm Model

Subjects taken in BSF; trainings attended;

From relatives; trainings attended

Trainings & workshops

Major Problems Encountered in Implementing the AF

Maintenance; thieves

Thieves; financing (not seriously)

Thieves; initial capital

Action Made on the Problems Encountered

Hiring laborer; provision of lighting in night time

Save amount from product sales for the expansion

Look for financial institutions; gradual implementation considering risks

Farmers Remarks on AF Practice

If financing is enough, better focus on the AF farm/ garden

Must be followed/ practice with long patience; long waiting period

AF can be a very good technology; very useful in the Integrated Pest Mgt.; trees minimize direct insect attacks to agricultural crops

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With a land area of more than five hectares, the Capistrano and Rosalita farms management introduced livestock as integral components of their AF system. Capistrano preferred to raise livestock with economic value. In addition, Capistrano AF farm raised native chicken for household consumption. Constrained with limited land area, Rellita AF farm uses pig pen for raising pigs. The three AF farm models showed adoption of crop diversification as one of the support AF technologies in their respective farm. It is important to note that the three AF farm models have no problem on pest and diseases which exemplify the advantage of mix species to counter said problem which is peculiar in agroforestry system. Based on experience, Mrs. Rosalita maintained the usefulness of AF in the integrated pest management because trees in AF minimize direct insect attacks to agricultural crops. Both Rellita and Rosalita AF farms advocated the use of organic fertilizer on vermicomposting while Capistrano AF farm adopted the contour farming or SALT technology. Although alley cropping was adopted by Rosalita AF farm, it was observed that it was not properly maintained. The incorporation of leguminous trees and shrubs was visible in Capistrano and Rosalita AF farms. The support AF technologies of the three AF farm practitioners were adopted the time they started their AF practices. The reasons for adoption of the AF technologies are to augment and increase income and food security in the household, and to have sustainable development. All these reasons coincide with the principles of agroforestry. All of the AF farmers augmented their knowledge in those AF technologies through trainings and seminars attended. Moreover, Capistrano claimed to have learned the technology from relatives. Being a Bachelor of Science in Forestry graduate, Mr. Rellita had sufficient knowledge on the AF technologies. The planting pattern in the AF farm models started with the onset of the rainy season. Harvesting would depend on the kind of crops grown. Short term crops were harvested between 2.5 to 12 months after planting. Harvesting of fruit trees would depend on the fruiting season (i.e. mango harvest is from July to December). Forest trees are harvested when needed and when they command a good market price. The three AF farms share common problem, i.e. thiebes. For financial problem, the AF practitioners use the AF farm income for expansion, gradual implementation, and linking with financial institutions. All three AF farm practitioners were optimistic and buoyant about the practice of agroforestry. While initial capital was a constraint, it was advised that financing should focus on one AF practice after the other.

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Ecological Services of the AF Farm Model The three AF farm models exhibited differences in terms of plant species diversity, soil properties, carbon sequestered, and soil erosion rate. Plant species diversity The Rellita AF farm model has lower tree diversity and moderately higher undergrowth diversity as shown in Table 4. The Capistrano AF farm model exhibited a moderately higher diversity for both trees and undergrowth presented in Table 5, compared to Rosalita AF farm model which had a low diversity for trees and had a very low diversity for undergrowth as seen in Table 6.

85

86

1 1

1 2 1

11.00 8.00 16.67 35.00 6.50 26.33 13.00 47.00 10.50 6.00

Talisay Guava Pitogo Durian Baongon Coconut Nangka Molave Rambutan Binunga

1

10.50 227.50 16.25

Total Mean

No. of species 14.00 Log of the no. 2.64 of species

1

10.00

15

1

8.00

Tambis Masanita s Chessnut

1

1

1

1

1

1

19.00

Gmelina

Freq

Mean DBH

Species

7.5

0.5

0.5

0.5

0.5

1

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

AF

1.000

0.067

0.067

0.067

0.067

0.133

0.067

0.067

0.067

0.067

0.067

0.067

0.067

0.067

0.067

RF

27

2

1

2

1

4

1

1

3

2

3

3

1

2

1

D/400sq .m

48.214

675.000

50.000

25.000

50.000

25.000

100.000

25.000

25.000

75.000

50.000

75.000

75.000

25.000

50.000

25.000

D/ha

1.000

0.074

0.037

0.074

0.037

0.148

0.037

0.037

0.111

0.074

0.111

0.111

0.037

0.074

0.037

RD

Table 4. Plant species diversity of Rellita agroforestry farm model

1.000 789.05

0.016

173.57

0.007

0.009

0.003

0.032

0.157

0.012

0.150

0.006

0.497

0.064

0.005

0.017

0.026

Rdo

11046.6 5

78.54

102.10

28.27

353.43

1734.95

132.73

1656.41

66.76

5489.16

706.86

50.27

190.07

283.53

Total BA

1.000

0.052

0.037

0.050

0.035

0.104

0.087

0.039

0.109

0.049

0.225

0.081

0.036

0.053

0.043

ni/N

-39.197

-2.954

-3.299

-2.996

-3.340

-2.259

-2.443

-3.255

-2.214

-3.017

-1.492

-2.518

-3.322

-2.944

-3.144

Ln ni/N

Diversity Index = Low Diversity

3.000

0.156

0.111

0.150

0.106

0.313

0.261

0.116

0.328

0.147

0.675

0.242

0.108

0.158

0.129

IV

2.456

0.154

0.122

0.150

0.118

0.236

0.212

0.126

0.242

0.148

0.336

0.203

0.120

0.155

0.136

H

87

0.167

1 2 1 1 1

Busikad Sweet potato Bulakbulak Gabi Mahogany

0.5 0.333

1 3 2 3 2 2 2 1 1 1 1

Carabao grass Agingay Skylove Makahiya Luy-a Motimoti Knot grass Spangletop Cogon Colapi Talking baka

0.167

0.167

0.167

0.167

0.333

0.333

0.333

0.5

0.167

1

Rambutan

0.167

0.167

0.167

0.167

0.333

0.167 0.167

1 1

Rambutan Tambis

Plants below 5cm diameter Freq AF (undergrowth)

Table 4. (Continuation)

0.031

0.031

0.031

0.031

0.063

0.063

0.063

0.094

0.063

0.094

0.031

0.031

0.031

0.031

0.031

0.063

0.031

0.031 0.031

RF

67.00

133.00

400.00

333.00

2333.00

2200.00

200.00

1467.00

2000.00

1267.00

1533.00

67.00

67.00

333.00

600.00

1467.00

8400.00

7.00 7.00

D/400sq.m

1675.00

3325.00

10000.00

8325.00

58325.00

55000.00

5000.00

36675.00

50000.00

31675.00

38325.00

1675.00

1675.00

8325.00

15000.00

36675.00

210000

175.00 175.00

D/ha

0.002

0.005

0.015

0.012

0.087

0.082

0.007

0.055

0.075

0.047

0.057

0.002

0.002

0.012

0.022

0.055

0.313

0.000 0.000

RD

Total Rdo BA

0.034

0.036

0.046

0.044

0.150

0.145

0.070

0.148

0.137

0.141

0.088

0.034

0.034

0.044

0.054

0.117

0.345

0.032 0.032

IV

0.023

0.025

0.031

0.030

0.101

0.098

0.047

0.100

0.093

0.095

0.060

0.023

0.023

0.030

0.036

0.079

0.233

0.021 0.021

ni/N

-3.780

-3.709

-3.466

-3.522

-2.291

-2.325

-3.051

-2.298

-2.378

-2.350

-2.816

-3.780

-3.780

-3.522

-3.316

-2.534

-1.456

-3.848 -3.848

Ln ni/N

0.086

0.091

0.108

0.104

0.232

0.227

0.144

0.231

0.221

0.224

0.169

0.086

0.086

0.104

0.120

0.201

0.340

0.082 0.082

H

88 2.639

Log of the no. of species

5.33

0.167

0.167

1.00

0.031

0.031

0.031

0.031

0.031

0.031

RF

6675.00

25000.00

1675.00

1675.00

18325.00

45000.00

D/ha

26801.00 ########

267.00

1000.00

67.00

67.00

733.00

1800.00

D/400sq.m

1.000

0.010

0.037

0.002

0.002

0.027

0.067

RD

1.48

0.041

0.069

0.034

0.034

0.059

0.098

IV

1.00

0.028

0.046

0.023

0.023

0.040

0.067

ni/N

-70.52

-3.580

-3.071

-3.780

-3.780

-3.228

-2.709

Ln ni/N

Diversity Index = Moderately High Diversity

Total Rdo BA

2.74

0.100

0.142

0.086

0.086

0.128

0.180

H

Low Diversity Moderately High Diversity High Diversity Very High Diversity

2.50-2.99 3.00-3.49 > 3.50

1.00-1.99 2.00-2.49

Descriptive Very Low Diversity

Numerical

Diversity Index Rating

Notes: AF = Absolute Fr equency; RF = Relative Fr equency; RD= Relative Density; BA = Basal Area; Rdo = Relative dominance; IV = Importance value; ni/N = Proportion of Importance Value; Ln ni/N = Log of the Proportion of Importance Value; H= Diversity Index

14

1

Wild ampalaya

No. of species

1

Bemuda

34

1

Total

0.167

1

Manimanihan Purple nutsedge 0.167

0.167

1

0.167

1

Chessnut

Freq AF

White kylingia

Plants below 5cm diameter (undergrowth)

Table 4. (Continuation)

89 2.890

Log of the no. of species

16.000

Palm

54.000 41.000 32.000 323.0 17.9 18.0

6.000

Giant seneguelas Mango Coconut Total Mean No. of species

1

9.000

Cacao Baongon

1 1 1 18

1

1

1 1 1 1 1 1 1 1 1 1 1 1

12 15.5 7 8 24 22.5 24 20 10 8 9 5

Gmelina Anchoan Dilaw Kapi Bagras African tulip Nangka Balsa Teak Salong Marang Malakapi Tisa

Freq

Mean DBH

Species

0.5 0.5 0.5 9

0.5

0.5

0.5

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

AF

0.056 0.056 0.056 1.000

0.056

0.056

0.056

0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056

RF

1 1 1 40

1

1

8

2 8 1 1 1 8 1 1 1 1 1 1

D/400 sq.m

25.00 25.00 25.00 1000.00 55.56

25.00

25.00

200.00

50.00 200.00 25.00 25.00 25.00 200.00 25.00 25.00 25.00 25.00 25.00 25.00

D/ha

201.06

28.27

532.50

265.47 1705.89 38.48 50.27 452.39 3449.48 452.39 314.16 78.54 50.27 63.62 19.64

Total BA

0.189 0.109 0.066 1.000

0.017

0.002

0.044

0.022 0.141 0.003 0.004 0.037 0.285 0.037 0.026 0.006 0.004 0.005 0.002

Rdo

0.270 0.190 0.147 3.000

0.097

0.083

0.300

0.127 0.396 0.084 0.085 0.118 0.540 0.118 0.106 0.087 0.085 0.086 0.082

IV

-3.430

-3.589

-2.304

-3.159 -2.024 -3.579 -3.567 -3.237 -1.714 -3.237 -3.338 -3.540 -3.567 -3.554 -3.598

0.090 -2.410 0.063 -2.762 0.049 -3.016 1.000 -55.625

0.032

0.028

0.100

0.042 0.132 0.028 0.028 0.039 0.180 0.039 0.035 0.029 0.028 0.029 0.027

ni/N Ln ni/N

0.217 0.174 0.148 2.666

0.111

0.099

0.230

0.134 0.267 0.100 0.101 0.127 0.309 0.127 0.118 0.103 0.101 0.102 0.099

H

Diversity Index = Moderately High Diversity

0.025 2290.23 0.025 1320.26 0.025 804.25 1.000 12117.15 673.18

0.025

0.025

0.200

0.050 0.200 0.025 0.025 0.025 0.200 0.025 0.025 0.025 0.025 0.025 0.025

RD

Table 5. Plant species diversity of Capistrano agroforestry farm model

90

0.17 0.17

1 1 2 3 1 1 3 2 5 1 3 1

Paguringon Gubas Gmelina Ipil-ipil Mango Marang Ferns Flamengia Coffee Moti-moti Rattan Nangka

0.17

0.5

0.17

0.83

0.33

0.5

0.17

0.17

0.5

0.33

0.33

2

Anchoan dilaw

0.33

2

Freq AF

Paloverde

Plants below 5cm diameter (undergrowth)

Table 5. (Continuation)

0.029

0.088

0.029

0.147

0.059

0.088

0.029

0.029

0.088

0.059

0.029

0.029

0.059

0.059

RF

2200.00

200.00

1467.00

2000.00

1267.00

1533.00

67.00

67.00

333.00

600.00

1467.00

8400.00

7.00

7.00

D/400sq.m

0.000

0.000

RD

55000.00

5000.00

36675.00

50000.00

31675.00

38325.00

1675.00

1675.00

8325.00

15000.00

36675.00

0.096

0.009

0.064

0.088

0.056

0.067

0.003

0.003

0.015

0.026

0.064

210000.00 0.368

175.00

175.00

D/ha

Total Rdo BA

0.126

0.097

0.094

0.235

0.114

0.155

0.032

0.032

0.103

0.085

0.094

0.398

0.059

0.059

IV

0.063

0.049

0.047

0.117

0.057

0.078

0.016

0.016

0.051

0.043

0.047

0.199

0.030

0.030

ni/N

-2.766

-3.026

-3.061

-2.142

-2.862

-2.555

-4.124

-4.124

-2.968

-3.157

-3.061

-1.615

-3.521

-3.521

Ln ni/N

0.174

0.147

0.143

0.251

0.164

0.199

0.067

0.067

0.153

0.134

0.143

0.321

0.104

0.104

H

91

2.639

Log of the no. of species

5.67

0.17

0.17

0.17

0.5

1.00

0.029

0.029

0.029

0.088

RF

22814.00

133.00

400.00

333.00

2333.00

D/400sq.m

570350.00

3325.00

10000.00

8325.00

58325.00

D/ha

1.000

0.006

0.018

0.015

0.102

RD

2.00

0.035

0.047

0.044

0.190

IV

1.00

0.018

0.023

0.022

0.095

ni/N

-56.46

-4.039

-3.752

-3.817

-2.351

Ln ni/N

Diversity Index = Moderately High Diversity

Total Rdo BA

2.64

0.071

0.088

0.084

0.224

H

Descriptive Very Low Diversity Low Diversity Moderately High Diversity High Diversity Very High Diversity

Numerical 1.00-1.99 2.00-2.49 2.50-2.99 3.00-3.49 > 3.50

Diversity Index Rating

Notes: AF = Absolute Fr equency; RF = Relative Fr equency; RD= Relative Density; BA = Basal Area; Rdo = Relative dominance; IV = Importance value; ni/N = Proportion of Importance Value; Ln ni/N = Log of the Proportion of Importance Value; H= Diversity Index

14

1

Cacao No. of species

1

Gabi 34

1

Manok-manok

Total

3

Freq AF

Plants below 5cm diameter (undergrowth) Malakapi

Table 5. (Continuation)

92

Mean DBH 21.74 25.00 6.00 19.67 10.00 14.00 38.00 8.00 16.00 37.00 12.00 15.50 7.00 229.91 10.48 13.00

2.56

Species Gmelina Marang Guava Lansones Papaya Suha Coconut Nangka Guyabano Mango Sibukaw Tambis Calamansi Total SD No. of species

Log of the no. of species

13

1

1

1

1

1

1

1

1

1

1

1

1

1

Freq

6.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

AF

1.000

0.077

0.077

0.077

0.077

0.077

0.077

0.077

0.077

0.077

0.077

0.077

0.077

0.077

RF

26

1

2

2

1

1

1

1

1

1

3

2

1

9

D/400 sq.m

54.962

650.000

25.000

50.000

50.000

25.000

25.000

25.000

25.000

25.000

25.000

75.000

50.000

25.000

225.000

D/ha

1.000

0.038

0.077

0.077

0.038

0.038

0.038

0.038

0.038

0.038

0.115

0.077

0.038

0.346

RD

1093.16

9058.25

38.48

490.88

226.20

1075.21

201.06

50.27

1134.12

153.94

78.54

984.11

56.55

490.88

4078.02

Total BA

Table 6. Plant species diversity of Rosalita agroforestry farm model

1.000

0.004

0.054

0.025

0.119

0.022

0.006

0.125

0.017

0.009

0.109

0.006

0.054

0.450

Rdo

1.000

0.040

0.069

0.060

0.078

0.046

0.040

0.080

0.044

0.041

0.100

0.053

0.057

0.291

ni/N

-35.721

-3.222

-2.669

-2.820

-2.551

-3.082

-3.211

-2.523

-3.121

-3.186

-2.299

-2.931

-2.873

-1.234

Ln ni/N

Diversity Index = Low Diversity

3.000

0.120

0.208

0.179

0.234

0.138

0.121

0.241

0.132

0.124

0.301

0.160

0.170

0.873

IV

2.332

0.128

0.185

0.168

0.199

0.141

0.129

0.202

0.138

0.132

0.231

0.156

0.162

0.359

H

93

3 1 3 3

Hagonoy Dilang baka Paragras Kudzo

2.639

Log of the no. of species

0.167 0.167 0.333 0.333 0.17 4.00

0.333

0.5 0.167 0.5 0.5

0.042 0.042 0.083 0.083 0.042 1.00

0.083

0.125 0.042 0.125 0.125

0.042

0.042 0.083 0.042

RF

67.00 333.00 533.00 200.00 67.00 7607.00

933.00

400.00 400.00 3267.00 1133.00

67.00

7.00 133.00 67.00

D/400sq.m

1675.00 8325.00 13325.00 5000.00 1675.00 190175.00

23325.00

10000.00 10000.00 81675.00 28325.00

1675.00

175.00 3325.00 1675.00

D/ha

0.009 0.044 0.070 0.026 0.009 1.000

0.123

0.053 0.053 0.429 0.149

0.009

0.001 0.017 0.009

RD

Total BA

0.050 0.085 0.153 0.110 0.050 1.56

0.206

0.178 0.094 0.554 0.274

0.050

0.043 0.101 0.050

IV

0.032 0.055 0.098 0.070 0.032 1.00

0.132

0.114 0.060 0.356 0.176

0.032

0.027 0.065 0.032

ni/N

-3.430 -2.904 -2.318 -2.654 -3.430 -37.71

-2.024

-2.172 -2.806 -1.033 -1.739

-3.430

-3.600 -2.738 -3.430

Ln ni/N

Diversity Index = Very Low Diversity

Rdo

0.111 0.159 0.228 0.187 0.111 1.87

0.267

0.247 0.170 0.368 0.306

0.111

0.098 0.177 0.111

H

Notes: AF = Absolute Fr equency; RF = Relative Fr equency; RD= Relative Density; BA = Basal Area; Rdo = Relative dominance; IV = Importance value; ni/N = Proportion of Importance Value; Ln ni/N = Log of the Proportion of Importance Value; H= Diversity Index

1 1 2 2 1 24 14

Muti-muti Agingay Skylove Makahiya Kawtang Total No. of species

2

1

Fortune plant

Carabao grass

0.167 0.333 0.167

1 2 1 0.167

AF

Freq

Plants below 5cm diameter (undergrowth) Gmelina Pomelo Tantaridas

Table 6. (Continuation)

Diversity Index Rating Numerical

Descriptive

1.00-1.99

Very Low Diversity

2.00-2.49

Low Diversity

2.50-2.99

Moderately High Diversity

3.00-3.49

High Diversity

> 3.50

Very High Diversity

Plant diversity is attributed to the number of years of AF practice. The farm size were not the determining factors on the level of plant species diversity as exemplified by the species diversity between Rosalita and Rellita AF farm models and between Rosalita and Capistrano AF farm models. Although Mrs. Rosalita is engaged in AF for 43 years, she also ventured on monocropping, which decreases the diversity. Of the ecological services of biodiversity, provisioning is the priority of agroforestry farm adopters who preferred species that could give them income to provide their primary needs like food. Soil properties

Soil pH of Capistrano AF farm model was slightly acidic while the Rellita AF and Rosalita AF farm models were classified as strongly acidic (see Table 7). The Capistrano AF farm model had the highest OM content while the Rellita AF farm model had the least. In terms of extractable P, Rellita AF farm model was the highest while the Capistrano AF farm model was the least. However, the exchangeable K of the Capistrano AF farm model is the highest while the exchangeable K of the Rellita AF farm model is the lowest. In terms of soil physical properties, Capistrano AF farm model had the highest bulk and particle density while the Rosalita AF farm model had the least among the three farms. The soil textural classification of the three farms was clay based on the soil textural analysis. Among the three farms, the Capistrano AF farm model had better soil properties. This could be due to its number of years operating as an agroforestry system. Through the years of employing agroforestry practices, the soil condition improves for the favorable growth and development of the plants. Better soil properties also coincide with a moderately high biodiversity index of the Capistrano agroforestry farm. This data supports the regulating and supporting services of biodiversity. 94

Table 7. Soil properties of the three agroforestry farm models Soil Properties

Name of Agroforestry Farm Models Rellita

Capistrano

Rosalita

Chemical pH

4.75

6.08

4.7

OM (%)

2.46

4.55

3.09

Extr. P (ppm)

16.89

1.85

4.9

Exch. K (ppm)

122

256

133

Bulk Density (g) Particle Density (g)

1.36 2.44

1.62 2.54

1.19 2.26

Textural Composition

Clay

Clay

Clay

Physical

Carbon stock of the three AF farm models Table 8 shows that the Capistrano AF farm model had the highest carbon stock of trees sequestered for both above and below ground. On the other hand, Rellita AF farm model has the least in both above and below ground with 65.70 and 6.6 ton per hectare, respectively. Capistrano AF farm existed for 28 years with a diversity of forest trees, as compared to the very low diversity of Rosalita AF farm that existed for 43 years. On the other hand, although Rellita AF farm was only six years in existence, yet its diversity is higher compared to Rosalita AF farm. The findings of this study conformed to the study of Mutuo et al. (2005) wherein agroforestry management practices increase the above ground carbon stocks and reduce soil degradation, as well as to mitigate greenhouse gas emissions. They added that the above ground carbon stocks in the humid tropics of potential agroforestry (tree-based) systems may sequester C in vegetation of over 70 ton ha-1. Thus, if these three agroforestry farms would sustain, they could capture more carbon, hence, strongly support climate change mitigation. 95

Table 8. Above and below ground carbon stocks of forest trees in the three agroforestry model farms (Ton Ha-1) Rellita AF Farm Trees Above Ground C (ton/ ha.) Below Ground C (ton/ ha.)

Plot A Plot B

Capistrano AF Farm

Mean

Plot A

Plot B

64.98

144.27 104.62

7.16

15.60

51.71

65.70

58.71

5.85

7.35

6.6

Mean

11.38

Rosalita AF Farm Plot A Plot B Mean

92.83

58.74 75.78

10.19

6.48

8.33

As shown in Table 9, Rosalita AF farm model has the highest carbon stocks of both undergrowth and litters. The Rellita AF farm model had a slightly higher undergrowth carbon stock than the Capistrano AF farm model. In terms of carbon stocks of the forest litters, Capistrano AF farm model is only second to Rosalita AF farm model. Rellita AF farm model, on the other hand, has the lowest value. These findings show a comparably lower carbon density value than the one presented by Lasco et al. (2004) in Mt. Makiling, with carbon density of 3.58 ton ha -1. The carbon stocks of the litters in Mt. Makiling were higher than the three AF farms in this study which have 1.94 ton ha -1. The difference was due to the diverse floral status of Mt. Makiling. However, expanding agroforestry farms may also increase the amount of carbon stocks of undergrowth and forest litter. Table 9. Carbon stocks of the undergrowth and forest litters of the three agroforestry model farms (Ton Ha -1). Category

Rellita AF Farm Plot A Plot B Mean

Capistrano AF Farm Plot A

Plot B

Mean

Undergrowth

0.47

0.43 0.45

0.41

0.39

0.4

Litters

0.87

0.30 0.58

1.33

0.44

0.88

96

Rosalita AF Farm Plot A Plot B Mean 1.01

0.80

0.90

1.61

1.04

1.32

Table 10 shows that the Capistrano AF farm model has the highest mean value of soil carbon. This can be attributed to its high soil OM content. Soil carbon is dependent on the amount of OM in the soil. Capistrano AF farm has also moderately high biodiversity index. The mean soil carbon stocks of the three AF farms under study are lower than those presented by Mutuo et al., (2005) where an agroforestry system in humid areas was found to had soil carbon stocks of up to 25 ton ha-1 in the top 20 cm of soil. The current study, however, was just considering 15 cm depth of the soil. Table 10. Soil Carbon of the three AF model farms (ton ha -1) Category

Rellita AF Farm Sta.1 Sta.2 Sta.3

Soil Carbon

6.27 4.87 6.23

Mean

5.79

Capistrano AF Farm Sta.1

Sta.2

Sta.3 Mean

10.54 12.70 14.95 12.06

Rosalita AF Farm Sta.1 Sta.2

Sta.3 Mean

6.76 6.98 5.57 6.44

Capistrano AF farm model has the highest total carbon stocks sequestered (Table 11). This is attributed to its very high carbon stocks in trees (Figure 3). Many trees found in Capistrano AF farm were already matured and huge. The bigger the vegetation, the greater is its biomass, and consequently, will have higher carbon stocks. According to Montagnini and Nair (2004), the extent of C sequestered will depend on the amounts of C in standing biomass. The carbon stocks sequestered in the three agroforestry farms were just within the range presented by Albrecht and Kandji (2003), that the C sequestration potential of agroforestry systems is estimated between 12 and 228 ton ha-1 with a median value of 95 ton ha-1. However, the total carbon stock of the agroforestry sites were higher, based on the report revealed by Montagnini and Nair (2004) that the average carbon storage of agroforestry practices in the humid region was 50 ton ha -1.

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Table 11. Total carbon stocks of the three AF model farms (Ton Ha -1) Agroforestry Model Farm

Tree (AG C ton/ha.)

Tree (BG C ton/ha)

Undergrowth (ton/ha)

Litters (ton/ha)

Soil Carbon (Ton/Ha)

Total carbon (Ton/Ha)

Rellita

58.71

6.6

0.45

0.58

5.79

72.13

Capistrano

104.62

11.38

0.4

0.87

12.70

137.8

Rosalita

75.78

8.33

0.92

2.32

6.44

93.79

Figure 3. Mean Carbon stocks of the three AF farms.

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Soil erosion rates of three AF farm models Table 12 shows that the Capistrano AF farm model has the highest soil erosion occurrence while Rosalita AF farm model has the least (Figure 4). The higher erosion rate of Capistrano AF farm model can be due to its steeper slope than the other two AF farms. Areas with higher slope gradient will most likely experience greater soil loss than moderately sloping areas. The soil erosion occurrences in the three sites, however, were within the tolerable limit of 10 ton ha-1 per year (Young, 1989; Paningbatan, 1990). This finding showed that the AF systems adopted in the three sites were effective in halting soil erosion. A study conducted by Tacio (1993) in the slope of Southern Mindanao employed with agroforestry land use seems parallel to this investigation which showed erosion of only 3.4 ton ha -1 per year. Table 12. Soil Erosion of the three agroforestry model farms (Tons Ha -1) Category

Rellita AF Farm Sta.1

Soil erosion

Sta.2 Sta.3

Mean

1.19 6.61 0.37 2.72

Capistrano AF Farm Sta.1

Sta.2 Sta.3 Mean

9.21 13.52 -7.54 5.03

Rosalita AF Farm Sta.1

Sta.2 Sta.3 Mean

4.16 1.13 0.68

Negative sign (-) indicates soil accumulation.

Figure 4. Mean soil erosion of the three AF farms 99

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CONCLUSIONS AND RECOMMENDATIONS There are factors that resulted to the adoption of the agroforestry. The AF practitioners had access to trainings ranging from actual operations and maintenance, conservation, and livelihood that substantially influenced farmers’ AF practices. Thus, AF practitioners adopted agroforestry practices regardless of educational attainment, age, and farm size,. The type of agroforestry systems and practices of the AF farm models are influenced by the farmers training and practices, farm size, biophysical condition, and location of the farm. Integration of livestock and poultry is dependent on the economic preference of the farmers and the available land area. AF practitioner with a bigger land area would most probably engage in livestock integration. Agroforestry practices of the three farms have significant role in enhancing biodiversity and carbon sequestration, due to the carbon storage potential of the multiple plant species grown in the area, and the improvement of soil condition. Plant species diversity is high, regardless of the farm size, educational attainment, and number of years in existence of AF, as evidenced in the AF farms of Mr. Capistrano and Mr. Rellita. High biodiversity index is also related to high carbon sequestration, carbon stock, and good soil condition. This also showed the contribution of agroforestry in sustaining ecological services. Agroforestry systems in the three farms employed effective soil conservation measures. The extent of soil erosions of the three farms was within the tolerable limit based on the accepted international standard. Cognizant of these findings, the following recommendations are herein outlined: The three AF models are typical land-uses, which have the potential to help in addressing the concern for sustainable development. In their effort to be catalysts in environmental conservation, the barangay and municipal local government units (LGUs) in these three AF models may tap the AF practitioners in championing projects on environmental conservation. The Executive Officers of the LGU shall recognize the significant role of these AF models and thus, the corresponding funding for its widespread adoption, whenever appropriate, shall be one of its priorities.

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LGUs, academe and other funding institutions must help in providing financial support to the AF models so as to sustain AF contribution to biodiversity and natural resources conservation, thus sustaining and securing the ecological services of agroforestry in Bukidnon. LGUs and government agencies (e.g. DA, DENR, academe) must encourage AF practitioners to continue AF practices and use such technology as a strategy in the National Greening Program (NGP), particularly in forest preservation, biodiversity conservation, and carbon sequestration.

Agroforestry practitioners should be provided with technical assistance such as monitoring of the biophysical conditions. In line with this, academe should be tapped in monitoring and evaluation of AF practices in Bukidnon.

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