Value Chain and Technical Efficiency analysis of small-scale seaweed farms in Zamboanga Sibugay, Philippines

Marvin Louie G. Orbetaab*, Larry N. Digalab, Ivi Jaquelyn T. Astronomob, Carol Q. Balgosb, Paolo Enrico Codogb

a PULL Program, University of the Philippines Mindanao, Davao City, Philippines; bSchool of Management, University of the Philippines Mindanao, Davao City, Philippines; *mgorbeta@up.edu.ph

Value Chain and Technical Efficiency analysis of small-scale seaweed farms in Zamboanga Sibugay, Philippines

Coastal communities in Zamboanga Sibugay rely on seaweed farming as a major source of livelihood but it continues to be a daunting task with difficult issues surrounding production. Using value chain analysis, the researchers determined the position of seaweed farmers relative to the larger value chain of the industry. After the situational analysis, an assessment of farm production efficiency was done using Data Envelopment Analysis (DEA) and a Tobit Regression Model was specified to identify determinants in the efficiency of production. Baseline survey data was collected and used in this study. Major issues in production were identified such as subsistence farming, prevalence of pest and diseases, bad practices in seaweed farming, and adverse weather conditions. DEA results showed that 80% of the respondents had high technical efficiency scores with a mean technical efficiency score of 0.936 (n=70). Analysis on TE scores based on farm location and type of product showed that deep sea farms (TE = 1.0; n = 11) and farmers selling Raw Dried Seaweeds (RDS) (TE = 0.995; n = 28) were more efficient. Tobit regression identified farm location, gender, type of product, and bad weather conditions as significant variables. Increase in the number of farms in deep sea locations and improving the role of women in seaweed farms can have a positive impact on technical efficiency. Meanwhile, RDS production and adverse weather conditions had a negative impact on technical efficiency. Improving RDS production capability and introducing practices to mitigate the effects of bad weather as possible interventions.

Keywords: seaweeds; value chain analysis; technical efficiency; data envelopment analysis

Introduction

The seaweeds industry in the Philippines provides livelihood to thousands of Filipinos from farmers to workers in processing plants. Seaweeds are grown along the long coastlines of the archipelago and further out into the deep waters. Estimates on seaweed farming areas is at 60,000 hectares (Pedrosa, 2017). This is less than 10 percent of the suitable land area of 700,000 hectares for both coastline and deep-sea farmable areas throughout the country (Pedrosa, 2017). In 2018, production of wet seaweeds was recorded at 1.56 million metric tons (PSA, 2018). Coastal communities engage in seaweeds production because the Kappaphycus alvarezii and Eucheuma cottonii have a high demand for many industrial uses (Narvaez, 2015). Wet seaweeds are processed to produce export products such as refined carrageenan, semi-refined carrageenan, raw dried seaweeds (RDS), and seaweed chips (DTI, 2019). Total export volume of seaweed products totalled 33,239.33 MT and consisted of 87.5 percent carrageenan in 2016 (DTI, 2019).

Zamboanga Peninsula produced 204,180.45 metric tons of seaweeds which is third in the country behind ARMM and Region IVB (PSA, 2018). The region has an estimated 25,451 small-scale seaweed farmers (DTI, 2017). The study location is in the Zamboanga Sibugay Province (ZSP), one of the four provinces of the region, with 10,364 seaweed farmers (DTI, 2017). Industry estimates were 63 percent of the seaweed farmers in the province were living below the poverty threshold (DTI, 2017).

Ipil is one of the thirteen coastal municipalities in Zamboanga Sibugay which is also the capital of the province. Among those municipalities, Ipil ranks second in terms of land area and along with the volume of wet seaweeds and RDS they produce shows that they are an important contributor to the seaweeds industry (Province of Zamboanga Sibugay, 2016). The top two seaweeds-producing barangay in terms of land area in Ipil are Barangay Magdaup and Barangay Buluan with land area of 450 hectares and 355 hectares, respectively (Apao, 2018).

This study was part of the PCAARRD - UP Mindanao - LandCare - LIFE (PULL) Program of the University of the Philippines Mindanao using the Livelihood Improvement through Facilitated Extension (LIFE) Model to improve the livelihood of farmers in coastal communities through action research. Seaweed farming in coastal areas in the Zamboanga Peninsula is common as an effect of the rapid spread of this farming option starting in the late 1960’s (Valderrama, et al., 2015). Despite the commercial success of seaweed production in the country, the production side continues to be the main problem of the industry. Major issues such as low-quality seedlings, ice-ice disease, fish herbivores, and epiphytes continue to gravely affect the seaweeds farming in the country (Monzales, 2017; Kim, Yarish, Hwang, Park, & Kim, 2017). An evaluation of farming systems in six countries revealed that production cost per hectare in the Philippines is high due to the low productivity of their farms (Valderrama, et al., 2015). There is a need to identify factors that can make seaweed farms more robust and cost-efficient (Kim, Yarish, Hwang, Park, & Kim, 2017) even in small-scale settings. We calculated for the technical efficiency of seaweed farms in the target area to potentially improve farm productivity and lower average farm cost.

Value chain analysis is an effective framework to help identify issues that exist within the chain. A seaweeds jobs value chain analysis discovered issues in credit availability, low quality planting materials, and lack of post-harvest facilities, low quality raw dried seaweeds, high overhead costs, and poor roads as limiting factor in job generation (Narvaez, 2018). There have also been several reports that used the value chain framework to analyse the situation of seaweed industry in areas such as Bohol, Ilo-ilo, Guimaras, Cebu and the Zamboanga Peninsula (Narvaez, 2015; CARE, 2018; Nobleza, 2015). A micro-level analysis of the seaweeds value chain and technical efficiency was used prior to the interventions of the program.

Production efficiency is the ratio of inputs to output in which resources are modified in order to maximize inputs and minimize waste of said inputs (Banton, 2019; Mulholland, 2017). An understanding of the current production function of seaweed farmers is important to improve on production inefficiencies via applying the necessary changes informed by the analysis (Coelli, Rao, O'Donnell, & Battese, 2005). Data Envelopment Analysis is a non-parametric method used to analyse the technical efficiency of decision-making units (DMUs) subject to the firms input use and output (Angiz, Mustafa, & Kamali, 2013; Mohammadi, Mirdehghan, & Jahanshaloo, 2016). For this study, the researcher will utilize the Constant Returns to Scale (CRS) model due to the model’s objective function on finding the highest slack values among the included firms (Golany & Roll, 1989).  Several studies have used DEA to evaluate technical efficiency in various aquaculture applications including effects of climate change on pangasius farms, profile of economically efficient mussel farms, and looking at the inefficiencies of tank culture systems in Malaysia (Nguyen, et al., 2017; Theodoridis, Ragkos, & Koutouzidou, 2019; Iliyasu & Mohamed, 2015). DEA approach is useful in measuring technical efficiency as the approach can use multiple inputs or outputs (Pascoe, 2007). The DEA approach measures the best practice within a sample data set (Bhagavath, 2009). It is the appropriate approach because it is an investigation of how to improve the input use and output of seaweed farmers in Ipil.

A study calculated the level of technical efficiency of seaweed farms in Tarakan, North Borneo and discovered production factors that have an impact on seaweed production are farm area, seeds, labor, and location (Banyuriatiga, Darwanto, & Waluyati, 2017). The author did not come across a study on seaweeds that used Data Envelopment Analysis and Tobit regression to calculate for technical efficiency. We saw it as an opportunity to contribute to the body of knowledge on the subject.

The nonparametric nature of DEA has an issue with the lack of estimates and error terms. The data is also strictly reliant on the available data, thus the evaluation of the impacts of each technological input is not possible. However, being a regression, the Tobit Regression model can help in identifying the relationships and the degree of influence of each variables to the discovered technical efficiency (Yu, Barros, Yeh, Lu, & Tsai, 2012). Studies pairing DEA and Tobit regressions had used calculated efficiency scores, scores that are strictly between 0 and 1, and identified independent variables that can influence a firm’s level of efficiency (Yusuf & Malomo, 2007; Tung, 2016; Oluwatayo & Adedeji, 2019).

The objectives of this study are to (a) examine the relative position of the small-scale seaweeds farmers in the value chain, (b) determine the level of technical efficiency of seaweed farms, (c) identify the factors affecting the production efficiency of seaweed farms in Ipil, Zamboanga Sibugay.

The succeeding chapters of this paper are Materials and Methods, Results, Discussions, Conclusions, Implications and Future Research. Materials and Methods section discusses the data collection, variable selection, and methodologies used by the researchers. The Results and Discussion sections present the results of the Value Chain Analysis, Data Envelopment Analysis, and Tobit regression. The final chapters will discuss the conclusions drawn from the result, implications to the project, and avenues for future research on this topic.

Materials and Methods

Data collection

Data for this study were collected from April to September 2018 using a baseline survey questionnaire and oral interviews with key value chain actors. Input use and output of seaweed farms for one production cycle were used in the analysis. A total of 114 farmers respondents were surveyed, 64 from Sitio Katipunan, Brgy. Magdaup and 50 from Brgy. Buluan, both located in Ipil, Zamboanga Sibugay. These respondents were selected from the list of partner farmers as part of the PULL Program of UP Mindanao. The LandCare Foundation of the Philippines and the local government of Ipil assisted in the selection of the coastal community. Based on data from the Municipal Agriculture Office of Ipil, the two barangays have the largest area planted for seaweeds (Apao, 2018).

A pilot run for the baseline survey was done for validation purposes. Adjustments were made on the questionnaire following the activity. The 114 filled survey questionnaires were reduced to 70 because of incomplete information and the presence of outliers provided by the respondents. Omitting the responses with outliers is important because these can influence the distance between the production frontier and the level of technical efficiency of farms (Ngoc, et al., 2018).

The researchers used the DEAP version 2.1 program (Coelli T. , 1996) to calculate the technical efficiency. TE scores were used as the dependent variable for the Tobit regression which was calculated using the Stata 12 software program.

Value Chain Analysis

The value chain analysis in this study will be used as a descriptive tool to gain insights on the current situation of small-scale seaweed farmers in the identified areas. Relative to their situation we also attempt to understand the nature of the entire chain from their position by examining their entry point (as small-scale farmers), products sold, access to markets, and the chain dynamics (governance, control, relationships, and linkages) (Rosales, et al., 2017). The micro-level analysis informed the researchers of the issues affecting their value chain and was also used to properly investigate the inefficiencies of their farm.

Data Envelopment Analysis

The researchers used Data Envelopment Analysis (DEA) in determining the technical efficiency of seaweeds farms. The DEA theory states that it is a tool to help assess technical efficiency of a decision-making unit (DMU) with their ratio of input and output levels. The ratio of output to input is referred to as the efficiency ratio which calculates how much input can be modified in order to acquire a level of output. Equation 1 shows the DEA Constant Returns to Scale (CRS) Model, an assumption where changes in input will bring proportional changes to output (Coelli T. J., Rao, O'Donnell, & Battese, 2005; Pettinger, 2017)

Maximizeu,v  ;                 (1)

   Subject to:     ≤ 1   i = 1, 2, ..., n

    ≥ 0

Where:

                                                         = vector of output weights of ith-DMU

                                                      = vector of input weights of ith-DMU

                                         = level of output by ith-DMU

                                      = level of input by ith-DMU

Efficiency scores are subject to two constraints: (1) the calculated value is less than or equal to 1 and (2) it is greater than or equal to 0.  Equation 1 shows the optimal levels needed for either input or output for each farm.

The duality in linear programming is incorporated in Equation 2 which is the enveloped form of Equation 1, which shows whether the ratio is efficient or inefficient. Additionally, the variable Y represents the output matrix for each DMU while X is the input matrix where both consider the different input-output combinations used.

                                                                           Minimizeꭋ, ≓ꭋ,        (2)

        Subject to: -yi + Y≓ ≥ 0,

                                  ꭋxi - X≓ ≥ 0.

                        ≓ ≥ 0.

The scalar variable is represented in ꭋ and ≓ is a N’1 vector of constants. The former represents the calculated efficiency score of the ith DMU which satisfies the two constraints in first Equation. If the value is closer to 1, the DMU is said to be technically efficient while values closer to 0 means otherwise (Jain & Jha, 2015).

We measured the technical efficiency of the farmer respondents using the inputs and materials used, labor and the total land area of their farm. Input and output variables were based on the baseline survey questionnaires where large values with impact on the results were included in the calculation. It was done to avoid limiting the DEA model in real life applications similar to the study by Jiangping Yu & Weiyang Yu (2018), where the selection of these variables can increase the truthfulness and effectiveness of variables used (Yu & Yu, 2018). Equation 3 is the DEA approach model for the respondents:

Maximizeu,v  ;                                                                         (3)

        Subject to:     ≤ 1   i = 1, 2, ..., n

                          ≥ 0

Table 1 provides a description of the different variables used in the DEA computation. The variables were categorized into the output (1) and input (8) variables. Volume of wet seaweeds (V), expressed in kilograms, was used because majority of the seaweeds farmers sold wet seaweeds to their buyers (Narvaez, 2018). Area (A) is treated as an input variable which is expressed in hectares planted in one production cycle (Juanich, 1988). Seedling are expressed in kilograms (Ruiz, 2018) and was limited to one cropping cycle. Materials, such as, plastic straws (PS), anchor bars (AB), floaters (F), binders (B), and tools (T) used for one cropping cycle were also categorized as input variables in the DEA equation (Ruiz, 2018). Labor for planting (PL) was expressed in man-days for one cropping in the computation (Yusuf & Malomo, 2007).

Table 1. Variables used for Data Envelopment Analysis

Tobit Regression

In this two-stage DEA approach, TE calculation was paired with Tobit Regression to determine factors that are sources of (in)efficiency (Cottrell & Lucchetti, 2017). Tobit Regression has been paired with DEA in several studies, including aquaculture (Xiping & Yuesheng, 2019; Samah & Kamaruddin, 2016). TE scores range between 0 to 1 which qualifies them for a censored regression model estimation (Xiping & Yuesheng, 2019; Samah & Kamaruddin, 2016). Tobit Regression helped in identifying the determinants of efficiency for the seaweed farmers in Ipil. A general Tobit model is shown in Equation 4:

                (4)

Where:

 = latent variable

Coefficient of the independent variable

Independent Variable

Dependent Variable, usually the DEA efficiency score

 = assumed to be ε~N(0,)  or normally distributed

In this application, variables in the model were categorized as socioeconomic, farming and marketing practices. The calculations were done to find out the determinants for technical (in)efficiency in seaweed farms. Data were taken from small-scale seaweed farmers capturing evidence to develop farm management strategies in seaweed farms. The Tobit model for this study is expressed in Equation 5 below:

  (5)

Technical efficiency (TE) is the dependent variable.  G for gender, SC for sources of capital, PO for primary occupation, FT for farm type, SL for landholding status, NF for number of farms, M for total number of monolines, C for membership in a cooperative, AT for trainings attended, H for harvesting days, NC for number of croppings, PT for product type, V for variety cultivated, and I for current issues in the farm. Ɛii represents the error term.

Gender

Women have an active role in communities who rely on seaweed farming for income. Female villagers who own seaweed farms perform similar activities as men and are best suited for shallow farming (De San, 2012; ASTRULI, 2019). In addition, family-run farms have women and children work as their contribution to the farm with women in-charge mainly with replacement cuttings on the cultivation lines (Valderrama D. , Cai, Hishamunda, & Ridler, 2013; De San, 2012)

Sources of Capital

Relatively, starting a seaweed farm requires less capital compared to other aquaculture ventures (Valderrama D. , et al., 2015). However, any agribusiness venture should be able to secure enough capital to fund farm implements and labor costs which is why the authors included sources of capital as a possible factor. In this study, we asked respondents if they acquired capital from their own assets, borrowed from family members, other farmers, or lenders.

Primary Occupation

Respondents may have other sources of livelihood aside from seaweed farming. Participation in another occupation may increase proficiency in said occupation but cause a decrease in efficiency in other occupations which in this case is seaweed farming (Yusuf & Malomo, 2007).

Farm Location

Farm location refers to the distance of the farm from the shore. Under this category, farms can be classified as located near shore (0.5 km – 1 km; 0.5 m – 2.5 m depth) and deep sea (> 1 km; > 2.5 m depth) (Narvaez, 2015; Butardo, et al., 2003). It has a significant effect on volume of production, vulnerabilities from diseases and storms, costs, and capital requirements. Several reports have cited that cultivating seaweeds near shore makes them more susceptible to diseases but is more accessible and does require less capital and equipment to set-up (FAO, 2019; Narvaez, 2018). Deep sea farming has a more stable environment for seaweed cultivation avoiding many of the factors that cause diseases in seaweeds such strong light, high temperatures, and nutrient-poor environment (Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002). As a result, production is higher, however, it also demands more capital for inputs, labor, and other costs such as expensive transportation (FAO, 2019; Narvaez, 2018; Zhang, 2018).

Total Monolines Used

The total monolines used is the total number of monolines installed and length of monolines to cultivate seaweeds. Growers who have a significant number of floating monolines has a possible positive impact on farm efficiency, where longer monolines can hold more seedlings for cultivation. This results to larger harvest volume, higher profit, and more efficient use of inputs (Valderrama D. , et al., 2015). However, it also assumes that growers have invested the right mount of capital and farm materials to maximize output and minimize loss (Valderrama D. , Cai, Hishamunda, & Ridler, 2013).

Attended Training

Multiple authors have identified the role of research and trainings for farmers as vital to the stable development of seaweed production (Zhang, 2018; ASTRULI, 2019; Cyber Colloids Ltd., 2019). The Seaweeds Industry Association of the Philippines is institution focused on improving the seaweeds industry in the country through training(s), intervention programs, and promotional activities.

Membership in a cooperative

Producer organizations such as cooperatives are advantageous for farmers in securing better prices, access to trainings and programs, and as a market channel for their produce (Narvaez, 2015; Neish, 2007; Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002). Cooperatives can also support their members through financing support via loans and inputs. It was included in the Tobit Regression model as a possible factor in production efficiency of a farm.

Harvesting Days

Harvesting days refers to the duration of harvesting mature seaweeds which can affect quality, freshness, and biomass volume (Trono, 1990). The implication is longer harvesting days result to less efficient farms.

Number of Cropping Season

Cropping period refers to the full cycle of cultivation from planting of the seedlings to harvesting (Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002).  Total number of croppings refers to the number of cycles on the farm in a year. The higher number of croppings can imply farms that are more efficient because they secure good quality seedlings, proper monitoring schedules, and other indicators of good farm maintenance practices (Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002). Seaweeds have a relatively short cropping cycle which falls, ideally, between 45 to 60 days (Nobleza, 2015).

Product Type

Seaweeds sold to the market by farmers can be categorized as wet or dry. Wet refers to the fresh seaweeds harvested directly from the farm that have not undergone any post-harvest processing (Fisheries Policy and Economics Division, 2010). In the Zamboanga Peninsula, 60 percent of farmers sell fresh seaweeds to buyers to get quick revenue and because they lack drying facilities near their communities (Narvaez, 2015). Dry refers to the Raw Dried Seaweeds that has undergone post-harvest processing and drying with a 38 to 45 percent moisture content (Narvaez, 2015). It is the type of product bought by processors using it to extract carrageenan, a highly demanded food additive (Monzales, 2017; Narvaez, 2015). In the Tobit Regression, producing one or both product types can lead to inefficiencies in the production. Issues such as harvesting seaweeds before they reach maturity and not leaving enough biomass for the next cropping are inefficiencies in farm management associated with the harvesting and selling wet seaweeds to buyers (Monzales, 2017; Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002; Narvaez, 2015). Production of RDS requires capital for post-harvest equipment such as dryers (Nobleza, 2015). Issues reportedly found in the quality of RDS because most small farmers don’t have the capacity to achieve the standard moisture content for RDS and they even resort to adding salt to speed up the drying process (Narvaez, 2015; Monzales, 2017).

Varieties Cultivated

Respondents in the survey cultivated the following seaweed varieties: Cotonii, Cabcab, Giant Pata, Gadung and Katunay. This is treated as a categorical variable which could identify a variety or varieties that has a significant impact on production efficiency (Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002).

Issues Stated

In the questionnaire, respondents were asked to cite issues they are currently facing during the period of the survey. These results are factored into the regression to determine if the issues identified by the farmers has a significant impact on farm efficiency.

Results and Discussion

Descriptive analysis

Value Chain Analysis

The seaweeds value chain is mapped in Figure 1 to illustrate the level of participation of seaweed farmers in this study with the larger industry. They sold wet seaweeds to barangay and sitio traders in the community. There are strikers - travelling traders from outside provinces - looking to purchase wet seaweeds during peak season. Also, farmers sell to wet market retailers for food consumption. During an interview, farmers revealed that they practice bartering with seaweeds in exchange for fruits and vegetables. The municipal trader in Ipil collects the volume from sitio and barangay traders, including those in the community. The large municipal trader is a primary source of capital for the sitio and barangay traders setting up an informal obligational buying contract (Nor, Gray, Caldwell, & Stead, 2019). Volume consolidated by the municipal trader are delivered to local processors. Processors are in Pagadian City and Zamboanga City. According to SIAP, there are three (3) large processors of carrageenan in Zamboanga City and 30 in the Philippines (Pedrosa, 2017).

Figure 1. Seaweeds value chain of KVAGA Growers

Source: Key informant interviews (2019)

Socio demographic profile

Small-scale seaweed farmers were asked to be respondents in a structured interview from April to September 2018. The typical respondent was female (63%), considered seaweed farming as their primary occupation (86%) and 60 out of the 64 respondents (94%) stated that seaweed was the main crop grown on their farms.

Respondents recorded an average annual off-farm income of P72,359 for the year 2017 from various sources such as owning small businesses, salaries, overseas (OFW) remittances, part-time laborers, pension, among others (Table 1). There were households who owned a small retail business called, “sari-sari”, stores. A few respondents earned income doing work as part-time laborers work in construction especially during the lean months. Some respondents also recorded salaries received from working as employees. According to their sitio leaders, an estimated 90 percent of the households in their community are recipients of the conditional cash transfer program of the national government called the Pantawid Pamilyang Pilipino Program (4Ps). Other sources of income outside of seaweed farming such as fishing, motorcycle drivers (“habal-habal”), and pension payment receipts were categorized under off-farm income as well.

Table 2. Socio-demographic profile of respondents

Location of Farm

The researchers differentiated farms in Sitio Katipunan, Barangay Magdaup and Barangay Buluan in terms of their location: near shore and deep sea. Both vary significantly in features such as: (a) distance, (b) water quality, (c) depth. Economic indicators were differentiated from the production location chosen by farmers to grow their seaweeds.

Table 2. Comparison of economic indicators based on farm location

Table 2 shows a significant difference in farmer income generated in the two locations. On average, farmers with seaweeds grown in deep-sea areas generated 140 percent more profit than farmers who planted in the near shore zone. Product sales were similar, P14,438 and P14,766, for both near shore and deep-sea location (Table 2). According to the survey data, productivity and cost of growing seaweeds deep sea was significantly higher than farms located near shore. On average, farms located at the deep-sea zone produced 1,246 kgs more wet seaweed volume compared to farmers who had seaweed farms near shore. Production cost was also lower for farms in the deep-sea zone recording a P2,830 difference between farms in the near shore zone. Notably, post-production cost was 150 percent higher for farmers with deep-sea farms. This is attributed to the high labor cost for harvesting due to distance and depth with laborers asking for higher fees because harvesting in this location requires more effort (Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002).

Several farmers also planted in two locations for one cropping. They have monolines in the deep-sea zone to grow the newly cut seedlings. After several days, the seedlings are transferred to their farms near the shore until harvest time. This practice is common among seaweed farmers in the area because in Sitio Katipunan most seaweed grower’s plant at the river mouth or near the frontage of the community which fall inside the near shore zone. This strategy was developed thru experience where they found that it is better to plant seedlings in the deep-sea zone because branches grow faster in this location. Transferring location from the deeper areas to the shallower ones is for easier farm management and reduction of cost. Environmental factors such as light intensity and temperature are more stable in the deep sea zone reducing plant stress and photoinhibition that stunts growth (Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002). It is common practice in the region to shift planting from the two locations to allow production areas to slowly filter out impurities from the previous cropping (Narvaez, 2015). Farmers also plant at the river mouth to avoid the current at sea. Planting near shore would be easier and cheaper for them to install the monoline (Narvaez, 2018) . Deep sea farms avoid the school of rabbitfish younglings during breeding season that feed on seaweeds. In terms of productivity, farms in the deep-sea zone had been identified as more productive. The depth and current delivers nutrients better to seaweeds facilitating growth because cultivation will be reduced to 30 days than the 45 days it normally takes for seaweeds to grow in the near shore zone (Narvaez, 2015). However, not all farmers can afford deep sea farming because it is more expensive due to the use of stronger materials since this location makes the farm more vulnerable to the damaging effects of the ocean such as current (Narvaez, 2015). Maintenance, harvest, and other important farming activities become more expensive due to the distance (DTI, 2015).

Type of product

Wet and dry seaweeds are produced from seaweed farms in Zamboanga. Raw dried seaweeds (RDS) undergo an extraction process to produce carrageenan (Nobleza, 2015). Carrageenan takes form in semi-refined and refined after processing which is used as ingredient in organic food products (DTI, 2015). Raw dried seaweeds are also bought by processors outside the country (Narvaez, 2015).

Table 3. Comparison of economic indicators by type of product sold

The respondents sold wet seaweeds more than RDS. The average volume of wet seaweeds sold was 1,880 kgs which was 300 percent of the average volume for dry seaweeds (Table 3). Sitio and barangay traders buy wet seaweeds from farmers. It is fairly common practice in seaweed farming communities due to the lack of drying facilities, immediate cash needs for the household (Nor, Gray, Caldwell, & Stead, 2019; DTI, 2015). Drying practice in the community takes four days which is not preferred by most small-scale farmers. Several farmers revealed they rely on the income from their seaweeds to finance their household needs hence the practice of harvesting less than 30 days after planting to attend to the pressing needs at home (Zamroni, 2018). Farmers also consider the prescribed period of 45 to 60 days to harvest as impractical because the weight of the fully mature seaweeds cause it to fall off the line and sink (Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002).

However, the profitability indicators clearly show that profit is maximized when they sell dry seaweeds instead of wet. Dry seaweed production, including post-production, costs 158 percent more than the total production cost of wet seaweeds. Despite the high cost, average net profit for farmers selling dry seaweeds was higher by a factor of fifteen compared to those who sold wet seaweeds. Selling dry seaweeds is more profitable because prices at the time of the survey for dry seaweeds was ten times higher than the wet seaweeds despite the lower volume sold. The lower volume is a consequence of primary processing with an estimated recovery rate of 6:1 during the sunny (dry) season and 7:1 during rainy (wet) season (Alisani, 2018; Narvaez, 2015). We should note that only seven respondents sold dry seaweeds while 39 farmers sold wet.

Technical Efficiency (DEA)

A total of 70 responses from the 114 responses were used after data cleaning. The output variable used in the DEA calculation was yield expressed in kilograms. The input variables used in the DEA calculation were seedlings, plastic straw, anchor bars/poles, floaters, binders, planting labor (man-days), and total area of seaweed farms.

Table 4. Variables for Technical Efficiency Computation

TE scores for majority of the respondents fell within the 0.901 to 1.0 range which implies efficient use of implements (Table 5). Interviews with farmers uncovered community practices such as borrowing/recycling of inputs and materials such as plastic bottles (as floaters). Seaweed production is treated as a family enterprise in many seaweed communities, including our respondents, reducing labor cost in production (Nor, Gray, Caldwell, & Stead, 2019; Pant, Barman, Murshed-E-Jahan, Belton, & Beveridge, 2013; Narvaez, 2018). A total of 14 farms feel below the 0.901 to 1.0 range which possibly indicate practices that contribute to inefficiency. Sources of inefficiency in farms are from external factors such as violent storms, pest, and diseases such as “ice-ice”, epiphytes, and rabbitfish (Narvaez, 2018; Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002; DTI, 2015).

DEA calculations in Table 5 showed 16 percent higher efficiency scores on average for farms located in deeper areas. Moving farms to deeper areas ensures protection from damage and stress and is considered a highly productive method (Zhang, 2018; Narvaez, 2015; Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002; DTI, 2015). This also proves that cultivating in the deeper parts of the ocean helps avoid the problem of “ice-ice” as it isolates seedlings from various environmental extremities such as strong currents, temperature and salinity (Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002; Valderrama D. , Cai, Hishamunda, & Ridler, 2013). However, most of the respondents are near shore cultivators due to its low capital and easy access as the producers doesn’t need to buy or rent any expensive boats (Narvaez, 2018; DTI, 2015).

In addition, the researchers also discovered farmers who sell RDS have 7.89 percent higher TE scores than those who sell wet. Farmers who sell RDS use/rent drying platforms which is less likely to get contaminants than the traditional makeshift drying areas (Nobleza, 2015). It can also be applied to farmers selling the two types of products (wet and RDS) because they also recorded high TE scores.

Table 5. Breakdown of technical efficiency scores of seaweed farmers, by farm location, and by product type

Tobit Regression

Using Tobit Regression, variables such as gender, farm location, type of product sold, and issues with bad weather were statistically significant in predicting the technical efficiency of Ipil seaweeds farmers (Table 5). The positive coefficient for gender means women participation help in farm efficiency by 20 percent. Women perform tasks such as cleaning, drying, tying, sorting, and cutting in the farm which contributes to the efficient use of inputs and materials (Narvaez, 2018; Valderrama D. , Cai, Hishamunda, & Ridler, 2013). In Indonesia, women seaweed farmers prefer farming near shore because it is more practical as it puts less of a toll on the body (ASTRULI, 2019).

Farming in deep sea areas has a positive impact on the TE of seaweed farms. According to Zhang (2018), the Chinese government had conducted restructuring of farms into deep sea locations in order to avoid pollution and extreme environmental conditions to produce higher volumes. It also reduces susceptibility to “ice-ice” as the seaweeds are isolated from the disease’s factors (Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002). Farming in deep sea locations will result in 29.14% higher TE scores despite high cost of input and materials. Out of the 114 respondents only 22 (19%) cultivate seaweeds in deep sea locations, due to the lack of capital, physical requirement, and need for logistics equipment such as banca (Province of Zamboanga Sibugay, 2015; Narvaez, 2018).

The type of product sold by seaweed farmers had a negative impact on efficiency. Farmers producing RDS will have 26 to 58 percent lower TE scores. According to key informant interviews, farmers who venture into RDS production need to acquire additional materials and a drying platform which is a huge constraint especially for small scale farmers (Nor, Gray, Caldwell, & Stead, 2019; Hurtado, Guanzon, De Castro-Mallare, & Luhan, 2002; Zamroni, 2018). Drying seaweed showed higher cost of production (Table 3) as they needed to buy inputs for drying. Respondent interviews revealed an unwillingness to dry seaweeds opting to sell wet produce directly to buyers.

Lastly, bad weather was another significant variable decreasing TE scores by 20.5 percent. Seaweed farming as a source of livelihood for farmers is susceptible to production shocks and stress on the plants due to weather hazards, tidal change, seasonality (Besta, 2013; Narvaez, 2015; Rajauria, 2015). Producing RDS through drying normally extends beyond the regular two to three days drying time during the rainy season (Nor, Gray, Caldwell, & Stead, 2019). Respondents also shared that applying salt to expedite the drying process was motivated by the uncertainty of weather (Narvaez, 2015).

Table 5. Tobit regression results

Note: * if p<0.1, ** if p<0.05, *** if p<0.01

Conclusions

The study uses the value chain framework of analysis to determine the current participation of a group of small-scale seaweed farmers in Ipil, Zamboanga Sibugay in the larger seaweeds value chain in the Philippines. Respondents were isolated as a supply base within the much larger industry. Most of the seaweed farmers were limited to selling products to sitio traders because they are closer in proximity. These buyers set prices at their level and rely on their long-standing relationship with the farmers. However, sitio trader prefer to buy wet seaweeds and perform value-adding activities (drying, sorting, etc.) on their own facilities. These terms were mostly dictated by location, peace and order situation, and subsistence mindset of farms. It is also strategy used by larger traders when sourcing from small coastal communities. Install an agent (barangay trader) within the community for easier consolidation. The barangay traders receive financing from larger traders to buy wet seaweed from farmers in the community and do primary processing to produce RDS. Lack of government presence within the area had also limited access to government services including livelihood programs leading to continued reliance on bad farm practices and use of outdated production technologies.

Growers sell to sitio traders to earn cash return from their farm. Early harvest is common practice to avoid losses and attend to immediate cash needs of the household. These are counter-productive practices because cultivation of seaweeds requires 45 to 60 days to reach full maturity. This information will be used to give context to the potential strategies for the improvement in farm efficiency of farmers in the area. The value chain framework had informed the calculations in determining the different factors that affect income which were type of product and farm location.

Variations in technical efficiency for farmers planting near shore and in deep sea were calculated which resulted to a mean score of 0.952 and 1.00, respectively. The minimum TE score for farms located near the shore was 0.526. On the other hand, all 11 respondents with farms found in deep sea areas recorded TE scores of 1.00. Deep sea farming is more technically efficient than farming in the near the coastlines. However, 85 percent of the respondents had planted their farms nearer the coastline than farther out because of the higher production cost, maintenance requirement, and the limited resources such as motor-powered banca. Farm location is a determinant of farm efficiency provided that farmer can overcome hurdles such as lack of capital, high cost, and deep sea farm production technology.

Seaweed farmers in Ipil, Zamboanga Sibugay sell wet despite earning lower profit compared to selling RDS. The mean TE score for wet seaweeds producers was at 0.981 with a minimum TE score of 0.663. On the other hand, farmers who sold RDS earned higher profit on average, a higher mean TE score of 0.995, and a minimum TE score of 0.914. Comparing the minimum TE scores between farmers selling wet and dry suggests that more farmers who sell dry are more technically efficient in their use of inputs.

Tobit regression results identified several variables with a significant effect on technical efficiency of the sample seaweed farms. These four variables: (a) gender, (b) farm location, (c) product type and (d) bad weather condition are indicators for efficiency.

The gender variable showed a positive impact on efficiency scores estimated at 19.99 percent. This suggests that farm efficiency can be improved by encouraging the participation of women in the farm, as well as, improving the technology, processes, and practices that women do such as cutting, seedling preparation, etc. Strategies addressing this need can be centered around the provision of tools and training on seaweed farming for women.

Farms located in deep sea areas improve efficiency by 29.14 percent. Assistance for farmers to plant in deeper areas can include training in production technologies for deep sea areas and seaweed farm financing. Farmers selling RDS had 26 to 58 percent less efficient farms depending on whether they sold RDS exclusively or sold both wet and RDS, respectively. Improving the drying practices of farmers by advising against salting, installing drying platforms, and teaching good post-harvest practices can further improve this.

Farms affected by bad weather conditions were 20.5 percent less efficient based on the TE results. Interventions from the government such as crop insurance can secure farmers in case of supply shocks brought by hazardous weather. Introducing production technologies that can help seaweed farmers avoid the bad effects of weather disturbances such as selecting multiple locations for planting, adapt effective farming systems, and production planning can be applied (Valderrama D. , et al., 2015).  

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