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International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 12 No: 03

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Optimization of Land Use and Allocation to Ensure Sustainable Agriculture in the Catchment Area of Lake Tondano, Minahasa, North Sulawesi, Indonesia Hengki D. Walangitan, Budi Setiawan, Bambang Tri Raharjo, and Bobby Polii

Abstract—Preservation of lake’s ecosystem and increasing productivity of dry land of Lake Tondano is a form of socioeconomic and ecological conflict in the use of land resources associated with sedimentation rate control of Lake Tondano due to erosion of the lake in one side and farmer income and agricultural employment in the other side. Therefore, a solution is needed to ensure optimal use of land for sustainable agriculture. Within this conceptual framework, we conducted a study aiming to analyze the optimal allocation of land use type in order to ensure sustainable agriculture in the catchment of the lake Tondano The soil erosion was evaluated using USLE model. The farming analysis was carried out to evaluate agricultural income and employment. The analysis on optimal land use type allocation was performed using goal programming. The result showed that the optimal land use type allocation with erosion control priority could be achieved in all scenarios, whereas the maximum employment target could not be achieved in all priority scenarios. The priority on achievement of farming income to support the needs of decent living for farmers and farm workers could be attained by using forest land for plantation of non-timber crops such as palm trees to produce sugar and local alcoholic beverage, which contributed significantly in increasing the income, employment, and acceptable erosion level of the catchment area.. Index Terms—Sustainable agriculture, goal programming, optimal land use allocation, priority scenarios.

I. INTRODUCTION

T

watershed ecosystem has a vital and strategic role for the economy of the region. Economic and ecological functions have contributed to economic growth in North Sulawesi Province through the tangible and intangible ONDANO

H. D. Walangitan is with the Postgraduate Program, Faculty of Agriculture, Brawijaya University, Jl. Veteran 2, Malang 6545 and Faculty of Agriculture, Sam Ratulangi University, Jl. Kampus Kleak, Manado 95115, North Sulawesi, Indonesia (corresponding author, phone +62-81356710105, e-mail: [email protected]). B. Setiawan and B. T. Raharjo are with the Postgraduate Program, Faculty of Agriculture, Brawijaya University, Jl. Veteran 2, Malang 6545, Indonesia. B. Polii is with the Postgraduate Program, Faculty of Agriculture, Sam Ratulangi University, Jl. Kampus Kleak, Manado 95115, North Sulawesi, Indonesia.

benefits. The tangible benefits include the potential contribution of agricultural land and environmental services from the utilization of the Lake Tondano outlet stream as hydroelectric power (hydropower) plant that generates power equal to 51 MW, the production of freshwater fish in the Lake Tondano with a potential of 60-180 kg.ha–1.year–1, the irrigation of rice field of approximately 8500 ha, and the use for drinking water. While the indirect benefits are the ecotourism value of the lake and the lake function as flood control for the city of Manado. The Tondano Watershed Management Institute (2008) recommended an integrated management with the goals (1) to increase community awareness and skills in order to implement the conservation and rehabilitation of land in agricultural systems and (2) to establish agricultural land use system based on the ability of land to support sustainable land use. Land use conflicts in Lake Tondano area are associated with the preservation of lake ecosystems where erosion and sedimentation rate is high and the will to improve farmers' welfare and income, to attain food security, and to provide jobs. Therefore, a land use type and allocation planning is required to achieve optimum sustainable farming systems. Sustainable agriculture is a dynamic concept that involves complex interactions of biological, physical, and socioeconomical factors and requires a comprehensive approach to improving existing systems and develops a new, more sustainable system. In order to achieve a sustainable agriculture at least three indicators must be met: a decent income for each farmer, less erosion than the tolerated erosion, and provide more agricultural employment [1]. Optimal allocation of land use is an activity to improve the efficiency of land use types by specifying the appropriate use of land [2]. Optimization model that can be employed for planning to achieve multiple targets is a goal programming [3]. This method is widely applied in farm planning such as for farm crop pattern, the pattern of optimal combination of crop and land use, and land allocation planning for the purpose of watershed landscaping, and efficient tool for integrating ecological and socio economic information and to explore possibilities for a sustainable agriculture [4]. Optimization analysis of land use with multiple goals can be

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International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 12 No: 03 done by preparing a variety of possible scenarios, whether viewed from the aspect of physical, economic or regional development policy. Approach to the analysis is carried out through land evaluation and analysis of input–output of major agriculture commodities in the study area, or potential commodities that has not applied by the farmers. Relationships of input–output, objectives, constraints and policies are formulated in mathematical form [5]. The objective of this study was to find the optimal allocation of land use type in order to (1) generate farming income to support the needs of decent living for farmers and farm workers, (2) accommodate more of the agricultural labor, (3) the average soil erosion per hectare is below the value that can be tolerated and (4) guarantee a minimum forest area in the catchment area. II. RESEARCH METHODS A. Study Area The area of interest of this study was the catchment area in the basin of Lake Tondano, situated in Minahasa District, North Sulawesi Province in Indonesia. Geographically the study area lies between 1° 06'- 1° 20' N and 124° 45'- 124° 58' E, 700-1000 meters above sea level (masl) with an area of 18,466.95 hectares. The study area accommodates some 69 villages.

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analysis of land use allocation optimization. Maps were used including zone maps of land capability and land use maps obtained in 2009 from BPDAS Tondano, and socio-economic characteristic of the data including county statistics, 2010 Village Potential Data (PODES) 2008, survey data and farm income. C. Model and Analysis Techniques Analysis of the Land Unit In this study land units based on slope class, land use and soil type. Furthermore overlay map land units with land capability zoning map to identify the types of land use in each zone. Farming Analysis on Major Land Use Types Farming analysis on each type of land use was emphasized in three variables: farming acceptance, cost, and income including the number of labor allocated in the farming. The farming analysis was done by two approaches: (1) input– output data obtained from interviews to respondent farmers on the type of land use being sought and (2) input–output data obtained from previous researches and interviews with agricultural field tutors in the study region as a comparison. Soil Erosion Prediction in each Type of Land Use Tolerable soil loss (TSL) due to erosion in each type of land use was calculated using equation (1) [6]:

TSL =

DE × Fd × BD × 10 T

(1)

where TSL is the tolerable soil loss (ton.ha–1.year–1), DE is the effective soil depth factor (mm), Fd is soil depth factor (according to Sub Order of soil), T is resources life (year) (for conservation purposes 400 years), and BD is bulk density (g/cm3). The amount of erosion was estimated by calculating the average annual soil loss from land use type calculated by the Lost Universal Soil Equation (USLE) [7]:

A = R × K × LS × C × P

(2)

where A is soil loss (ton.ha–1.year–1), R is rainfall erosion index, K is soil erodibility factor, LS is slope length and steepness factor, C is vegetation cover factor, and P is erosion control practice factor.

Fig. 1. A map showing the area of interest bordered by the red rectangle (enlarged in the inset). The images were generated from Google Earth version 6.0.3.2197 using image taken on April 16, 2003.

B. Data and Tools The tools used in this study include the computer with the operating system MS Windows XP MS Office especially Excel for data analysis, and Qwin32 Program version 2.0 for

Analysis of Income Levels to Support the Needs of Decent Living (KHL) and the Absorption of Labor The threshold value for food sufficiency level of household expenses in rural areas is equivalent to the rice exchange rate ranged between 240-320 kg [8]. Therefore, income levels can support the needs of decent living (KHL) for farmers and farm workers, used in this study was 320 kg/person/year × the price of rice (IDR/kg) × number of family members (persons/ household) × 2.5. In this study the amount of farm income to

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International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 12 No: 03 meet the needs of decent living is IRD 15.18 millions for every farmer and farm worker households per year. The amount of labor in farming that is measured from the number of labor required for tillage, crop maintenance, and harvesting is expressed in adult male workday amount (AMWA) unit [9]. The assumptions in this calculation were: (1) working age 15-64 years old, (2) the proportion of farmers and farm workers based on PODES data in 2008, and (3) the adult female was equivalent to 0.75 AMWA. Optimization Analysis of Land Use Variables and Parameters The land use types included in the optimization model were (1) annual crop field, (2) rice paddies, (3) residential, (4) mixed farm, and (5) forest. The types of land use were defined as the optimal activity according to the suitability of land capability zone and presented in Table I. TABLE I NOTATION OF LAND USE TYPE AS THE OPTIMAL ACTIVITY ACCORDING TO LAND CAPABILITY ZONE IN LAKE TONDANO Land activity (variable j) Capability annual rice mixed Class residential forest crop field paddies farm (variable i) (X3) (X5) (X1) (X2) (X4) I II III IV V VI VII VIII

X11 X 21 X 31 X 41 X 51 X 61 -

X 12 X 22 X 32 X 42 X 52 v62 -

X 13 X 23 X 33 X 43 X 53 X 63 -

X 44 X 54 X 64 X 74 -

X 65 X 75 X 85

The formulation of land use allocation optimization targets was expressed as follows: 1. The combination of land use that generates farming income that meets the the needs of decent living for farmer households and farm workers in the amount of IDR 15.18 million per year. 2. Combination of land uses that can provide employment for farm households and agricultural laborers in the watershed 3. The combination of land use type in the catchment area that generates land erosion does not exceed the Tolerable Soil Loss (TSL). 4. The combination of land use type in the catchment area that can guarantee a minimum area of forest to 1014 hectares as the protected forest area according to forest land use map of North Sulawesi. These four objectives were considered to have equal weight, but in the analysis simulations were made with different priority scenarios. General model used in the optimization of the allocation of land use in the catchment area of Lake Tondano are as follows:

∑( P d n

Z=

Minimize

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)

(3)

+ di− − d1+ = b1

(4)

+ − ij ij

+ Pij− dij+

i =1

Constraint functions: m

(goal constraint)

∑a

ij X ij

j =1

For : i = 1, 2, . . . ., m goals m

∑g

jk X j

≤ or ≥ Ck

(5)

j =1

For k = 1, 2, …, p functional constraints j = 1, 2, …, n decision variables And

X j d i− , d1+ ≥ 0

di− , di+ = 0 where Z is the objective, di− and di+ are amount unit underachieved (–) or overachieved (+) to the objective (b1), aij is coefficient functions of the constraints of technology-related goals of decision variables (Xj), bi is the goal or target to be achieved, Xj is decision variable, gjk is coefficient function of the technology constraint, and pi is priority value of the target. Some assumptions underlying this study were: (1) extensive catchment area used in the analysis did not include lake area and rivers, (2) farm income of each type of land use did not take into account the aspect of accessibility, (3) farm income was considered equal to the land capability classes I to III, while on land capability classes IV, V and VI assumed a decline in revenues of 10%, (4) optimization model in this study was a static model that did not take into account the time, and (5) number of agricultural labor was the population living in the catchment area of Lake Tondano.

III. RESULTS AND DISCUSSION A. Actual Land-Use Types Based on Land Capability General description of the lake catchment area Tondano according to land capability classes are presented in Table II. It shows that classes I, V and VI are the most dominant classes in the catchment area of Lake Tondano. Actual conditions of land use in the study area based on data from Landsat imagery interpretation from BP DAS Tondano [11] are presented in Table III which shows that the most dominant land use were paddy field, arable upland and mixed estate. Furthermore, based on analysis of the land unit, data showed that the dominant type of land use in class I to class V was the annual dryland crop, paddy fields and settlements. While on a classes VI – VIII, the dominant types of land use was mixed estate and forests (primary and secondary forest).

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International Journal of Civil & Environmental Engineering IJCEE-IJENS Vol: 12 No: 03

TABLE II CATCHMENT AREA OF LAKE TONDANO BASED FOR LAND CAPABILITY CLASS Zone/class Area (ha) Percentage ( %) I 4570.53 24.75 II 723.68 3.92 III 500.83 2.71 IV 2234.28 12.10 V 4445.87 24.07 VI 5626.57 30.47 VII 266.14 1.44 VIII 99.05 0.54

TABLE III ACTUAL LAND USE TYPE IN THE CATCHMENT AREA OF LAKE TONDANO Percentage No. Land Use Type Area (ha) (%) 1. Primary forest 462.42 2.50 2. Secondary forest 988.03 5.35 3. Mixed estate 3073.21 16.64 4. Residential 1536.10 8.32 5. Arable upland 4509.66 24.42 6. Paddy field 7750.83 41.97 7. Bush 146.70 0.79 Total 18466.95 100.00

B. Calculation of Input Coefficient Value A. Amount of Erosion in Each Land Use Activity and the Tolerable Soil Loss Calculation of the TSL used the soil type data[12] showed that the effective average soil depth in sub watersheds of Lake Tondano is 1500 mm, the soil sub order is Udands with value 1.00, land use age of 400 years, and soil volume weight (bulk density) 1.24 (g/cm3). The accepted TSL was 40.125 tons.ha– 1 .year–1. Results on erosion prediction generated in each type of land use showed that the type of mixed farm and annual crop on steep to very steep slopes produced the highest erosion >500 tons.ha–1.year–1. The amount of erosion in any type of land use is presented in Table IV. Further erosion of the target value that can be tolerated (TSL) by the results of the analysis with equation (1) is 40 tons.ha–1.year–1 and thereby target erosion for the watershed is 738 678 ton year–1. This value is included in the category of high tolerance. For the purpose of controlling sedimentation in the lake Tondano, a value of TSL for 12-25 tons.ha–1.year–1 was used [12]. Thus the erosion of the target on average in the entire watershed area is expected to not exceed the value of 221,603-461,673 tons.year–1.

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TABLE IV PREDICTION OF THE SOIL EROSION THAT OCCURS IN VARIOUS TYPES OF LAND USE

Land Use Type Forest Mixed farm (Clove + tree) Mixed farm (Clove + corn) Mixed farm(Clove + peanut) Corn with good terrace Corn with bad terrace Corn with fairly good terrace Peanut with fairly good terrace Tomato with good terrace Rice Paddies

>25 >40 25-40 15-25

Land Capacity Zone VII, VIII VII VI III, IV

Erosion (ton.ha–1 year–1) >1 - 21 >1000 954 72 - 98.91

40 25-40

I, II, III VII VI