View of Optimization Utilization Study Storage of Raknamo Dam Amabi Oefeto District Kupang Region

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*Corresponding author: Water Resources Engineering Department, Engineering Faculty, Universitas Brawijaya, Malang 65145, Indonesia E-mail address: [email protected] (Frits Jeferson Bale)

doi: https://doi.org/10.21776/ub.pengairan.2023.014.02.4 Received: 2022-11-09; Revised: 2023-01-03; Accepted: 2023-11-29.

Vol. 14 No. 02 (2023)

Jurnal Teknik Pengairan: Journal of Water Resources Engineering

Journal homepage: https://jurnalpengairan.ub.ac.id/index.php/jtp

Original research article

Optimization Utilization Study Storage of Raknamo Dam Amabi Oefeto District Kupang Region

Frits Jeferson Bale*, Muhammad Bisri, Sri Wahyuni

Water Resources Engineering Department, Engineering Faculty, Universitas Brawijaya, Malang 65145, Indonesia

A R T I C L E I N F O A B S T R A C T Keywords:

Crop productivity;

Dynamic deterministic;

Optimization;

Raknamo reservoir;

Water distribution

Raknamo Irrigation area (1323 ha) has problems with water availability, especially during the dry season, which causes a decrease in the productivity of the agricultural products from local communities. This is due to the suboptimal water distribution system in the Raknamo Reservoir.

This study aims to analyze and optimize water distribution in the Raknamo Reservoir to obtain the most optimal water distribution to fulfill agricultural land's water needs in various seasons.

Optimization analysis is carried out using a deterministic dynamic program. With this program, a complex and large-scale problem can be dispart into several small parts (decomposition), which are then optimized. Based on the analysis results, the maximum profit that can be obtained from the cropping intensity is 16% in the normal season. Meanwhile, the highest increase of the land area is 218.64 ha. It is worth noting that this study uses a deterministic dynamic program for optimization analysis to dispose of the complex problem into smaller, manageable parts. This approach facilitates a more systematic and comprehensive analysis and provides a framework for future decision-making processes related to water management and agricultural planning.

The study's methodology and findings can serve as a valuable reference for similar water-related challenges in other irrigation areas, thereby contributing to the broader agricultural research and sustainable water resource management field.

1. Introduction

Reservoir as a water resource facility has a function to store the water. This storage is certainly expected to bring benefits to the surrounding area. In other words, a reservoir can also be interpreted as a large-scale water provider expected to fulfill citizens' water needs. One of the reservoirs that finished building in 2018 is Raknamo Dam, located in Amabi Oefeto District, East Kupang Regency, Nusa Tenggara Timur.

Raknamo Reservoir has a 14.09 million m³ water storage;

accordingly, the Raknamo Reservoir has the potential as a water provider in the region, especially during the dry season, both as a provider of irrigation water, raw water, and electrical energy. One of the beneficiaries of the Raknamo Reservoir is the Raknamo Irrigation Area, which often experiences water shortage problems in the dry season. The great potential of the Raknamo Reservoir is expected to overcome the problems in the Raknamo Irrigation Area.

Therefore, this study analyzes the optimization utilization of Raknamo Dam storage to ensure the water in the storage can be optimal in providing benefits for irrigation.

Several studies mentioned before yielded the same results as this study. Still, the research location was reservoirs on the island of Java, which are flowed by rivers with high baseflow.

In comparison, this study was conducted in locations in Kupang District with a dry tropical climate with intermittent river characteristics and low baseflow.

A shortage of irrigation water that occurs can reduce the benefits of agricultural products obtained and trigger potential conflicts between water users [1]. In addition, water shortages can cause crop failure, so an effective water use strategy is needed to overcome these negative impacts [2].

Optimization calculations are needed based on the problems in the Raknamo Irrigation Area to determine the maximum profit that can be obtained based on the discharge flowing in the irrigation area. The optimization model is an effective technique for determining the efficient allocation of irrigation water and has been of great interest in research in recent years [3]. The optimization model in this study is a deterministic dynamic program. The results can help determine water resources management policies by maximizing the benefits obtained and minimizing the possible risks [4].

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This study used the dynamic program, which in principle is a scientific approach to optimize with a multi-stage decision process; what is the pattern of distribution of water that can be flowed; what is the optimal area of land that can be utilized;

and what is the maximum profit that can be obtained based on the inflow that is flowing. Several previous researchers have discussed using the dynamic program method for irrigation optimization.

Using the dynamic program method for optimization in the Gembleng Kanan Irrigation Area can increase profits by 11.5% from before optimization [5]. Using the dynamic program method for optimization in the Tengoro Irrigation Area can increase profits by 20.84% in the first planting season and 0.85% in the second planting season [6]. In addition, it is known that there has been an increase in irrigation profits of 10.7% by implementing dynamic programs as an irrigation optimization method in the Amu Darya Watershed [7]. Then, it was also shown that optimization with a dynamic program could increase profits by 8% according to the availability of debits in the area [8].

The results of this study are expected to be useful for related Institutions in implementing the Raknamo Reservoir water distribution system so that local communities and farmers can increase their agricultural productivity.

2. Method 2.1. Study Area

Based on the topographic map, Raknamo Dam is located on the Noel Puames River, which administratively is in the area of Dusun Oepoi, Raknamo Village, Amabi Oefeto District, Kupang Regency, East Nusa Tenggara Province, and is located at coordinates 10°07'08" South Latitude and 123°55'54" E (Figure 1). The service area of Raknamo Reservoir has been changed due to the Regional Spatial Plan of Kupang Regency. The service area allocated to their regional irrigation is the Raknamo Irrigation Area.

The location of the Raknamo Irrigation Area is in the area of Raknamo Village, Manusak Village, Amabi Oefeto District, and Naibonat Village, East Kupang District, East Nusa Tenggara Province (Figure 2). A four-wheeled vehicle can reach the work location for 1.5 hours east of Kupang City.

Geographically Raknamo Irrigation Area is located at coordinates 10° 04ʹ 31.0ʺ - 10° 07ʹ 06.9ʺ South Latitude and 123°

51ʹ 36.8ʺ - 123° 55.49ʹ 2ʺ East Longitude. The potential irrigation area in Raknamo Irrigation Area is equal to 1323 ha.

Figure 2. Irrigation Map

2.2. Optimation Model

In general, the concept of a deterministic dynamic model is to allocate x amount of resources to n targets to optimize the benefits that can be obtained. Dynamic programming has the characteristics of a problem, which is broken down into several stages, with decision variables at each stage. These stages are irrigation structures that will be studied. Therefore, this follows buildings for tapping, which are sequential and interdependent between one building and another in an irrigation network system in an irrigation area [9]. According to the elements of the dynamic program used in the study, there are stages, decision variables (dn), status variables (Sn), and stage consequences (rn).

The decision for the next stage does not depend on the decisions taken (at the previous stage). Completion of Dynamic Programming starts from the initial stage and moves to the final stage (forward recursive) or vice versa (backward recursive). In forward recursive, for each stage, the optimal policy is determined based on the optimal policy from the previous stage and the objective function. Figure 3 presents the scheme of the forward recursive equation, which can be written as follows [10]:

𝑓

^∗

𝑛(𝑆𝑛) = 𝑜𝑝𝑡𝑑𝑛 [ 𝑟𝑛 (𝑆𝑛, 𝑑𝑛)𝑂𝑓

^∗

(𝑆𝑛 − 1)]

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Where O denotes an algebraic operation which can be in the form of addition, subtraction, multiplication or others as intended in the problem.

For the backward recursive procedure, the equation is as follows:

𝑓

^∗

𝑛(𝑆𝑛) = 𝑜𝑝𝑡𝑑𝑛 [ 𝑟𝑛 (𝑆𝑛, 𝑑𝑛)𝑂 𝑓

^∗

(𝑆𝑛 + 1)]

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Figure 3. N-stage system dynamic programming model

Raknamo Main Dam

Raknamo Dam Impounding Area Naunu Dam

Kuledoki I Dam Kuledoki II Dam

RAKNAMO VILLAGE

NAUNU VILLAGE

FATULEU SUB-DISTRICT

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136 2.3. Study Data

This study requires some data to support the research. The data required are as follows:

1. Rainfall data

The required daily rainfall data is from the last ten years, from 2012 to 2021, at the Raknamo rain station.

2. Discharge data 3. Soil type data

Soil type data is required to calculate the water required for irrigation based on WLR and percolation values in the service area.

4. Climatology data

This data is needed to calculate the potential evaporation value in the service area. The data required is climatology data from Naibonat climatology station.

5. Global Planting Plan (RTTG) data

RTTG data is needed to know the description of the cropping system for one year at the study location by considering water availability.

6. Farm business data

This data includes product prices, crop productivity, production yields, production costs, and net profits.

2.4. Study Work Steps

The optimization of reservoir operation pattern requires the systematic study step as follows: calculate the irrigation water requirements, the climatological data was calculated on average to get the potential evapotranspiration value using the modified Penman method, calculating crop water requirements, i.e.crop coefficient multiplied by potential evapotranspiration, determine the rate of land percolation determining the need for land cultivation, calculating effective rainfall from Rainfall Pos Raknamo and Climatological Pos Naibonat, determining the efficiency of the irrigation network, calculation of the need for clean water in the fields, calculation of irrigation water needs in the intake building, calculating the volume of water requirements, calculate the volume of available water from the main stay discharge for each growing season, calculating the area of irrigated land based on the division between the volume of water available and the volume of water needed.

Calculating production costs based on the type of plant, namely the multiplication between volume and unit price, calculating the gross benefit based on the type of plant, namely the multiplication between the production of the plant and the selling price of the plant, calculating net benefits from the difference between gross benefits and production costs, to get the benefits of irrigation based on the type of plant, namely the multiplication of the area of land irrigated with the net benefit. The optimization results are in the form of profits from agricultural production with the most optimum irrigated land area.

3. Result and Discussion 3.1. Rainfall

Potential evaporation was obtained using the Penman method based on rainfall data (Table 1) from the Raknamo rain post and climatological data from the Naibonat climatology post. Irrigation water is needed to meet plants' water needs within a certain area (Table 2) [11]. Rainfall can be an important factor in differences in the need for irrigation water in one growing season. It can be used to save water in agricultural production [12]. Determination of effective rainfall varies for each type of plant [13].

Table 1. Calculate reliable rainfall

No

Rainfall Data

Ranking

Data Note

Year R Year R

1 2012 854 2012 854 R 97 2 2013 1548 2019 1548 3 2014 1086 2020 1086 R75

4 2015 919 2015 919

5 2016 1297 2018 1297 R51 6 2017 1473 2014 1473 7 2018 1023 2016 1023 8 2019 887 2017 887 R26

9 2020 905 2013 905

10 2021 3738 2021 3738

Table 2. Water requirement for land preparation

No Var Unit Month

Jan Feb Mar Apr May Jun Jul Ags Sep Oct Nov Dec

1 Eto (mm/d) 5.59 6.53 6.27 4.98 4.64 4.64 4.45 5.19 6.39 7.13 7.52 6.34 2 Eo (mm/d) 6.15 7.18 6.89 5.47 5.10 5.11 4.89 5.70 7.03 7.84 8.27 6.98 3 P (mm/d) 4.50 4.50 4.50 4.50 4.50 4.50 4.50 4.50 4.50 4.50 4.50 4.50 4 M (mm/d) 10.65 11.68 11.39 9.97 9.60 9.61 9.39 10.20 11.53 12.34 12.77 11.48

5 T Day 31 29 31 30 31 30 31 31 30 31 30 31

6 S Mm 300 300 300 300 300 300 300 300 300 300 300 300

7 k - 1.10 1.13 1.18 1.00 0.99 0.96 0.97 1.05 1.15 1.28 1.28 1.19

8 LP (mm/d) 15.96 17.26 16.47 15.80 15.26 15.56 15.12 15.66 16.85 17.13 17.71 16.52 (lt/s/ha) 1.85 2.00 1.91 1.83 1.77 1.80 1.75 1.81 1.95 1.98 2.05 1.91

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3.2. Planting Pattern

At the study location of the Raknamo Irrigation Area with a land area of 1323 ha, the existing cropping pattern of the PU method [14] based on the Global Planting Plan (RTTG) of the Balai Wilayah Sungai Nusa Tenggara II is planned to plant rice, secondary crops (corn). The calculated data comes from one Raknamo Rainfall Post obtained from the Naibonat Climatology Post. An analysis of water needs is carried out in calculating irrigation water needs, which is influenced by potential evapotranspiration factors, effective rainfall, percolation, crop coefficient, land management, irrigation efficiency, and various other factors. Multiple Planting Arrangements with each - each percentage of planting area:

1. MT 1 rice field (56.86% or 752.257 ha) – Palawija (43.14%

or 570.742 ha) with a rice production of 4137.4 tons and 3424.5 tons of corn.

or 972.934 ha) with a rice production of 1925.4 tons and corn production of 5837.6 tons.

3.3. Area Irrigation

The available water volume is taken from the reservoir water volume, while the maximum planted area is the existing standard area of rice fields, which is 10,651 ha. Suppose the discharge given in each planting season can irrigate the available land area. In that case, it will be allocated to the next planting season and the profit of production when irrigating the maximum land area. In this study, the optimization process is divided into three stages; at each stage, we allocate a certain volume of water in buildings for the Raknamo irrigation area with building code numbers BR4, BR9, and BK1. Figure 4 is a scheme of the Raknamo irrigation area, and Table 3 is the building code data and area in the Raknamo Irrigation Area.

Figure 4. Raknamo irrigation scheme

Table 3. Area raknamo irrigation

No Plot Area (Ha) No

Plot Area (Ha) No

Plot Area (Ha)

1 R1 Ka 39.80 BR 4 408.52 BR 9 579.80

2 R2 Ka 58.75 16 R9 Ki 4.50 29 K1 2 Ka 35.10

3 R3 Ka 3.00 17 R9M Ka 53.80 30 K1 2 Ki 29.80

4 R1 Tg 12.76 18 R9 Ka 1 39.00 31 K1 3 Ka 43.80

5 R4 Ka 4.00 19 R9 Ka 2 11.20 32 K1 3 Ki 35.70

6 R5 Ka 13.00 20 R10 Ka 58.60 33 K1 3M Ka 54.20

7 R6 Ka 44.20 21 R10 Ki 12.30 34 K1 3M Ki 33.50

8 R7 Ka 3.70 22 R11 Ki 60.00 35 P1 Ki 63.30

9 R8 Ka 53.40 23 R12 Ka 37.80 36 P1M Ki 60.15

10 S1 Ki 21.87 24 R12 Ki 49.60 BK 1.2 355.55

11 S2 Ka 4.87 25 R13 Ki 64.56

12 S2 Ki 14.61 26 R14 Ka 46.51

13 K2 1 Ka 16.45 27 R14 Ki 59.30

14 K2 1 Ki 75.41 28 R14M Ka 82.63

15 K1 1 Ki 42.70

Diversion Box BR4 Stage 1

Diversion Box BR9 Stage 2

Diversion Box BK1 Stage 3

RAKNAMO PRIMARY CHANNEL

KULEDOKI 1 PRIMARY CHANNEL KULEDOKI 2

PRIMARY CHANNEL KULEDOKI 2 SUPLESION PRIMARY CHANNEL

Noel River

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138 3.4. Optimization Analysis

Optimization analysis in this study will be carried out in two growing seasons and four discharge conditions. In this study, it is assumed that If the allocation of water exceeds the need, the excess water will be wasted to be used by the irrigation areas below. If water allocation is below the requirement, then only the allocated water will be used and bring profit. In deterministic dynamics, the input is the average value in each plantation season [15].

In dynamic program modeling in this study using the Network model with Back Recursive, several things are determined [16]: The constraint function in this optimization is Volume Availability in "units." The objective function of this optimization is to maximize profits. There are 3 (five) stages, namely Building for BR.4, BR.9, and BK 1.2. The decision variable is the allocation of x for each channel. Some notations

for optimization analysis are as follows: F(x) is an estimate of the profit from each unit allocation value at a certain stage (expected value), Si is the stage (stage), Fi * is the maximum gain in stage I where is the remaining volume available in units. Then, calculate the Expected Value table, namely the contents of the Fi(x) table, where I is the canal, and x is the unit volume of water allocated, for example, optimization in MT2 on 97% Discharge.

The x value in Table 4 is 15. This value indicates the profit obtained based on the number of units allocated with buildings for those given water units. The number of x values is 15 because the number of units of water volume available is 15. In the first stage, the remaining water units are obtained based on the difference from the water needs in the allocation of BR 4 MT II, which has reached the maximum point in its profit or has irrigated all of its land.

Table 4. Expected value on 97% discharge

0 1 2 3 4 5 6

BR4 (MT II) F1 (x) 0 1,711,444,049 3,422,888,099 5,134,332,148 5,739,509,320 5,739,509,320 5,739,509,320 BR9 (MT II) F2 (x) 0 1,711,444,049 3,422,888,099 5,134,332,148 6,845,776,198 8,145,910,858 8,145,910,858 BK1.2 MT II) F3 (x) 0 1,711,444,049 3,422,888,099 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 BR4 (MT III) F4 (x) 0 1,205,924,186 2,411,848,371 3,617,772,557 4,823,696,742 5,739,509,320 5,739,509,320 BR9 (MT III) F5 (x) 0 1,205,924,186 2,411,848,371 3,617,772,557 4,823,696,742 6,029,620,928 7,235,545,114 BK1.2 MT III) F6 (x) 0 1,205,924,186 2,411,848,371 3,617,772,557 4,823,696,742 4,995,306,322 4,995,306,322

7 8 9 10 11 12 13 14 15

5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 5,739,509,320 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 8,145,910,858 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322 4,995,306,322

Table 5. Final optimization on 97% discharge

Available Stock 0 f5*

0 23,099,246,071 23,099,246,071 1 23,099,246,071 21,893,321,885 2 23,099,246,071 20,687,397,700 3 23,099,246,071 19,481,473,514 4 23,099,246,071 18,275,549,328 5 21,970,720,990 16,975,414,668 6 20,398,302,767 15,402,996,445 7 18,686,858,718 13,691,552,396 8 16,975,414,668 11,980,108,346 9 15,263,970,619 10,268,664,297 10 13,552,526,569 8,557,220,247 11 11,841,082,520 6,845,776,198 12 10,129,638,471 5,134,332,148 13 8,418,194,421 3,422,888,099 14 6,706,750,372 1,711,444,049

15 4,995,306,322 0

Maximum 23,099,246,071

Decision 0

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In the second stage, water availability starts from the remaining water in the first stage. In contrast, the remaining water is the difference between the total existing water units and the water availability from the previous stage [17].

Example of calculation for available 12 and remaining 7 (IDR 13,280,243,006.39) using the following equation:

𝐹_𝑖^∗(𝑆_𝑖) = 𝐹₁^∗ is the maximum profit of the previous stage (S1) for 12 units of water available = IDR 5,134,332,148.32.

𝑅𝑖+1(𝑆𝑖+1,𝑑𝑖+1)= Is the value of the profit if allocated three units (d2 = 3) in stage 2 (S2), which comes from the reduction of the volume unit used available with the remainder (15-12 = 3). This value can be seen in the value table Expected Value F2 (x = 3) = IDR 8,145,910,854.97

So that:

𝐹_𝑖+1^∗ (𝑆_𝑖+1) = 𝑅_𝑖+1(𝑆𝑖+1,𝑑𝑖+1) + 𝐹_𝑖^∗(𝑆_𝑖)

= IDR 8,145,910,854.97 + 5,134,332,148.32

= IDR 13,280,243,006.39

Then, in the third stage, up to one stage before the last stage, optimization calculations are carried out with full water

availability and full residue.

At this or the last stage, the water is assumed to be used up so that the remaining = 0. With backtracking, a path with the maximum (optimal) value is obtained: 15 – 12 – 7 – 4 – 0 – 0 with a total profit of IDR 23,099. 246,070.87 with optimal allocation and profit (obtained from the EV table based on unit allocation) for each stage (Channel) as follows:

Stage 1 = 15 - 12 = 3 units --> IDR 5,134,332,148.42 Stage 2 = 12 - 7 = 5 units --> IDR 8,145,910,857.97 Stage 3 = 7 - 4 = 3 units --> IDR 4,995,306,322.10 Stage 4 = 4 - 0 = 4 units --> IDR 4,823,696,742.39 Stage 5 = 0 - 0 = 0 units --> IDR 0

Stage 6 = 0 - 0 = 0 units --> IDR 0

Total of All Stages = IDR 23,099,246,070.87 (Table 5).

Then, a check is made between the decisions in stage 6 and the total profit after backtracking is carried out. If the numbers are the same, then the number of unit volumes allocated is correct. This step is carried out for other reliable debits [18].

Thus, the optimization results are obtained in Table 6 to Table 9.

Table 6. Optimization result on 97% discharge

Divider Structure

Profit (IDR) Irrigated Land Area (ha) Productivity per ha (ton/ha)

Rice Corn

Before

Optimize After Optimize Before Optimize

After Optimize

Before Optimize

After Optimize

Before Optimize

After Optimize

BR4 MT 1 7,546,402,093 7,546,402,093 408 408 2246 2246 - -

BR 9 MT 1 10,710,378,765 10,710,378,765 579 579 3188 3188 - -

BK 1.2 MT 1 6,567,911,642 6,567,911,642 355 355 1955 1955 - -

BR4 MT 2 5,739,509,320 5,134,332,148 408 365 - - 2451 2192

BR 9 MT 2 8,145,910,858 8,145,910,858 579 579 - - 3478 3478

BK 1.2 MT 2 4,995,306,322 4,995,306,322 355 355 - - 2133 2133

BR4 MT 3 3,617,772,557 4,823,696,742 257 343 - - 1545 2060

BR 9 MT 3 - - - -

BK 1.2 MT 3 - - - -

Total 47,323,191,557 47,923,938,571 2,945 2,988 7,391 7,391 9,608 9,864

Deviation 600,747,014 43 0 256

Divider Structure

Rice Corn

Before

After Optimize

Before Optimize

After Optimize

Before Optimize

After Optimize

BR4 MT 1 7,546,402,093 7,546,402,093 408 408 2246 2246 0 0

BR 9 MT 1 10,710,378,765 10,710,378,765 579 579 3188 3188 0 0

BK 1.2 MT 1 6,567,911,642 6,567,911,642 355 355 1955 1955 0 0

BR4 MT 2 5,739,509,320 4,799,776,407 408 341 0 0 2451 2049

BR 9 MT 2 8,145,910,858 7,999,627,345 579 569 0 0 3478 3416

BK 1.2 MT 2 4,995,306,322 4,799,776,407 355 341 0 0 2133 2049

BR4 MT 3 2,386,955,263 4,773,910,527 169 339 0 0 1019 2038

BR 9 MT 3 0,00 1,193,477,632 0 84 0 0 0 509

BK 1.2 MT 3 0,00 0,00 0 0 0 0 0 0

Total 46,092,374,263 48,391,260,817 2,857 3,021 7,391 7,391 9,082 10,064

Deviation 2,298,886,553 164 0,00 981

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140

Divider Structure

Rice Corn

Before

After Optimize

Before Optimize

After Optimize

Before Optimize

After Optimize

BR4 MT 1 7,546,402,093 7,546,402,093 408 408 2246 2246 0 0

BR 9 MT 1 10,710,378,765 10,710,378,765 579 579 3188 3188 0 0

BK 1.2 MT 1 6,567,911,642 6,567,911,642 355 355 1955 1955 0 0

BR4 MT 2 5,739,509,320 5,569,817,776 408 396 0 0 2451 2378

BR 9 MT 2 8,145,910,858 7,426,423,701 579 528 0 0 3478 3171

BK 1.2 MT 2 4,995,306,322 3,713,211,850 355 264 0 0 2133 1585

BR4 MT 3 5,739,509,320 4,706,830,337 408 335 0 0 2451 2010

BR 9 MT 3 4,706,830,337 7,844,717,229 335 558 0 0 2010 3350

BK 1.2 MT 3 0,00 3,137,886,892 0 223 0 0 0 1340

Total 54,151,758,657 57,223,580,285 3,431 3,649 7,391 7,391 12,524 13,836

Deviation 3,071,821,628 218 0,00 1.311

Divider Structure

Rice Corn

Before

After Optimize

Before Optimize

After Optimize

Before Optimize

After Optimize

BR4 MT 1 7,546,402,093 7,546,402,093 408 408 2246 2246 0 0

BR 9 MT 1 10,710,378,765 10,710,378,765 579 579 3188 3188 0 0

BK 1.2 MT 1 6,567,911,642 6,567,911,642 355 355 1955 1955 0 0

BR4 MT 2 5,739,509,320 5,739,509,320 408 408 0 0 2451 2451

BR 9 MT 2 8,145,910,858 8,145,910,858 579 579 0 0 3478 3478

BK 1.2 MT 2 4,995,306,322 4,995,306,322 355 355 0 0 2133 2133

BR4 MT 3 5,739,509,320 4,773,910,527 408 339 0 0 2451 2038

BR 9 MT 3 8,145,910,858 7,160,865,790 579 509 0 0 3478 3058

BK 1.2 MT 3 1,193,477,632 3,580,432,895 84 254 0 0 509 1529

Total 58,784,316,810 59,220,628,212 3,761 3,792 7,391 7,391 14,502 14,689

Deviation 436,311,402 31 0,00 186

From the calculation above, it can be seen that after optimization, the profit increases with the increase in the area of irrigated irrigation by 42.76 ha or increases the profit by IDR 600,747,014.08 at 97% discharge (Table 6). After optimization, the profit increases with the increase in the area of irrigated irrigation by 163,63 ha or increases by IDR 2,298,886,553.28 at 75% discharge (Table 7). Meanwhile, at 51% discharge, the Irrigated Land area increased by 218.64 ha, and the profit increased by 3,071,821,627.55 (Table 8). At 26% discharge optimization increase in the area of irrigated by 31.06 ha or an increase in profit by IDR 436,311,402.22 (Table 9).

After optimization, the profit increased by increasing the area of the irrigated area by 42.76 ha or by IDR 600,747,014.08 at 97% discharge, increasing the area of irrigated irrigation by 163.63 ha or increasing profits and amounting to 2,298,886,553.28 at 75% discharge, increasing the area of irrigated area by 218.64 ha or increasing profits by 3,071,821,627.55 at 51% discharge, increasing the area of irrigated.

After optimization, profits increase as the irrigation area increases [19]. This can be caused by several factors, including (a) Increased Production: As the area of irrigation increases, the amount of land that can be cultivated will increase [20].

This will enable increased production of the crops or

commodities produced. Thus, the profits obtained from selling production products will also increase, (b) Economies of Scale: In terms of economies of scale, the greater the area of irrigation owned, the more efficient the use of resources such as water, fertilizer, and labor. With higher efficiency, production costs can be reduced to increase profits, (c) Crop Diversification: Farmers can diversify crops with larger irrigated areas. Crop diversification can increase farmers' income because they can take advantage of different market opportunities and reduce the risk of crop failure, (d) Labor Absorption: With the increase in irrigation area, there will be an increase in labor absorption in the agricultural sector. This can help reduce the unemployment rate and increase the income of people in the area.

4. Conclusion

Due to the low cropping intensity, the optimization process is necessary. By using land area, reservoir storage capacity, and cropping patterns data which are the same as the existing conditions, optimization in this study is carried out by determining the allocation of water supply, which is the water is given according to the land water necessary and will be given to each plot (area) until it fulfills the maximum

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area. The required discharge is the correlation between available volume divided by time, and the land area is the ratio of available volume divided by required volume. This correlation will obtain a discharge for each plot (area) to fulfill the maximum area. In comparing profits before optimization / existing and after optimization, it can be seen that there is an increase in the intensity of irrigation planting. There is an increase in irrigation intensity in dry years by 3%, in low years by 12%, in normal years by 16%, and in inadequate years by 2%. The increase in irrigation intensity has significant implications for crop production and the overall agricultural development in the area. By providing the necessary water supply to fulfill the maximum area, farmers can cultivate a larger portion of their land, leading to increased crop yields and potentially higher profits. Moreover, the optimization process allows for a more efficient use of available water resources, ensuring that the water needs of agricultural land are met in various seasons. It is important to note that the study's findings are based on the analysis of data related to land area, reservoir storage capacity, and cropping patterns.

A deterministic dynamic program for optimization analysis enables a systematic approach to address the complex water distribution problem. By considering these factors and implementing the recommended strategies, related institutions can play a crucial role in improving water management practices, enhancing agricultural productivity, and promoting sustainable agricultural development in the Raknamo Irrigation area.

Acknowledgments

The author would like to thank Balai Wilayah Sungai Nusa Tenggara II for providing data information for this research.

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