Geospatial Analysis of Abandoned Lands Based on Agroecosystems: The Distribution and Land Suitability for Agricultural Land Development in Indonesia
1
Indonesian Center for Agricultural Land Resources Research and Development, Jakarta 12540, Indonesia
2
Department of Soil Science and Land Resources, IPB University, Bogor 16680, Indonesia
3
Research Center for Geospatial, National Research and Innovation Agency, Jakarta Pusat 10340, Indonesia
*
Authors to whom correspondence should be addressed.
Land 2022, 11(11), 2071; https://doi.org/10.3390/land11112071
Submission received: 7 October 2022
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Revised: 9 November 2022
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Accepted: 16 November 2022
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Published: 17 November 2022
(This article belongs to the Topic Earth Observation-Based Ecosystem Services in Support of Planet SDGs)
Abstract
:The Indonesian land area is 191.1 million ha, part of which is abandoned land in various agroecosystems that have the potential for expanding the agricultural area. The purpose of this research was to geospatially analyze abandoned land based on its agroecosystem at the national and district levels, as well as to evaluate the land suitability of the land for expanding agricultural development. The methods included: (1) geospatial analysis of the national land cover map at a scale of 1:250,000 combined with soil and climate information to identify abandoned land and examine its agroecosystem, (2) selecting representative districts in each agroecosystem for visual interpretation using high-resolution imagery, i.e., SPOT 6/7, (3) assessing the land suitability of abandoned land for agricultural development at the national and district levels, and (4) predicting national abandoned land and its land suitability. The essential finding is the identification of abandoned land at around 42.6 million ha in Indonesia distributed over six agroecosystems, with the widest being in dry lowland and wet climates. Then, 54 districts were selected to characterize abandoned land by using SPOT 6/7 high-resolution imagery and were interpreted visually. It was found that the abandoned land covered approximately 16.9 million ha. The distribution of abandoned land from the interpretation of satellite imagery was smaller than that of geospatial analysis due to differences in the map scale and the use of ancillary data. The identification of abandoned land from high-resolution imagery should be carried out for all regions of Indonesia to accurately map the distribution of the abandoned land and characterize the properties. However, it requires a large amount of time, cost, and facilities to complete the inventory. The geospatial analysis that combined imageries and ancillary data identified 27.7 million ha of abandoned land suitable for expanding the agricultural area. The largest suitable abandoned land for the purpose was found in the lowlands with a wet climate, especially in Papua, Kalimantan, and Sumatra islands. The identified suitable abandoned land of 54 districts differed by scale, in which it was 11.2 million ha at the scale of 1:250,000 and 8.5 million ha at the scale of 1:50,000, respectively. The potential land expansion for food crops, particularly paddy fields, was only 2.2 million ha, located in mineral swamp land, which was predominantly located in Papua, with inadequate accessibility. Expanding paddy fields for national food security in the future would be constrained by less suitable land resources, while the near future challenge is the competition of land allocation for agricultural and non-agricultural sectors, as well as for food crops and plantations.
1. Introduction
Food sufficiency has been challenged by various aspects of production and its distribution. Many factors are involved in heightening the issue, including population growth, conflicts and climate change that lead to drought or floods, and uneven management capacity. The challenges have been faced variably by almost all countries, including Indonesia, which comprises 191.7 million ha of land mass spreading over 16,056 islands and had a population of 270.2 million in 2020 [1]. The quality of Indonesian land resources varies in terms of soils, parent materials, physiography, altitudes, landforms, and climate characteristics that necessitate grouping into several agroecosystems [2,3], including swamps and non-swamps, mineral or peat soils, lowlands or highlands, wet climates, and dry climates [4,5,6]. The diversity of land and climatic conditions has a positive value for the development of agriculture, i.e., food and horticultural and plantation commodities. Around 83.6 million ha (43.6% of the total area) have been used for the agricultural sector: 7.4 million ha for rice fields, 16.2 million ha for moor and farmlands, 17.8 million ha for plantations, a mix plantation for 21.7 million ha, and 20.2 million ha as abandoned lands [7]. These agricultural lands were not sufficient to support all national food demand; thus, imports of several commodities such as wheat, soybeans, garlic, and sugar were undertaken. Accurate figures, however, need to be further developed within the context of spatial data. This allows for more accurate spatial-aware data for many purposes, including agricultural planning. In terms of the unproductive utilization of abandoned lands, accurate data need to be generated as a basis of the expansion of food production areas.
Mapping the distribution of biophysically suitable land is a necessity for the preparation of the extension. Increasing Indonesian population at a rate of 1.31%/year or about 3.5 million people/year [1] reinforces the need for additional food production areas. It is estimated that the population will reach 320 million in 2045. On the other hand, the increase in cropland is somewhat insignificant [8]; thus, upholding the rest of production areas is imperative.
Meanwhile, maintaining agricultural land for food production is challenged by land conversion at an alarming rate. For instance, paddy fields were converted at around 110,160 ha/year, of which around 75% of Java rice fields have shifted to housing [9]. It was approximately 96,500 ha/year land converted within the period of 2000–2012 [10]. This was not only for housing; agricultural areas were also altered into facilities. For example, paddy fields were converted into airports located mainly in the peri-urban region of the West Java and Yogyakarta provinces at around 7500 ha and 4278 ha, respectively [11]. The production capacity of paddy fields would decrease if the land conversion keeps its rate at around 90,000 ha/year, in which Indonesia needs about 25.9 million tons of additional rice by 2045 [12]. The trend of rice field conversion occurs not only in Indonesia but also in other countries such as China [13,14,15]. Economic, political, and government policy at the national, provincial, and district levels have influenced the rate and direction of land use conversion [16]. To control rice-field conversion, policies and regulations were formed and enacted, such as Law No. 41 of 2009 regarding the Protection of Agricultural Land for Food Sustainability. The law has subsequently been supported by the issuing of the Presidential Regulation No. 59 of 2019 regarding controlling land use change. The objective of the Presidential Regulation includes accelerating the provision of the protected paddy fields map, controlling land conversion, empowering farmers not to convert land, and providing data on paddy fields for determining sustainable food production land.
Another challenge of maintaining a food production area is related to various problems from inherent land resources such as erosion, degradation [17], and decreasing soil quality. Moreover, external disturbances such as global climate change, which is indicated by the increasing frequency of floods and drought [18,19], seawater intrusion and sea level rise [20], may also be well known. Yet, the impact of global warming on the agricultural sector varies greatly, depending on climate, technology, and adaptive capacity [21].
Land fragmentation may hinder the sustainability of cropland. Rice field ownership can be fragmented due to inheritance, thereby increasing the number of smallholder farmers with less than 0.5 ha of rice field ownership [22]. Fragmentation is problematic, not only in Indonesia but also in other countries such as in China, which drives the increasing number of abandoned lands [23], and it was also discovered in Slovakia [24]. This challenge leads to a threat to national food security. Therefore, increasing production cannot rely solely on agricultural intensification but needs to be accompanied by extensification, including the utilization of suited abandoned lands (AL). The abandoned land is distributed in various agroecosystems of almost all Indonesian islands. A previous study based on the reconnaissance scale estimated that there were about 34.6 million hectares AL; nonetheless, none of detail scales have been available yet. Recently, the land suitability for various crops is existing at a scale of 1:50.000; thus, the identification of AL at this scale could possibly be accomplished. Considering the vast coverage of Indonesian land at about 191.7 millions hectare, geospatial analysis, which employs archived data and maps, is required to estimate AL. Consequently, it is necessary to further identify the position, distribution, and suitability of AL over various agroecosystems. Differentiating AL based on its agroecosystem would ease and direct recommendations regarding the use of AL, whether in the peat or mineral soils. By implementing geospatial analysis by utilizing available land resource data, this research explored the possibility of such methods in mapping the distribution of AL in Indonesia, following previous studies [25,26,27]. This paper presents the results of a geospatial analysis of AL at the national level from available data, visually identifying AL in six primary agroecosystems using high-resolution satellite imagery at the district level, as well as assessing the suitability of these AL for expanding the possibility of future agricultural fields, especially food-related commodities.
2. Materials and Methods
2.1. Definition of Abandoned Land
Abandoned land has started from the excessive exploitation of natural resources for various purposes [28], including for shifting agriculture [29], for plantation development [30], for transmigration programs [31], and others. The slash-and-burn process [32] to clear forests is intended for shifting cultivation or for the development of plantations, since this is economically inexpensive and simple to operate [33]. However, the deforested lands are sometimes sub-optimally used; thus, prolonged AL are formed.
Most of AL is initially forested land; after logging and burning litter, the land has predominately been grown by reeds and shrubs [28]. The land can be defined as biophysically unutilized land for any activities covered by groves (secondary), shrubs, grass, grasslands, or open land [34]. Meanwhile, based on Indonesian Government Regulation Number 11 2010 regarding the Control and Utilization of AL, which was followed up by Regulation of the Head of the National Land Agency of the Republic of Indonesia Number 4 2010 concerning Procedures for Controlling Abandoned/AL. AL is defined as land that is being uncultivated, unused, or unutilized in accordance with the purpose of granting rights. Another note regarding AL is that the land should be covered by grasslands and shrubs within three to five years after being unemployed [35]. Visockiene et al. [36] affirm that the minimum duration of abandoned agricultural land was at least 3 years. Moreover, the likelihood of land abandonment was higher in dry land agroecosystems [37].
AL are widely discussed in many countries and analyzed geospatially by using various imageries with varying coverage at the national and regional levels. The utilization of satellite imagery to identify AL has been performed in various countries such as Albania and Romania [38], Carpathian Police—Poland [39], Romania [40], Kazakhstan [41], China [42,43], and Lithuania [36].
2.2. Research Area
The geospatial analysis of AL at the national level was carried out for the whole coverage of Indonesia. AL has been analyzed by using visual analysis from SPOT 6/7 satellite imagery from 2016–2021 in 54 districts representing major islands and agroecosystems such as dry lowland of wet climate (LWC), dryland of low-land and dry climate (LDC), dryland of highland and wet climate (HWC), dryland of highland and dry climate (HDC), mineral swamp land (MS), and peat swamp land (PS) (Figure 1). Wet climate predominately covers the western part of Indonesia, and the dry climate is predominately in the eastern part of Indonesia, while mineral and peat swamps are predominately in Sumatra, Kalimantan, and Papua Islands.
2.3. Data
The data for the geospatial analysis of the distribution of AL at the national level for the year 2019 were land cover maps of the Ministry of Environment and Forestry at 1:250,000, a rice fields map of the National Land Agency at 1:25,000, an oil palm plantation map of the Coordinating Ministry for Economic Affairs Republic of Indonesia at 1:50,000, a cocoa plantation map at 1:50,000 [44], a coconut plantation map at 1:50,000 [45], a soil database of ICALRRD at 1:50,000, a land suitability database of ICALRRD at 1:50,000, a climate map, a peatland map at 1:50,000 [46], and a swampland map at 1: 5000 [47].
SPOT 6/7 Mosaic Satellite imageries from the National Institute of Aeronautics and Space (LAPAN 2019/2020) were utilized to classify the land use of rice fields, plantations, and infrastructure (settlements, industrial areas, and others) which were later removed from potential AL. Soil and land suitability databases were used to evaluate the suitability of AL for agricultural development. Visual interpretations were performed using ESRI ArcGIS 10.8 and Global Mapper 21. In addition, global virtual Google Earth PRO was employed to support the image interpretation.
2.4. Methods
2.4.1. Identification of AL at the National Level
Prior to the identification of AL, a few land covers such as rice fields, plantations, settlements, and forests were excluded based on official publications of recent land cover, as mentioned previously. The output was further overlapped with soil, climate, swamp, and peat maps to obtain the distribution of AL based on the agroecosystem such as dry lowland and wet climate (LWC), dry lowland and dry climate (LDC), dry highland and wet climate (HWC), dry highland and dry climate (HDC), mineral swamp land (MS), and peat swamp land (PS). The segregation of AL based on the agroecosystem was used to select representative districts for further analysis (Figure 2). In addition, the specific agroecosystem would assist in prioritizing AL utilization for expanding agricultural land.
2.4.2. Identification of AL at the District Level
Several districts were selected to represent six agroecosystems (Table 1). High resolution mosaic satellite imageries of SPOT 6/7 were used for detail identification and filtering land uses for selected districts. The procedure of AL identification at the district level resembles the one at the national level with smaller areas. Delineating the boundary for visual image interpretation uses the ArcGIS 10.8 tool and works up to a scale of 1: 5000.
The characteristics of AL from the image were identified by the appearance of land cover, color, shape, size and height, shadow, pattern, texture, and context [48]. Visual interpretation was carried out manually, combined with the ground check. The generated map was the distribution of AL in selected districts that represent the agroecosystem. The map was then overlaid with the land suitability database produced by ICALRRD.
Field check has been carried out in all representative agroecosystems (Table 1) distributed in 28 districts to verify the accuracy of the image interpretation and to discuss with the Assessment Institute for Agricultural Technology (AIAT), related agencies, and Bappeda to obtain additional information regarding the regions.
2.4.3. Land Suitability Evaluation
The evaluation of land suitability was carried out by matching land characteristics with criteria of suitability for specific uses, employing soil databases at the semi-detailed scale of 1:50,000. The soil database consisted of several land characteristics (including soil depth, texture, and coarse materials), chemical properties (pH, CEC, organic C, P and K contents), slope, climate, elevation, and land typology. The evaluation was performed using the Land Evaluation Assessment System software for several agricultural commodities, following the technical guidelines of the Indonesian Center for Agricultural Land Resources Research and Development (ICALRRD) [49]. The suitability evaluation was for various commodities of annual and perennial crops on AL. The slope was classified into <15%, as recommended for food crops or annual crops, a slope of 15–40% was used for perennial crops, and a slope of >40% was not recommended for agricultural development (Figure 3). This suitability assessment was carried out at both the national and district levels of AL.
2.4.4. Land Suitability Estimation Based on Visual Interpretation Results
The distribution of suitable AL for agricultural land at a scale of 1:250,000 (national level) may differ from that at a scale of 1:50,000 (district level) by visual interpretation. The identified AL from SPOT imageries that were evaluated for their land suitability were used for predicting land suitability at the national level by using this equation:
where:
PSAi = ∑ (VIa123456/LCA a123456 × LC)
- PSA = Prediction of national AL suitability (ha)
- VI = Area of AL suitability from visual interpretation (ha)
- LCA = Area of AL suitability based on agroecosystem (ha)
- LC = Area of total AL suitability (ha)
- i = Agroecosystem class, which consists of:
- a1 = Agroecosystem of lowland wet climate
- a2 = Agroecosystem of lowland dry climate
- a3 = Agroecosystem of highland wet climate
- a4 = Agroecosystem of highland dry climate
- a5 = Agroecosystem of peat swamps
- a6 = Agroecosystem of mineral swamps.
3. Results
3.1. Geospatial Analysis of AL at the National Level
The distribution of AL was around 42.6 million ha (Table 2). The AL was further classified into lowland (<700 m asl) and highland (>700 m asl), wet climate with annual rainfall >2000 mm and dry climate <2000 mm [50], and mineral swamps [47] or peat swamp [46]. The largest AL was dry lowland and wet climate, covering of 24.9 million ha (58.4%) spread over almost all parts of Indonesia, especially in Kalimantan and Papua. Meanwhile, the largest AL for dryland and dry climates was found in Bali, West Nusa Tenggara, and East Nusa Tenggara, covering an area of 3.4 million ha.
The AL at mineral swamps was found to be about 5.5%, with the largest area found in Papua, Central Kalimantan, West Kalimantan, and South Sumatra. The largest AL of peat swamp was found in Central Kalimantan and Papua, covering about 0.2 million ha. The total peatland in Indonesia was around 13.4 million ha [51], and approximately 0.46 million ha or 1.1% of the total peatland were abandoned (Table 2). This shows that the widest AL was outside Java Island.
3.2. Identification of AL at the District Level
The identification of AL in 54 districts covered 22 regencies for dryland of lowland at wet climate, 17 regencies for dryland of lowland at dry climate, 5 regencies for dryland of highland at wet climate, 2 districts for dryland of highland at dry climate, 4 districts for peat swamp land, and 4 districts for mineral swamp land.
Visual interpretation was feasible to use for the identification of an object, with an accuracy at 91% compared to fixed classification (82%) and machine learning (85%) [52]. The experiences and skill of the interpreter may increase the accuracy of visual interpretation [48]. Figure 4 presents an example of the visual interpretation of AL representing each agroecosystem. Secondary forest gives almost the same visual appearance in all agroecosystems with a large size, irregular shape, generally dark-green color, and random pattern. The differences between shrubs and grasslands are mainly reddish-green or brownish in color for dry climate areas.
Figure 5 and Figure 6 illustrate the visual appearance of SPOT 6 mosaic images for shrubs viewed at a scale of 1:50,000 and at a scale of 1:5000. The appearance of shrubs looks light-green with a slightly rough texture at a scale of 1:50,000, while it looks slightly brownish green with a smoother texture at a scale of 1:5000 (Figure 5). In Figure 6, with a scale of 1:5000, the color of the shrubs looks reddish, and bare soil is dominant with a coarser texture. The location affects differing appearances for the same land cover as shrubs.
The AL is spread over various agroecosystems with varying soil and climatic characteristics (Table 3). In wet climates (rainfall > 2000 mm/year), Ultisols generally have an acidic pH and a low cation exchange capacity (CEC) and base saturation (BS) [53,54] due to the advanced weathering and leaching of bases [55]. Meanwhile, Hapludands soil from volcanic material has a neutral pH and a high cation exchange capacity and base saturation, despite the high rainfall of 3034 mm/year. In tidal swamps, both peat and mineral swamps have a low base saturation and pH (acidic) and a high CEC, with rainfall of more than 2000 mm/year.
3.3. The Comparison of AL
The results showed that AL was about 42.6 million ha nationally, and it was about 18.1 million ha in 54 selected districts/cities. The identified AL in the 54 districts/cities was larger than the result of visual interpretation, which was 16.9 million ha (Table 4). This was due to the different scales of databases being used at the national level (1:250,000) and district level (1:50,000). Landsat TM images were used at a scale of 1:250,000, while, for a scale of 1:50,000, we used SPOT 6/7.
Nonetheless, according to Hamylton et al. [52], visual interpretation may be used for the large coverage area of Indonesia for various purposes and various images such as land cover maps by the Ministry of Forestry and Environment, land use and paddy field maps produced by the National Land Agency of Indonesia, and the distribution of agricultural land produced by ICALRRD.
3.4. Land Suitability Evaluation for AL
The land suitability evaluation for 42.6 million ha of AL throughout Indonesia (Table 2) confirmed that the suitable land for agricultural development was around 27.7 million ha (63.9%), as shown in Table 5. The percentage of suitable land on AL at various agroecosystems ranged from 24.3% to 88.2%. The lowest percentage was found in upland agroecosystems in both wet (24.3%) and dry (39.3%) climates, while the highest percentage was found in mineral swamp agroecosystems, especially for food crops. The low percentage of suitable land in the highlands was generally due to the highland area being a montane with slopes > 40% and annual rainfall of more than 3000 mm. Therefore, the land was considered unsuitable for agricultural development. In the dry climates such as West Nusa Tenggara and East Nusa Tenggara, the limitation of suitability was due to shallow and rocky solum [4,56].
Figure 7 presents the distribution of AL at the national level throughout Indonesia and at North Sumatra Province, which has six agroecosystems. The acreage of AL at North Sumatra Province is about 1.4 million ha, and 0.7 million ha are suitable for agricultural development. At the district level, a sample of AL identification and its land suitability evaluation for agriculture was at the Humbang Hasundutan District of North Sumatra. There were three suitable agroecosystems for the agricultural area, namely, highland peat around Lake Toba (0.1%), dryland of lowland with wet climate (35.3%) and dryland of highland with wet climate (64.6%). This figure shows that the suitable AL is smaller than the total AL.
3.5. Prediction of AL Suitability at the National Level
The suitable land for agricultural commodities of AL was around 27.4 million ha at the national level, located in 511 districts/cities. For 54 district/cities, it was estimated that around 11.2 million ha or 40.9% was suitable for agricultural development. It was found that there was about 8.5 million ha suitable for agriculture (Table 5) from the total estimated AL of 16.9 million ha (Table 3). Assuming that all districts were visually interpreted, it was predicted that the suitable land for agriculture at the AL was around 20.8 million ha or 75.8% of each district (Table 6). It seems that there is less suitable land for expanding agricultural areas to warrant national food security. A case study of Kubu Raya Regency, West Kalimantan Province showed that the suitable AL for agriculture covered 55,936 ha; most of those were owned by private companies and individuals, so around 9668 ha were available for agricultural land reserve [57].
3.6. Recommendation of Agricultural Development
Based on the land suitability assessment, it was shown that the suitable land for agricultural expansion was around 27.7 million ha (Table 4), which is slightly bigger than the result of image interpretation, i.e., 20.7 million ha. It seems that the suitable AL for agricultural development was smaller compared to that of the previous study (around 34.6 million ha [5]) that was identified by using databases at the scale of 1:250,000. Increasing the scale of soil databases up to 1:50,000 reduced the acreage of suitable land by 32.3 million ha [58].
Table 5 showed that the largest AL suitable for the agricultural area was found in dryland with wet climate for about 11.9 million ha, followed by dryland with dry climate for 4.6 million ha, and mineral swamp land for 1.6 million ha. The smallest was in peat swamp land for only 0.7 million ha. In Indonesia, food crops, especially rice, are mainly produced in irrigated land, which covers around 1.5% of dryland [1]. If the AL is prioritized for expanding paddy fields, the recommended wetland area was only 1.6 million ha, which was mainly located in Papua, with limited accessibility. It was estimated that, when land conversion and rice consumption per capita per year are in their current states, by 2045, there will be a shortage of 25.9 million tons of dried rice milled grain [12]. Thus, land expansion for food production is required as the rate of population growth and food increases.
The competition of uses between agricultural and non-agricultural sectors will be intensified in the near future. Oil palm plantations have grown rapidly since 1990 and have reached 16.4 million ha, while paddy fields producing staple food are about 7.4 million ha [7]. Based on a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of AL in each agroecosystem (Table 7), recommendations for future agricultural developments to maintain national food security are as follows: (1) optimizing the existing agricultural land by increasing the productivity and cropping index, (2) reducing the rate of land conversion, (3) expanding the agricultural area in accordance with the result of Table 7 (for instance, creating paddy fields on wetlands), (4) prioritizing specific land use, (5) preparing the allocation and distribution of land reserves, (6) constructing policies and regulations that support sustainable national food security programs.
The utilization of AL for expanding the agricultural land should consider land ownership, forest status, and accessibility. Agrarian Law No 5/1960 of Indonesia differentiates private and corporate ownership. There are rights to cultivate, rights to build, rights to use, or rights to manage for corporations. In general, private AL is small and may be located separately, while the AL of corporations usually covers wider fields [57]. In addition, about 29.5% of 55.936 ha of AL are suitable and located at a production forest or converted forest. Land clearance is required from the forest to be converted into cultivated land. Moreover, accessibility is an essential aspect, in which most of AL is located remotely and is less accessible, either on peat or on mineral lands. Utilizing those lands requires substantial investment to build infrastructure. Thus, comprehensive evaluation considering physical, economical, land status-related, or other institutional aspects should be carried out before agricultural expansion.
4. Conclusions
This study identified AL by employing a base map at a 1:250,000 scale for about 42.6 million ha located in six agroecosystems or about 18.1 million ha located in 54 selected districts. The largest AL was found in the dryland of lowlands with wet climate, and the least was in peat swamps.
The identification of AL at the district level through the visual interpretation of SPOT 6/7 satellite imageries combined with ancillary data at 1:50,000 has been conducted in 54 selected districts representing six agroecosystem, indicating that the identified AL was smaller than that at the 1:250,000 scale, at about 16.9 million ha. The differences relate to the differing scales of the base map and the type of imageries and auxiliaries being used.
The acreage of AL suitable for the expansion of agricultural was about 27.7 million ha out of 42.6 million ha and was mostly in the dryland located both in the lowlands and highlands. There was about 91.2% combined AL located both in dry lands with wet climate and dry land with dry climate, while the rest was in mineral swamps at about 2.2 million ha and in peat swamps at about 0.3 million ha.
The suitable AL resulting from image interpretation at a scale of 1:50,000 and a land cover at a scale of 1:250,000 in 54 districts were about 8.5 million ha and 11.2 million ha, respectively. The identification of AL using high-resolution imagery should ideally be carried out for all parts of Indonesia, but it absorbs substantial time, labor, and supporting facilities. The suitable AL is estimated to be proportional between the suitable abandoned land by visual interpretation (scale of 50,000) and the suitable abandoned land by geospatial analysis (scale of 250,000). The estimation of abandoned land by employing high-resolution imageries found about 20.7 million ha, which was smaller than other estimations employing a database at a scale of 1:250,000 for about 27.7 million ha.
The suitability evaluation was based on biophysical properties and disregarded land ownership and legal status. If those variables were considered, the area of AL for agriculture expansion would likely be smaller. These variables need to be considered for further study to identify available land as reserves for the expanding agricultural area.
The expanding agricultural area from AL should consider land suitability, land tenure, forest status, and accessibility. It is recommended to utilize suitable wetlands for food crops, especially for paddy fields. Moreover, prioritizing land for food production is imperative to achieving food security, and it needs the support of applicable policies and regulations.
Author Contributions
Each author (A.M., B.M., B.B., D.R.P. and H.) had an equal role as main contributors who equally discussed the conceptual ideas and the outline, designed and collected the data, developed the methodology, analyzed the data, prepared the initial draft, provided critical feedback, engaged in editorial oversight, and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the ICALRRD, Indonesian Agency for Agricultural Research Development, Ministry of Agriculture, through the research program “Analysis of land resource management policies to support agriculture in 2016–2020” and “Analysis of the distribution of agricultural land in 2020–2021”.
Data Availability Statement
Not applicable.
Acknowledgments
The authors would like to thank the head of ICALRRD for the funding that supports this research implementation. This endeavor would not have been possible without the assistance of researchers and technicians within the ICALRRD during the data collection and processing.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
The coverage of geospatial analysis to identify AL in Indonesia (yellow) and the coverage of AL in 54 districts (green).
Figure 1.
The coverage of geospatial analysis to identify AL in Indonesia (yellow) and the coverage of AL in 54 districts (green).
Figure 2.
The procedure to identify AL at the national and district levels with the visual interpretation of SPOT 6/7.
Figure 2.
The procedure to identify AL at the national and district levels with the visual interpretation of SPOT 6/7.
Figure 3.
The procedure to generate land suitability maps for the agricultural development in AL.
Figure 4.
The characteristics of elements for the visual interpretation of AL (secondary forest, shrubs, and grasslands) in representative agroecosystem.
Figure 4.
The characteristics of elements for the visual interpretation of AL (secondary forest, shrubs, and grasslands) in representative agroecosystem.
Figure 5.
Path profile of global mapper (a,b) (elevation), Mosaic SPOT 6 at a scale of 50,000 (c), bush-shrub at a scale of 5000 (d). Location in Humbang Hasundutan Regency, North Sumatera (Toba Lake), elevation of > 1400 m above sea level, annual rainfall >2000 mm (wet climate).
Figure 5.
Path profile of global mapper (a,b) (elevation), Mosaic SPOT 6 at a scale of 50,000 (c), bush-shrub at a scale of 5000 (d). Location in Humbang Hasundutan Regency, North Sumatera (Toba Lake), elevation of > 1400 m above sea level, annual rainfall >2000 mm (wet climate).
Figure 6.
Path profile of global mapper (a,b), Mosaic SPOT 6 at a scale of 50,000 (c), bush-shrub at a scale of 5000 (d). Location in Kupang Regency, East Nusa Tenggara, elevation between 0 and 500 m above sea level, annual rainfall <1500 mm (dry climate).
Figure 6.
Path profile of global mapper (a,b), Mosaic SPOT 6 at a scale of 50,000 (c), bush-shrub at a scale of 5000 (d). Location in Kupang Regency, East Nusa Tenggara, elevation between 0 and 500 m above sea level, annual rainfall <1500 mm (dry climate).
Figure 7.
The distribution of AL at the national level and samples of AL at the provincial level (North Sumatra) for six agroecosystems, generated map from the visual image interpretation at Humbang Hasundutan District, and AL suitability for agricultural food crops and annual crops.
Figure 7.
The distribution of AL at the national level and samples of AL at the provincial level (North Sumatra) for six agroecosystems, generated map from the visual image interpretation at Humbang Hasundutan District, and AL suitability for agricultural food crops and annual crops.
Table 1.
The district samples for the visual interpretation and ground check of AL.
| Province | District | Agroecosystem | Visual Interpretation | Ground Check | Year |
|---|---|---|---|---|---|
| Aceh | Gayo Luwes | HDC | + | − | 2020 |
| Aceh Barat | LWC | + | + | 2018 | |
| North Sumatera | Humbanghasundutan | HWC | + | + | 2020 |
| Padang Lawas Utara | LWC | + | + | 2022 | |
| Riau | Pelalawan | PS | + | − | 2019 |
| Siak | PS | + | + | 2017 | |
| West Sumatera | Sijunjung | LWC | + | − | 2021 |
| Bengkulu | Muko-muko | LWC | + | + | 2017 |
| Kaur | HWC | + | − | 2021 | |
| South Sumatera | Ogan Komering Ilir | PS | + | + | 2021 |
| Lampung | Way Kanan | LWC | + | − | 2021 |
| Riau Island | Lingga | LWC | + | − | 2021 |
| Jambi | Tanjung Jabung Timur | MS | + | + | 2016 |
| Kerinci | HWC | + | − | 2021 | |
| Babel Island | Bangka Selatan | LWC | + | − | 2021 |
| West Kalimantan | Kapuas Hulu | MS | + | − | 2021 |
| Ketapang | LWC | + | − | 2021 | |
| Kuburaya | PS | + | + | 2017 | |
| Central Kalimantan | Katingan | LWC | + | − | 2021 |
| Barito Selatan | LWC | + | − | 2020 | |
| Pulang Pisau | MS | + | + | 2017 | |
| South Kalimantan | Tanah Bumbu | LWC | + | − | 2021 |
| Kota Baru | LDC | + | − | 2021 | |
| East Kalimantan | Berau | LWC | + | + | 2017 |
| Paser | LWC | + | + | 2017 | |
| Kutai Kertanegara | LWC | + | − | 2021 | |
| Kutai Timur | LWC | + | + | 2017 | |
| North Kalimantan | Malinau | LWC | + | + | 2018 |
| Bulungan | LWC | + | − | 2021 | |
| South Sulawesi | Gowa | LDC | + | + | 2018 |
| Luwu Utara | HWC | + | + | 2020 | |
| West Sulawesi | Mamuju | HWC | + | − | 2019 |
| Central Sulawesi | Buol | LDC | + | + | 2018 |
| Morowali Utara | LWC | + | − | 2021 | |
| Gorontalo | Gorontalo | LDC | + | + | 2018 |
| Boalemo | LDC | + | − | 2021 | |
| North Sulawesi | Bolaang Mongondow | LDC | + | − | 2021 |
| South-east Sulawesi | Buton | LWC | + | + | 2019 |
| Konawe Selatan | LDC | + | − | 2020 | |
| West Nusa Tenggara | Sumbawa Barat | LDC | + | + | 2021 |
| Bima | LDC | + | + | 2019 | |
| East Nusa Tenggara | Manggarai Barat | LDC | + | + | 2018 |
| Flores Timur | LDC | + | + | 2019 | |
| Kupang | LDC | + | + | 2018 | |
| Timor Tengah Selatan | HDC | + | + | 2018 | |
| Maluku | Buru Island | LDC | + | + | 2018 |
| Aru Island | LDC | + | − | 2021 | |
| North Maluku | Tidore | LDC | + | + | 2019 |
| Halmahera Timur | LDC | + | − | 2021 | |
| West Papua | Sorong | LWC | + | + | 2019 |
| Fakfak Barat | HWC | + | − | 2021 | |
| Papua | Nabire | LWC | + | + | 2019 |
| Merauke | MS | + | − | 2021 | |
| Mappi | LWC | + | − | 2021 |
Notes: + = Visual interpretation and ground check, − = No ground check. LWC = dry lowland and wet climate, LDC = dry lowland and dry climate, HWC = dry highland and wet climate, HDC = dry highland and dry climate, PS = peat swamps, MS = mineral swamps.
Table 2.
Distribution of AL based on six agroecosystems in Indonesia.
| Province | Distribution of AL Based on Agroecosystem | Total Area (ha) | |||||
|---|---|---|---|---|---|---|---|
| Swamp Land | Dry Land | ||||||
| PS | MS | LWC | LDC | HWC | HDC | ||
| Aceh | 15 | 27,073 | 33,917 | 292,793 | 109,532 | 352,370 | 815,700 |
| Sumatera Utara | 9168 | 16,404 | 587,445 | 240,887 | 398,789 | 136,825 | 1,389,520 |
| Sumatera Barat | 2227 | 10,887 | 965,079 | 2478 | 337,101 | 3760 | 1,321,532 |
| Riau | 22,603 | 43,773 | 304,119 | 21,757 | - | - | 392,252 |
| Jambi | 748 | 33,650 | 704,557 | 157,955 | 94,303 | 1247 | 992,459 |
| Bengkulu | - | 179 | 161,496 | - | 61,501 | - | 223,176 |
| Sumatera Selatan | 6348 | 172,230 | 359,307 | - | 72,525 | - | 610,411 |
| Kep Bangka Belitung | 6253 | 39,788 | 330,342 | - | - | - | 376,382 |
| Kep Riau | 2 | 416 | 130,881 | - | 1264 | - | 132,563 |
| Lampung | 495 | 19,909 | 146,567 | 37,836 | 28,101 | 6 | 232,914 |
| Banten | - | 5095 | 44,046 | 19,517 | - | - | 68,658 |
| Jawa Barat | - | 527 | 60,152 | 4243 | 113,582 | 3274 | 181,779 |
| Jawa Tengah | - | 911 | 12,922 | 10,881 | 26,235 | 2564 | 53,514 |
| Yogyakarta | - | 34 | 288 | 1085 | - | 1407 | |
| Jawa Timur | - | 3.202 | 23,680 | 139,866 | 46,829 | 68,884 | 282.460 |
| Bali | - | 6 | 28,376 | 36,251 | 13,600 | 6066 | 84,299 |
| Nusa Tenggara Barat | - | 1402 | 5333 | 628,153 | 24,716 | 94,057 | 753,662 |
| Nusa Tenggara Timur | - | 2513 | 55,588 | 1,639,787 | 24,649 | 876,945 | 2,599,481 |
| Kalimantan Barat | 4717 | 128,731 | 2,660,869 | 65,869 | 18,431 | - | 2,878,616 |
| Kalimantan Selatan | 54 | 37,589 | 666,186 | 288,805 | 39,715 | - | 1,032,349 |
| Kalimantan Tengah | 224,143 | 297,591 | 4,648,902 | 604,587 | - | - | 5,775,223 |
| Kalimantan Timur | 6281 | 116,678 | 3,378,715 | 13,060 | 147,434 | - | 3,662,169 |
| Kalimantan Utara | - | 37,260 | 1,495,291 | 35,019 | 32,885 | - | 1,600,454 |
| Gorontalo | - | 395 | 61,839 | 117,367 | 1636 | 25,558 | 206,795 |
| Sulawesi Utara | - | 275 | 41,975.0 | 133,462.0 | 75,068.0 | 82,248.0 | 333,028 |
| Sulawesi Selatan | - | 2504 | 436,606 | 48,745 | 575,887 | 5231 | 1,068,973 |
| Sulawesi Tengah | 658 | 7479 | 670,317.6 | 773,137.7 | 353,590.5 | 196,143.2 | 2,001,326 |
| Sulawesi Tenggara | - | 19,943 | 112,514.0 | 822,963.0 | 9827.0 | 40,589.0 | 1,005,836 |
| Sulawesi Barat | 213 | 652 | 113,986.0 | 9989.0 | 332,159.0 | 177,389.0 | 634,388 |
| Maluku | - | 40,290 | 1,320,196 | 543,123 | 225,484 | 101,681 | 2,230,773 |
| Maluku Utara | - | 5582 | 524,043 | 458,886 | 40,696 | 38,524 | 1,067,731 |
| Papua | 148,534 | 1,205,358 | 2,890,131 | 828,810 | 1,366,916 | 121,601 | 6,561,350 |
| Papua Barat | 26,887 | 69,939 | 1,885,853 | - | 49,282 | - | 2,031,960 |
| Total | 459,346 | 2,348,264 | 24,861,520 | 7,976,224 | 4,622,824 | 2,334,962 | 42,603,139 |
Notes: LWC = dry lowland and wet climate, LDC = dry lowland and dry climate, HWC = dry highland and wet climate, HDC = dry highland and dry climate, PS = peat swamps, MS = mineral swamps.
Table 3.
Soil and climate characteristics of AL in six agroecosystems.
| Land Characteristic | Agroecosystem Type | |||||
|---|---|---|---|---|---|---|
| LWC | LDC | HWC | HDC | PS | MS | |
| Berau | Bima | Humbahas | TTS | Kuburaya | Tanjabtim | |
| Climate: | ||||||
| Rainfall (mm/year) | 2.438 | 952 | 3034 | 1245 | 2772 | 2584 |
| Temperature (°C) | 26.9 | 28.3 | 23.0 | 22.2 | 27.2 | 26.6 |
| Elevation (m asl) | <200 | <200 | 1420 | 893 | <50 | <10 |
| Topography: | ||||||
| Landform | Rolling tectonic plain | Lava flow | Volcanic | Tectonic hills | Tidal topogenous peat | Tidal swamp |
| Slope (%) | 8–15 | 3–8 | 8–15 | 15–25 | 0–1 | 0–1 |
| Soil: | ||||||
| Great group | Hapludults | Haplustepts | Hapludands | Haplustepts | Haplosaprists | Sulfaquepts |
| Solum (cm) | Deep | Deep | Deep | Slightly deep | Very deep | Very deep |
| Texture | Fine | Slightly fine | Slightly fine | Slightly fine | Sapric | Fine |
| Drainage | Well drained | Well drained | Well drained | Well drained | Poorly drained | Poorly drained |
| pH | Acid | Neutral | Neutral | Neutral | Acid | Acid |
| CEC | Low | Medium | High | High | High | High |
| Base saturation | Low | Very high | High | High | Low | Low |
Notes: LWC = dry lowland and wet climate, LDC = dry lowland and dry climate, HWC = dry highland and wet climate, HDC = dry highland and dry climate, PS = peat swamps, MS = mineral swamps.
Table 4.
The AL at the national and district levels for six agroecosystems.
| Agroecosystem | Districts Amount | AL Based on | ||
|---|---|---|---|---|
| Land Cover Map of Indonesia | Land Cover Map of 54 districts | Visual Interpretation of 54 Districts | ||
| ha | ||||
| LWC | 22 | 24,861,520 | 9,563,644 | 7,754,597 |
| LDC | 16 | 7,976,224 | 3,033,903 | 2,498,629 |
| HWC | 6 | 4,622,824 | 1,297,199 | 1,010,142 |
| HDC | 2 | 2,334,962 | 529,316 | 500,547 |
| PS | 4 | 459,346 | 197,258 | 1,076,536 |
| MS | 4 | 2,348,264 | 3,516,552 | 4,053,793 |
| Total | 54 | 42,603,139 | 18,137,873 | 16,894,244 |
Notes: LWC = dry lowland and wet climate, LDC = dry lowland and dry climate, HWC = dry highland and wet climate, HDC = dry highland and dry climate, PS = peat swamps, MS = mineral swamps.
Table 5.
The suitability of AL for annual and perennial crop development based on their agroecosystems in Indonesia.
Table 5.
The suitability of AL for annual and perennial crop development based on their agroecosystems in Indonesia.
| Island | PS | MS | LWC | LDC | HWC | HDC | Total | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| AC | AC | AC | PC | AC | PC | AC | PC | AC | PC | ||
| ×1000 ha | |||||||||||
| Sumatera | 41 | 343 | 1776 | 1097 | 344 | 236 | 151 | 211 | 16 | 140 | 4355 |
| Jawa | - | 8 | 53 | 47 | 43 | 82 | 6 | 33 | 38 | 34 | 343 |
| Bali & NT | - | 4 | 15 | 65 | 563 | 1248 | 5 | 29 | 16 | 479 | 2423 |
| Kalimantan | 96 | 484 | 3964 | 3322 | 691 | 40 | - | - | - | - | 8596 |
| Sulawesi | 1 | 31 | 336 | 561 | 649 | 459 | 119 | 182 | 41 | 91 | 2470 |
| Maluku & Papua | 175 | 1314 | 4034 | 1774 | 1082 | 672 | 73 | 305 | 10 | 93 | 9533 |
| Total | 313 | 2185 | 10,178 | 6865 | 3372 | 2737 | 353 | 760 | 121 | 838 | 27,721 |
Notes: LWC = dry lowland and wet climate, LDC = dry lowland and dry climate, HWC = dry highland and wet climate, HDC = dry highland and dry climate, PS = peat swamps, MS = mineral swamps, AC = annual crop, PC = perennial crop.
Table 6.
Prediction of AL suitability on the results of visual interpretation in Indonesia.
| Agroecosystem | Sum of District | Land Cover Map of Indonesia | Land Cover Map of 54 Districts | Visual Interpretation of 54 Districts | Prediction of Indonesia |
|---|---|---|---|---|---|
| ha | |||||
| LWC | 22 | 16,727,764 | 5,852,976 | 4,174,303 | 11,930,128 |
| LDC | 16 | 6,108,161 | 2,073,032 | 1,544,315 | 4,550,304 |
| HWC | 6 | 1,113,403 | 630,230 | 510,549 | 901,966 |
| HDC | 2 | 958,301 | 282,636 | 264,916 | 898,219 |
| PS | 4 | 312,307 | 183,038 | 399,305 | 681,309 |
| MS | 4 | 2,153,059 | 2,176,208 | 1,597,777 | 1,580,780 |
| Total | 54 | 27,372,993 | 11,198,120 | 8,491,164 | 20,756,037 |
LWC = dry lowland and wet climate, LDC = dry lowland and dry climate, HWC = dry highland and wet climate, HDC = dry highland and dry climate, PS = peat swamps, MS = mineral swamps.
Table 7.
SWOT analysis of AL in each agroecosystem.
| Agroecosystem | Strengths | Weaknesses | Opportunities | Threats |
|---|---|---|---|---|
| LWC | Distributed widely in Sumatera, Kalimantan, and Papua Islands | High rainfall, low fertility | High potential for the development of various commodities | Requires balanced fertilization, ameliorant, and commodity zoning |
| LDC | Medium fertility | Water scarcity, slightly sallow depth, rock outcrop | High potential for the development of cereal commodities | Exploration of water sources and efficient use of water is needed |
| HWC | Volcanic soil, medium-high fertility | The distribution of highland is not wide | Potential for horticultural commodities | High rainfall and slope; erosion control needed |
| HDC | Medium-high fertility | Dry climate and water scarcity; the distribution of highland is not wide | Potential for suitable horticultural commodities in dry climate areas | Exploration of water sources and efficient use of water is needed |
| PS | Water available for food crop or horticulture | The distribution is narrow | Potential for paddy fields and horticultural commodities | This land requires water management and fertilization |
| MS | Water available for paddy field | Some areas contain pyrite | Potential for paddy fields and perennial crop | This land requires water management and fertilization |
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Mulyani, A.; Mulyanto, B.; Barus, B.; Panuju, D.R.; Husnain. Geospatial Analysis of Abandoned Lands Based on Agroecosystems: The Distribution and Land Suitability for Agricultural Land Development in Indonesia. Land 2022, 11, 2071. https://doi.org/10.3390/land11112071
AMA Style
Mulyani A, Mulyanto B, Barus B, Panuju DR, Husnain. Geospatial Analysis of Abandoned Lands Based on Agroecosystems: The Distribution and Land Suitability for Agricultural Land Development in Indonesia. Land. 2022; 11(11):2071. https://doi.org/10.3390/land11112071
Chicago/Turabian StyleMulyani, Anny, Budi Mulyanto, Baba Barus, Dyah Retno Panuju, and Husnain. 2022. "Geospatial Analysis of Abandoned Lands Based on Agroecosystems: The Distribution and Land Suitability for Agricultural Land Development in Indonesia" Land 11, no. 11: 2071. https://doi.org/10.3390/land11112071
APA StyleMulyani, A., Mulyanto, B., Barus, B., Panuju, D. R., & Husnain. (2022). Geospatial Analysis of Abandoned Lands Based on Agroecosystems: The Distribution and Land Suitability for Agricultural Land Development in Indonesia. Land, 11(11), 2071. https://doi.org/10.3390/land11112071
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