Climate Change Impact Analysis on Food Availability in the Province of Kalimantan Utara, Indonesia

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Indonesian Journal of Geography, Vol 55, No. 3 (2023) 509-516

Climate Change Impact Analysis on Food Availability in the Province of Kalimantan Utara, Indonesia

Rika Harini, Bowo Susilo, Rina Dwi Ariani dan Supriyati Faculty of Geography, Universitas Gadjah Mada, Indonesia

Submit: 2020-03-11 Received: 2022-03-27 Accepted: 2023-12-14

Key words: temperature, rainfall, agricultural production, food crop

Correspondent email:

[email protected]

Abstract Climate change directly affects crop yields as a measure of food availability by, e.g., causing harvest causing harvest failure. El Niño and La Niña are two weather patterns that affect the climate of Indonesia, a country at low latitudes, and reportedly increase its vulnerability to food insecurity. This study was designed to analyze the impact of climate change on food availability in the Province of Kalimantan Utara using secondary data (i.e., temperature, rainfall, and agricultural production) as the base data. This study employed quantitative descriptive analysis to explain the results expressed in maps, tables, and graphs and the regression technique to determine the impact of climate change on food availability. Regression analysis revealed that climate change significantly shaped the availability of rice (0.008) and vegetable commodity (0.000) but posed insignificant effects on tubers, legumes, and fruits. Apart from climate change, food availability also depends on land management, land suitability, capital, technology, and cultivated plant variety.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY NC) licensehttps://creativecommons.org/licenses/by-nc/4.0/.

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Flood Risk Mapping Using GIS and Multi-Criteria Analysis at Nanga Pinoh West Kalimantan Area

*Ajun Purwanto¹, Rustam², Eviliyanto³, Dony Andrasmoro⁴

1,3,4Departmen of Geography Education IKIP PGRI Pontianak

2Departmen of Counseling Guidance Education IKIP PGRI Pontianak

Abstract. Flood is one of the disasters that often hit various regions in Indonesia, specifically in West Kalimantan.

The floods in Nanga Pinoh District, Melawi Regency, submerged 18 villages and thousands of houses. Therefore, this study aimed to map flood risk areas in Nanga Pinoh and their environmental impact. Secondary data on the slope, total rainfall, flow density, soil type, and land cover analyzed with the multi-criteria GIS analysis were used. The results showed that the location had low, medium, and high risks. It was found that areas with high, prone, medium, and low risk class are 1,515.95 ha, 30,194.92 ha, 21,953.80 ha, and 3.14 ha, respectively.

These findings implied that the GIS approach and multi-criteria analysis are effective tools for flood risk maps and helpful in anticipating greater losses and mitigating the disasters.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY NC) licensehttps://creativecommons.org/licenses/by-nc/4.0/.

1. Introductin

Floods occur when a river exceeds its storage capacity, forcing the excess water to overflow the banks and fill the adjacent low-lying lands. This phenomenon represents the most frequent disasters affecting a majority of countries worldwide (Rincón et al., 2018; Zwenzner & Voigt, 2009), specifically Indonesia. Flooding is one of the most devastating disasters that yearly damage natural and man-made features (Du et al., 2013; Falguni & Singh, 2020; Tehrany et al., 2013;

Youssef et al., 2011).

There are flood risks in many regions resulting in great damage (Alfieri et al., 2016; Mahmoud & Gan, 2018) with significant social, economic, and environmental impacts (Falguni & Singh, 2020; Geographic, 2019; Komolafe et al., 2020; Rincón et al., 2018; Skilodimou et al., 2019). The effects include loss of human life, adverse impacts on the population, damage to the infrastructure, essential services, crops, and animals, the spread of diseases, and water contamination (Rincón et al., 2018).

Food accounts for 34% and 40% of global natural disasters in quantity and losses, respectively (Lyu et al., 2019; Petit- Boix et al., 2017), with the occurrence increasing significantly worldwide in the last three decades (Komolafe et al., 2020;

Rozalis et al., 2010). The factors causing floods include climate change (Ozkan & Tarhan, 2016; Zhou et al., 2021), land structure (Jha et al., 2011; Zwenzner & Voigt, 2009), and vegetation, inclination, and humans (Curebal et al., 2016).

Other causes are land-use change, such as deforestation and urbanization (Huong & Pathirana, 2013; Rincón et al., 2018;

N. Zhang et al., 2018; Zhou et al., 2021).

The high rainfall in the last few months has caused much flooding in the sub-districts of the West Kalimantan region.

Thousands of houses in 18 villages in Melawi Regency have been flooded in the past week due to increased rainfall

intensity in the upstream areas of West Kalimantan. This occurred within the Nanga Pinoh Police jurisdiction, including Tanjung Lay Village, Tembawang Panjang, Pal Village, Tanjung Niaga, Kenual, Baru and Sidomulyo Village in Nanga Pinoh Spectacle, Melawi Regency (Supriyadi, 2020).

The flood disaster in Melawi Regency should be mitigated to minimize future consequences by mapping the risk.

Various technologies such as Remote Sensing and Geographic Information Systems have been developed for monitoring flood disasters. This technology has significantly contributed to flood monitoring and damage assessment helpful for the disaster management authorities (Biswajeet & Mardiana, 2009; Haq et al., 2012; Pradhan et al., 2009). Furthermore, techniques have been developed to map flood vulnerability and extent and assess the damage. These techniques guide the operation of Remote Sensing (RS) and Geographic Information Systems (GIS) to improve the efficiency of monitoring and managing flood disasters (Haq et al., 2012).

In the age of modern technology, integrating information extracted through Geographical Information System (GIS) and Remote Sensing (RS) into other datasets provides tremendous potential for identifying, monitoring, and assessing flood disasters (Biswajeet & Mardiana, 2009; Haq et al., 2012;

Pradhan et al., 2009). Understanding the causes of flooding is essential in making a comprehensive mitigation model.

Different flood hazard prevention strategies have been developed, such as risk mapping to identify vulnerable areas’

flooding risk. These mapping processes are important for the early warning systems, emergency services, preventing and mitigating future floods, and implementing flood management strategies (Bubeck et al., 2012; Falguni & Singh, 2020; Mandal

& Chakrabarty, 2016; Shafapour Tehrany et al., 2017).

GIS and remote sensing technologies map the spatial variability of flooding events and the resulting hazards

Received: 2021-12-22 Accepted: 2022-10-13 Keywords:

Flood Risk; GIS, Multi-Criteria Analysis; Nanga Pinoh

*Correspondeny email:

[email protected]

ARTICLE REVIEW RESEARCH ARTICLE ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Indonesian Journal of Geography Vol 55, No 3 (2023): 509-516 DOI: 10.22146/ijg.54800 website: htps://jurnal.ugm.ac.id/ijg

1. Introduction

Climate change in the medium and long terms adversely threatens national and even global food security and agricultural system stability (Liu, 2016). Weather conditions, as well as microclimate and macroclimate (i.e., rainfall and temperature) that are less conducive to vegetative and generative plant growth can interfere with the farming system (Ruminta, Handoko, & Nurmala, 2018; Sumaryanto, 2012), leading to perturbed growing seasons and decreased harvest quantity (Lobell, Schlenker, & Costa-Roberts, 2011) and area (Müller, Cramer, Hare, & Lotze-Campen, 2011; Niang & et al., 2014; Sarr, 2012). Temperature increase that results in climate change can alter rainfall intensity (Songok, Kipkorir,

& Mugalavai, 2011), which is the foremost determinant of food production (Weldearegay & Tedla, 2018). Wet years are generally associated with higher food production, whereas dry years are generally associated with lower production (Nath &

Behera, 2011).

Global warming, a fundamental aspect of climate change, brings about anomalies in rainfall, consequently, elevating the risks of drought (El Niño) and flood (i.e., La Niña)(Niang &

et al., 2014; Tschakert, Sagoe, Ofori-Darko, & Codjoe, 2010).

El Niño and La Niña tend to lower agricultural production and threaten food availability, which have a connection to Goal 13: Climate Action in Sustainable Development Goals (Niang & et al, 2014; Thornton, Jones, Ericksen, & Challinor, 2011). Generally, the El Niño Southern Oscillation can intensify rainfall, tropical cyclones, drought, forest fires, and floods, increase the emergence of other extreme weather, and disrupt agricultural production worldwide (Winarto, Stigter, Dwisatrio, Nurhaga, & Merryna Bowolaksono, 2013). La Niña

is linked to flooding in agricultural areas and subsequent crop damage. At the vegetative stage, flooding can decrease grain yields by as much as 124 kg/ha/day, whereas at the reproductive stage, the loss can reach 157 kg/ha/day (Akhmad, 2017).

Compared with other parts of the world, countries at low latitudes or the tropics are more vulnerable to climate change (Weldearegay & Tedla, 2018) and at a higher risk of depletion because they have a wide rainfall variability (Slingo, Challinor, Hoskins, & Wheeler, 2005). The Food and Agriculture Organization (FAO) reported that, in 2010, 65 countries were on the verge of losing their cereal production by 280 million tons because of global climate change. FAO also estimated that several developing countries in Asia and Africa will suffer from hunger by 2030. Long-term climate change projections have shown that such negative impacts will be even worse after 2030. Climate change vulnerability, as measured from the yields of agriculture, will be higher in developing countries in- the-tropics-than-in developed countries (FAO, 2016) because of slow technological advancements. Moreover, the lack of resources to diminish the negative impact on agriculture, high dependence on the farming sector, and large population size (FAO, 2010) can reduce food availability. Studies of the impact of climate change on food crop production are thereby necessary to determine the extent to which climate change affects agricultural yields, particularly in Indonesia as a developing country.

2. Methods

This study was conducted on the border of Indonesia and Malaysia, i.e., the Province of Kalimantan Utara (Figure 1).

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CLIMATE CHANGE IMPACT ANALYSIS ON FOOD Rika Harini, et al.

The data were analyzed using quantitative descriptive and statistical regression methods to obtain information on climate change (i.e., temperature and rainfall), changes in food availability, and the impact of climate change on food availability. Climate change was determined from trend charts and isohyetal maps processed in the ArcGIS application.

Food availability, was assessed from the data on agricultural production in the Province of Kalimantan Utara. The data on the annual yields of grains, tubers, legumes, fruits, and vegetables were obtained from Statistics Indonesia (Badan Pusat Statistik), processed by the trends method, and presented in graphs for further spatial analysis. The impact of climate change on food availability was analyzed by multiple linear regression in the SPSS software, with rainfall and temperature as independent variables and food availability as the dependent variable. The simultaneous effects that all independent variables had on the dependent variable were identified by an F-test, commonly known as concurrency testing, model testing, or ANOVA. This test indicated whether the regression model developed was good/significant or not good/not significant. The model considered significant if F_count is higher than F_table with a significance value of <5%, whereas it is considered insignificant if F_count is lower than F_table with a significance value of <5%.

3. Results and Discussion Climate Change

Food production is largely influenced by interannual climate variability (i.e., optimal temperature, precipitation, and radiation). Extreme events such as climate change

Indonesian Journal of Geography, Vol 44, No. 2, December 2012: 121 - 134

122

extent to which climate change affects agricultural yields, particularly in Indonesia as a

1

developing country.

2 3

2. Methods

4

This study was conducted on the border of Indonesia and Malaysia, i.e., the Province of

5

Kalimantan Utara (Figure 1).

6

Figure 1: Location of the research area in the Province of Kalimantan Utara, Indonesia.

7

The data were analyzed using quantitative descriptive and statistical regression methods to

8

obtain information on climate change (i.e., temperature and rainfall), changes in food

9

availability, and the impact of climate change on food availability. Climate change was

10

determined from trend charts and isohyetal maps processed in the ArcGIS application. Food

11

availability, was assessed from the data on agricultural production in the Province of

12

Kalimantan Utara. The data on the annual yields of grains, tubers, legumes, fruits, and

13

vegetables were obtained from Statistics Indonesia (Badan Pusat Statistik), processed by the

14

Figure 1: Location of the research area in the Province of Kalimantan Utara, Indonesia.

(Hansen, Sato, & Ruedy, 2012), account for the failure in food availability (Weldearegay & Tedla, 2018). Research on climate variability in the Province of Kalimantan Utara is vital, given that it is relatively vulnerable to climate change.

Changes in rainfall patterns and temperatures can characterize climate change. A trend of variation was observed in the rainfall pattern of the province. There were no rain months when rainfall was expected, and the rainy seasons extended to the dry periods. According to the Meteorological, Climatological, and Geophysical Agency (BMKG), in March 2018, the rainfall in the Province of Kalimantan Utara was above average.

This province has a bimodal monthly rainfall distribution with two rainfall peaks and categorically rainy seasons nearly throughout the year, or called an equatorial rainfall pattern.

Rainfall usually peaks in March and October or during the equinoxes, i.e., when the sun is near or perpendicular to the equator (Hermawan, 2010) twice a year (Fadholi, 2013).

Apart from the two peaks, the equatorial rainfall pattern also consists of two lows during the dry season, which often occur in January and July. Figure 2 shows the shifts in the maximum and minimum rainfall in the Province of Kalimantan Utara that have modified the rainfall pattern. For instance, two rainfall highs occurred in February and September (instead of March and October), whereas two rainfall lows were identified in January and March (instead of January and July). Changes in rainfall patterns are one factor responsible for the loss of arable land (Kotir, 2010; Niang & et al., 2014), where food crops are produced (Lindgren, Albihn, & Andersson, 2011).

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Changes in average rainfall in the period between 2012 and 2018 are shown in Figure 3. In 2012, the average rainfall was generally high in all areas of the province, except for Tarakan City and Malinau Regency. Meanwhile, in 2018, the average rainfall of nearly the entire region was low, signifying a categorically rapid change in precipitation.

Apart from the province, a significant change also occurred in several regions in Indonesia. Between 2010 and 2039, Indonesia is estimated to experience an increase in rainfall, as evidenced by positive anomalies of the convection zone, temperature increease, and changes in evaporation, particularly for the highest convection zones along the Malacca Strait, Banda Sea, Karimata Sea, and Arafura Sea (Syahbuddin

& Wihendar, 2014). Such an increase has been identified in, among others, Kulon Progo Regency in Yogyakarta (Harini &

Nurjani, 2014).

In the 6 years (i.e., 2012-2018), rainfall fluctuated annually (Figure 4). The 2012 data showed the highest rainfall in the province, which amounted to 4,364.3 mm³, whereas the 2015 data showed the lowest rainfall (wich amounted to 2,043.3 mm³). In 2018, rainfall was the highest in Tarakan City and the smallest in Bulungan Regency. The optimum amount of rain is required to improve productivity and food security. In high quantities, rainwater can trigger soil erosion or landslides (i.e., environmental degradation), resulting in decreased agricultural production and crop failure—the two determinants of food availability.

trends method, and presented in graphs for further spatial analysis. The impact of climate 1

change on food availability was analyzed by multiple linear regression in the SPSS software, 2

with rainfall and temperature as independent variables and food availability as the dependent 3

variable. The simultaneous effects that all independent variables had on the dependent 4

variable were identified by an F-test, commonly known as concurrency testing, model testing, 5

or ANOVA. This test indicated whether the regression model developed was good/significant 6

or not good/not significant. The model considered significant if F

count

is higher than F

table

with 7

a significance value of <5%, whereas it is considered insignificant if F

count

is lower than F

table

8

with a significance value of <5%.

9

3. Results and Discussion 10

Climate Change 11

Food production is largely influenced by interannual climate variability (i.e., optimal 12

temperature, precipitation, and radiation). Extreme events such as climate change (Hansen, 13

Sato, & Ruedy, 2012), account for the failure in food availability (Weldearegay & Tedla, 2018).

14

Research on climate variability in the Province of Kalimantan Utara is vital, given that it is 15

relatively vulnerable to climate change.

16

Changes in rainfall patterns and temperatures can characterize climate change. A trend of 17

variation was observed in the rainfall pattern of the province. There were no rain months 18

when rainfall was expected, and the rainy seasons extended to the dry periods. According to 19

the Meteorological, Climatological, and Geophysical Agency (BMKG), in March 2018, the 20

rainfall in the Province of Kalimantan Utara was above average.

21

Source: Secondary Data Processing, 2018 22

a b

Source: Secondary Data Processing, 2018

Figure 2: Monthly rainfall charts of the province (a) and the regencies (b) of Kalimantan Utara in 2012-2018

122

Figure 2: Monthly rainfall charts of the province (a) and the regencies (b) of Kalimantan

1

Utara in 2012-2018

2

This province has a bimodal monthly rainfall distribution with two rainfall peaks and

3

categorically rainy seasons nearly throughout the year, or called an equatorial rainfall pattern.

4

Rainfall usually peaks in March and October or during the equinoxes, i.e., when the sun is

5

near or perpendicular to the equator (Hermawan, 2010) twice a year (Fadholi, 2013). Apart

6

from the two peaks, the equatorial rainfall pattern also consists of two lows during the dry

7

season, which often occur in January and July. Figure 2 shows the shifts in the maximum and

8

minimum rainfall in the Province of Kalimantan Utara that have modified the rainfall

9

pattern. For instance, two rainfall highs occurred in February and September (instead of

10

March and October), whereas two rainfall lows were identified in January and March (instead

11

of January and July). Changes in rainfall patterns are one factor responsible for the loss of

12

arable land (Kotir, 2010; Niang & et al., 2014), where food crops are produced (Lindgren,

13

Albihn, & Andersson, 2011).

14

Figure 3. Average rainfall maps of the Province of Kalimantan Utara in 2012 (a) and 2018 (b)

15

Changes in average rainfall in the period between 2012 and 2018 are shown in Figure 3. In

16

2012, the average rainfall was generally high in all areas of the province, except for Tarakan

17

City and Malinau Regency. Meanwhile, in 2018, the average rainfall of nearly the entire

18

region was low, signifying a categorically rapid change in precipitation.

19

a b

Figure 3. Average rainfall maps of the Province of Kalimantan Utara in 2012 (a) and 2018 (b)

Figure 5 shows the monthly fluctuation of temperature in the Province of Kalimantan Utara. The highest temperature was in April, and the lowest temperature was in December.

The graph also illustrates the absence of a relationship between rainfall and temperature. For instance, the rain peaked twice in 1 year, but the temperature was at its highest level only once.

The annual rainfall maps shown in Figure 6 indicate that the rain fluctuated constantly from 2012 to 2018. In 2012, the maximum and minimum rainfall occurred in Bulungan and Malinau Regencies, respectively. For Nunukan, and Tana Tidung Regencies and Tarakan City, the rain was moderate.

In 2018, Bulungan Regency no longer received the highest rainfall. By contrast the lowest, and the highest rainfall were identified in Tarakan City. In other words, from 2012 to 2018, Tarakan City and Malinau Regency experienced increased rainfall, whereas Nunukan, Tana Tidung, and Bulungan Regencies received annual rainfall that was lower than average.

The graph shown in Figure 7 illustrates the annual temperature fluctuation in the Province of Kalimantan Utara between 2012 and 2018. The highest and lowest temperatures occurred in 2015 and 2018, respectively. During this period, the temperature of the entire province increased, except for Nunukan Regency, wich instead experienced a temperature, decrease because of several conservation practices.

Temperature increase perturbs the plant production process directly and indirectly (Weldearegay & Tedla, 2018).

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Source: Secondary Data Processing, 2018

1

Figure 4. Monthly rainfall charts of the province (a) and regencies (b) of Kalimantan Utara in

2

2012-2018

3

Apart from the province, a significant change also occurred in several regions in Indonesia.

4

Between 2010 and 2039, Indonesia is estimated to experience an increase in rainfall, as

5

evidenced by positive anomalies of the convection zone, temperature increease, and changes

6

in evaporation, particularly for the highest convection zones along the Malacca Strait, Banda

7

Sea, Karimata Sea, and Arafura Sea (Syahbuddin & Wihendar, 2014). Such an increase has

8

been identified in, among others, Kulon Progo Regency in Yogyakarta (Harini & Nurjani,

9

2014).

10

In the 6 years (i.e., 2012-2018), rainfall fluctuated annually (Figure 4). The 2012 data showed

11

the highest rainfall in the province, which amounted to 4,364.3 mm

³

, whereas the 2015 data

12

showed the lowest rainfall (wich amounted to 2,043.3 mm

³

). In 2018, rainfall was the highest

13

in Tarakan City and the smallest in Bulungan Regency. The optimum amount of rain is

14

required to improve productivity and food security. In high quantities, rainwater can trigger

15

soil erosion or landslides (i.e., environmental degradation), resulting in decreased agricultural

16

production and crop failure—the two determinants of food availability.

17

a b

Source: Secondary Data Processing, 2018

Figure 4. Monthly rainfall charts of the province (a) and regencies (b) of Kalimantan Utara in 2012-2018Indonesian Journal of Geography, Vol 44, No. 2, December 2012: 121 - 134

122

Source: BPS Data Analysis

1

Figure 5: Monthly temperature charts of the province (a) and the regencies (b) of Kalimantan

2

Utara

3

Figure 5 shows the monthly fluctuation of temperature in the Province of Kalimantan Utara.

4

The highest temperature was in April, and the lowest temperature was in December. The

5

graph also illustrates the absence of a relationship between rainfall and temperature. For

6

instance, the rain peaked twice in 1 year, but the temperature was at its highest level only

7

once.

8

Figure 6. Annual rainfall maps of the Province of Kalimantan Utara in 2012 (a) and 2018 (b)

9

The annual rainfall maps shown in Figure 6 indicate that the rain fluctuated constantly from

10

2012 to 2018. In 2012, the maximum and minimum rainfall occurred in Bulungan and

11

Malinau Regencies, respectively. For Nunukan, and Tana Tidung Regencies and Tarakan

12

City, the rain was moderate. In 2018, Bulungan Regency no longer received the highest

13

rainfall. By contrast the lowest, and the highest rainfall were identified in Tarakan City. In

14

other words, from 2012 to 2018, Tarakan City and Malinau Regency experienced increased

15

a b

Figure 5: Monthly temperature charts of the province (a) and the regencies (b) of Kalimantan Utara

122

1

Figure 5: Monthly temperature charts of the province (a) and the regencies (b) of Kalimantan

2

Utara

3

Figure 5 shows the monthly fluctuation of temperature in the Province of Kalimantan Utara.

4

The highest temperature was in April, and the lowest temperature was in December. The

5

graph also illustrates the absence of a relationship between rainfall and temperature. For

6

instance, the rain peaked twice in 1 year, but the temperature was at its highest level only

7

once.

8

9

The annual rainfall maps shown in Figure 6 indicate that the rain fluctuated constantly from

10

2012 to 2018. In 2012, the maximum and minimum rainfall occurred in Bulungan and

11

Malinau Regencies, respectively. For Nunukan, and Tana Tidung Regencies and Tarakan

12

City, the rain was moderate. In 2018, Bulungan Regency no longer received the highest

13

rainfall. By contrast the lowest, and the highest rainfall were identified in Tarakan City. In

14

other words, from 2012 to 2018, Tarakan City and Malinau Regency experienced increased

15

a b

rainfall, whereas Nunukan, Tana Tidung, and Bulungan Regencies received annual rainfall

1

that was lower than average.

2

3

Figure 7: Temperature bar charts of the province (a) and regencies (b) of Kalimantan Utara in

4

2012-2018

5

The graph shown in Figure 7 illustrates the annual temperature fluctuation in the Province of

6

Kalimantan Utara between 2012 and 2018. The highest and lowest temperatures occurred in

7

2015 and 2018, respectively. During this period, the temperature of the entire province

8

increased, except for Nunukan Regency, wich instead experienced a temperature, decrease

9

because of several conservation practices. Temperature increase perturbs the plant

10

production process directly and indirectly (Weldearegay & Tedla, 2018).

11

Although the yearly rainfall in the study area fluctuated, the temperature from 2012 to 2018

12

exhibited an increasing trend. This condition corresponded to weather anomalies (i.e., La

13

Niña and El Niño) arising from several factors, such as global warming, that increase the CO2

14

concentrations and temperatures, causing a decrease in photosynthesis and biomass

15

production and increase in the vulnerability of some commodity crops, particularly in the

16

tropics, such as the Province of Kalimantan Utara. In general, extreme weather changes can

17

alter the transmission of pathogens and the presence of insects and pests that can interfere

18

with crop production (Jaggard, Qi, & Ober, 2010).

19

Food Availability

20

The growth of food crop production in an area is determined by two factors: (1) the

21

a

b Source: BPS Data Analysis

Figure 7: Temperature bar charts of the province (a) and regencies (b) of Kalimantan Utara in 2012-2018

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Although the yearly rainfall in the study area fluctuated, the temperature from 2012 to 2018 exhibited an increasing trend. This condition corresponded to weather anomalies (i.e., La Niña and El Niño) arising from several factors, such as global warming, that increase the CO2 concentrations and temperatures, causing a decrease in photosynthesis and biomass production and increase in the vulnerability of some commodity crops, particularly in the tropics, such as the Province of Kalimantan Utara. In general, extreme weather changes can alter the transmission of pathogens and the presence of insects and pests that can interfere with crop production (Jaggard, Qi, & Ober, 2010).

Food Availability

The growth of food crop production in an area is determined by two factors: (1) the expansion of the harvested area and (2) the increase in productivity. The average food availability and growth rates from 2012 to 2018 in the Province of Kalimantan Utara are presented in Table 1.

Cereals or grains are a source of carbohydrates and the main staple food of the Indonesian population. Grain needs as a staple food increase from year to year with population growth. However, rice production cannot meet people’s needs for grain, because productive agricultural land is used for nonagricultural activities. To meet the community’s need for grain, new areas that have the potential to be planted need to be discovered (Harini, Susilo, & Nurjani, 2015). As shown in Table 1, the availability of grains from 2012 to 2018 in the Province of Kalimantan Utara fluctuated with an average of 70,017.12 tons and annual reduction rate of -0.0637. This reduction was attributed to the lower profitability of grains compared with that of other commodities, e.g., guava. Even worse, the less lucrative agricultural sector has caused farmers to work in different fields e.g., the grain farmers in Nunukan (Sebatik) Regency who have chosen to work as seaweed farmers instead because the selling price of seaweed is more promising than that of grains. This finding is consistent with that of Tulangow, Waney, and Timban (2017), who reported that most people in Tatelu—a village in the Province of Sulawesi Utara—prefer to work in mining because the wages obtained from the agricultural sector are categorically low, and the nonagricultural industry is considered to be economically prospective. Moreover, the extent of arable land has shrunk significantly and, when coupled with climate uncertainty, led to a steady decline in crop yields and farmers-incomes (Pesik, Kapantow, & Katiandagho, 2016).

Aside from grains, farmers also plant tubers. Table 1 shows that tuber production in 2012-2018 increased in

quantity, with an annual growth rate of 0.0123. In the Province of Kalimantan Utara, tubers are usually consumed as a side dish instead of staple food. Similar to grains, the availability of beans as a source of plant proteins increased at an annual rate of 0.1406, with an average production of 1,065.56 tons.

Sources of vitamins and minerals, i.e., fruits and vegetables, showed increasing trends with annual growth rates of 0.1175 and 0.2469, respectively.

Figure 8 shows the distribution of food availability in the Province of Kalimantan Utara. Based on this map, Bulungan Regency produced the highest average of grain, which was 37,575 tons. This condition was supported by the provincial government which designated 50,000 ha of land for integrated agriculture called the Delta Kayan Food Estate. According to Suwarno (2010), the expansion of agricultural land in Indonesia is essential in an attempt to improve food security.

In Indonesia, a vast area currently used for dry cultivation and covered by swamps has the same potential for agriculture as the Delta Kayan Food Estate. Furthermore, 18.7 million ha out of the 29.8 million ha of abandoned land in the country is suitable for the cultivation of agricultural commodities (Mulyani & Agus, 2017). Wahyunto et al, (2013) confirmed that the degraded peatlands nationwide cover an area of 3.7 million ha, 20% of which is suitable for food crops and horticulture farming.

In addition to grains, Bulungan Regency had the highest availability of beans (869 tons) and fruits (11,358 tons).

Nevertheless, fruit production showed a steady decline at an annual rate of -0.091. In contrast to Bulungan Regency, Tana Tidung Regency had the lowest availability of grains (1,573 tons) and beans (2.71 tons). Nunukan Regency had the highest availability of tubers, with an average of 20,904 tons and an annual growth rate of 0.0625, whereas Tana Tidung Regency had the lowest, i.e., an average of 1,179 tons. For vegetables, the highest availability was observed in Tarakan City, with an average of 21,640 tons and an annual growth rate of 0.4376. This abundance was supported by the Department of Agriculture in Tarakan City, which introduced hydroponics and organic agriculture programs, particularly for vegetables. Agricultural products can also be maximized by implementing urban agriculture (Nordahl, 2009). Urban agriculture can produce on average 15% to 20% of food in the world (Smith, Nasr, & Ratta, 2001). For this reason, Haletky and Taylor (2006) reported that, if implemented sustainably, these farming practices can be a solution to the issues related to food availability and security.

Malinau Regency produced the lowest quantity of vegetables, with an average of 528 tons and an annual growth Table 1: Average production and growth rate of food commodities in the Province of Kalimantan Utara in 2012-2018

Types of food commodities Average availabilities in 2012-2018

(tons) Growth rates

Source of carbohydrate

1. Grains 70,017.12 -0.0637

2. Tubers 33,751.80 0.0123

Source of plant protein

- Legumes 1,065.56 0.1406

Source of vitamins and minerals

1. Fruits 32,622.17 0.1175

2. Vegetables 33,830.88 0.2469

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rate of -0.1667. According to Springmann et al., (2016), vegetable and fruit scarcity causes vitamin deficiency that, unless addressed properly, is projected to be responsible for 534,000 deaths per year in 2050.

Decreased productivity and increased risk of crop failure or crop damage caused by extreme weather can severely impact food availability (Noiret, 2016). The impact of climate change is estimated to start spreading globally in 2030 (FAO, 2016), and by 2080, the agricultural outputs will have declined by 20% in developing countries, should agricultural systems fail to implement adaptation measures. Agricultural yields are predicted to worsen significantly if El Niño occurs at the same time as the temperature increases. A temperature increases of 20°C will reduce corn yields by 20% and rice production by 10%, and regions at low latitudes, such as the Province of Kalimantan Utara, will experience a higher loss in corn production (Porter et al, 2014). Global warming is associated with a 10% to 40% decrease in rainfall, which results in a reduction in peanut production by 2.5% to 15% (Akhmad, 2017).

Impacts of Climate Change on Food Availability The agricultural sector, particularly food crops, is most vulnerable to climate change because climate variability explains approximately 60% of the primary factors of food production (Matiu, Ankerst, & Menzel, 2017; Osborne &

Wheeler, 2013; Ray, Gerber, MacDonald, & West, 2015).

The impact climate change on multidimensional agriculture needs to be anticipated by observing changes in rainfall and fluctuations in temperature. Generally, in tropical regions, such as the Province of Kalimantan Utara, crop productivity decreases because of the 2°C to 3°C increase in warming (Mariara & Drb, 2015). In this study, the impact of climate change on food availability was assessed statistically using

multiple linear regression between rainfall-temperature and food availability. There were three hypotheses, i.e., that air temperature (1) and rainfall (2) significantly influence food availability and that both temperature and rainfall simultaneously affect food availability (3). The results are presented in Tables 2 and 3.

Table 2 shows that changes in temperature and rainfall significantly influenced the availability of grains and vegetables, with a significance level of ≤0.05. This result was also supported by the F-test: F_count > F_table (6.94). In contrast to that of tubers, the production of legumes and fruits was not significantly affected by the changes in temperature and rainfall, as evidenced by F_count< F_table (6.94) and a significance value greater than alpha (0.05). The analysis results showed that the effects of rainfall and temperature on the production of sweet potatoes, fruits, and legumes (R²) amounted to ≤50%, whereas the remaining percentage accounted for the effects of other factors that were excluded in this study.

Table 2 also shows that rainfall had a higher significance value than temperature, which is consistent with previous studies in which precipitation was confirmed to have a higher explanatory power to change yields than temperature. The same case applies to the rainfall-yield correlation, which is higher or stronger than the temperature-yield correlation.

Climate change, particularly rainfall, was concluded to have an impact on food production, particularly rice. This conclusion is consistent with the results of the climate change study conducted by Ruminta et al., (2018), who claimed that temperature increase and changes in rainfall patterns are responsible for the decrease in rice production in Sumatra Selatan and Malang. This finding is consistent with that of Hosang, Tajuh, and Rogi, (2012), who catagorized both variables as influencing factors of rice production in the Province of Sulawesi Utara.

122

Figure 8 Map of Average food availability in the Province of Kalimantan Utara in 2012-2018

1

In addition to grains, Bulungan Regency had the highest availability of beans (869 tons) and

2

fruits (11,358 tons). Nevertheless, fruit production showed a steady decline at an annual rate

3

of -0.091. In contrast to Bulungan Regency, Tana Tidung Regency had the lowest availability

4

of grains (1,573 tons) and beans (2.71 tons). Nunukan Regency had the highest availability of

5

tubers, with an average of 20,904 tons and an annual growth rate of 0.0625, whereas Tana

6

Tidung Regency had the lowest, i.e., an average of 1,179 tons. For vegetables, the highest

7

availability was observed in Tarakan City, with an average of 21,640 tons and an annual

8

growth rate of 0.4376. This abundance was supported by the Department of Agriculture in

9

Tarakan City, which introduced hydroponics and organic agriculture programs, particularly

10

for vegetables. Agricultural products can also be maximized by implementing urban

11

agriculture (Nordahl, 2009). Urban agriculture can produce on average 15% to 20% of food in

12

the world (Smith, Nasr, & Ratta, 2001). For this reason, Haletky and Taylor (2006) reported

13

that, if implemented sustainably, these farming practices can be a solution to the issues

14

related to food availability and security.

15

Figure 8 Map of Average food availability in the Province of Kalimantan Utara in 2012-2018

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515

The regression analysis between temperature-rainfall and food availability (Table 3) proved that temperature and rainfall simultaneously had a significant impact on the availability of grains and vegetables, with significance values of 0.038 and 0.000. Respectively, as evidenced by the F-test: F_count > F_table (6.94) and a significance value of 0.05. This result was attributed to the limited data available for temperature and rainfall analyses. The ideal duration of time-series data for analyzing rainfall and temperature change 10 years, but in the Province of Kalimantan Utara, only 6 years of data was available. Food availability is influenced not only by climate change but also by several other factors controlling food production, namely land management, soil fertility, land suitability, capital/asset, technology, cultivated plant variety (Crespo, Hachigonta,

& Tadross, 2011), irrigation, fertilization, and harvest area.

The statement that climate change negatively impacts the agricultural sector in the Province of Kalimantan Utara is consistent with that of Harini and Nurjani, (2014), who claimed that ≥70% of corn, sweet potato, and cassava in the Special Region of Yogyakarta are affected by climate change and that agricultural production continues to decline.

4. Conclusion

Climate change due to rainfall fluctuations and extreme temperatures has been attributed to drought and floods that directly and indirectly affect agricultural systems, particularly crop production that support food availability. This study has proven that climate change negatively affects the production of grains and vegetables, which is consistent with the hypothesis that climate change reduces crop yields in tropical regions, such as the Province of Kalimantan Utara. Grains and vegetables are more vulnerable to climate change than tubers, legumes, and fruits. Based on the analysis results, climate and rainfall determine 80% of grain production and 98% of vegetable production. For tubers, legumes, and fruits, the effects of precipitation and temperature on their availability

were ≤50%. Apart from rainfall and temperature, agricultural production is also influenced by several other factors, namely land management, soil fertility and land suitability, capital/

asset, technology, and cultivated plant variety. However study, excludes these factors to highlight the connection between the study results and the existing theories.

Acknowledgment

The authors would like to thank the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia for funding this research under the scheme of

“Penelitian Terapan Unggulan Perguruan Tinggi” in 2018. The authors would also like to thank the Research Directorate of Universitas Gadjah Mada, Yogyakarta, Indonesia.

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