Drought-Induced Agricultural and Food Security Challenges in the Baribo Basin, Cambodia

Abstract

Rice production within the Tonle Sap basin is a critical driver of economic and social development in Cambodia. This region has been subject to various natural disasters, with increasing attention directed towards drought. This study aims to evaluate the impacts of drought on agriculture and food security through an in-depth case study of the Baribo basin, a sub-basin of the Tonle Sap. The analysis spans the period from 1985 to 2008, a timeframe characterized by relatively high-quality data. Drought assessment was conducted using ground observations and satellite-based products, with the Standardized Precipitation Index (SPI) and Standard Vegetation Index (SVI) employed to assess meteorological and agricultural droughts, respectively. Findings from both the SPI and SVI indicate that drought constitutes a significant natural hazard contributing to food insecurity in the study area. The highest drought intensity (DI) and drought severity (DS) were recorded during the 1993–1994 period, while the most prolonged drought duration (DD) was observed from 2002 to 2006. The year 2004 witnessed the most severe impact on rice production, with approximately 46% of the total cultivated area affected. The analysis further reveals a strong correlation between the drought duration and the extent of rice cultivation affected, as well as the overall food security in the Tonle Sap basin.

1. Introduction

Food security is a complex, multifaceted, and politically driven issue at both the global and local levels [1,2,3]. It is defined as a condition where all individuals, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their dietary needs and preferences for an active and healthy life [4,5]. Over the past half-century, the global food demand has consistently outpaced the supply, primarily due to the increasing world population [6,7,8,9,10,11]. At the same time, natural disasters such as droughts have increasingly threatened food security, particularly in countries like Cambodia that rely heavily on agriculture [2,6,11,12,13].

In Cambodia, where approximately one-third of the population depends on traditional rice farming [14], drought presents a significant challenge to both agricultural production and food security. To address these challenges, the Royal Government of Cambodia (RGC) has implemented a series of policies under its “Rectangular Strategy” (2004–2023), focusing on sustainable water resource management and disaster risk reduction, with the aim of boosting agricultural productivity [15]. This strategy emphasizes the importance of improving water management systems and enhancing resilience against natural disasters like drought, which have historically caused substantial damage to the agricultural sector.

The Tonle Sap basin, a crucial agricultural region that covers approximately 44% of Cambodia’s territory and is home to about 32% of the population [16], has experienced many forms of natural disasters, including drought [17]. Drought, a slow-onset natural disaster [18], can cause large reductions in agricultural yields and exacerbate food insecurity. The Ministry of Environment (MoE) of Cambodia has estimated that droughts in 1998 and 2002 caused a 20% reduction in the country’s agricultural Gross Domestic Product (GDP), affecting over half of the population [19]. The severe drought in 2004 damaged 3000 km² of rice paddies, leading to an 82% loss in potential rice production, while a drought in 2009 necessitated approximately USD 12 million in mitigation efforts [19]. Another severe drought during 2015–2016, caused by the El Niño, affected an estimated 2.5 million people across 18 provinces, leading to widespread water shortages, livestock losses, and reduced agricultural productivity [20]. In late 2018 and early 2019, the country experienced drought conditions once more as rainfall was insufficient, leading to river water shortages and drought declarations in several regions. Agricultural losses were substantial, with 324,641 hectares of rice and 44,734 hectares of other crops impacted across 16 provinces [21]. More recently, the Mekong River Commission (MRC) reported a prolonged drought affecting most provinces of Cambodia during the early rainy season of 2024, resulting from significantly below-average and delayed rainfall [22]. Given the recurring nature and significant impact of drought on Cambodia’s agricultural sector and its implications for national food security, it is crucial to assess the characteristics of drought, including its frequency, duration, severity, intensity, and spatial distribution. Such assessments are essential for understanding drought processes and identifying vulnerable agricultural areas, which can guide both policy and adaptation strategies [23,24].

Droughts are typically categorized into three types: meteorological, agricultural, and hydrological [25]. This study focuses on assessing meteorological and agricultural droughts, as hydrological drought is less relevant to rice production in Cambodia. Meteorological drought refers to a deficiency in rainfall, with its assessment relying primarily on rainfall and temperature data [26]. This type of drought can lead to insufficient soil moisture, subsequently causing agricultural drought, which occurs when crops reach their wilting point, leading to crop failure [27].

Drought indices or indicators are widely used to quantify and assess different types of droughts [28]. These indices transform raw climatological variables into single numerical values, providing a more convenient tool for drought assessment [29]. The Standardized Precipitation Index (SPI) is a widely used meteorological drought index in the Lower Mekong region and Tonle Sap basin [19,30,31]. Numerous studies have demonstrated that the SPI effectively captures drought characteristics across these regions [19,30,31,32,33]. Moreover, the World Meteorological Organization (WMO) has recommended the SPI as a reference meteorological drought index [34]. While ground observation data, such as soil moisture and evapotranspiration, are often limited, advances in satellite technology have significantly improved agricultural drought assessments in recent decades. The Standard Vegetation Index (SVI), recognized as an agricultural drought index, uses the Normalized Difference Vegetation Index (NDVI) as its primary input to assess vegetation greenness and density [35,36]. The SVI has been applied globally due to its adaptability and sensitivity to drought conditions [37]. Furthermore, the SVI was introduced in a training session for technical staff from three ministries and Tonle Sap Authorities (TSAs) under the knowledge and innovation support for the Asian Development Bank (ADB)’s water financing program [38].

The objective of this study is to assess the impacts of drought on agriculture and food security in the Baribo basin, providing insights to support the implementation of RGC policies and contribute to achieving the United Nations Sustainable Development Goals. This study builds upon previous research in the region by employing both the SPI and SVI, which allows for a comprehensive assessment of meteorological and agricultural droughts. While previous studies in Cambodia often focused on either precipitation anomalies (using the SPI) or vegetation stress (using the SVI) in isolation, this research integrates both indices to provide a more holistic understanding of how droughts have evolved to affect agricultural systems. The combined analysis of the SPI and SVI enables a clearer identification of vulnerable areas and offers deeper insight into the interaction between meteorological drought and its impact on agricultural productivity, particularly in regions heavily reliant on rain-fed farming systems. Furthermore, this study contributes to the existing literature by linking the findings from the SPI and SVI to policy recommendations that align with Cambodia’s Rectangular Strategy, emphasizing the need for improving drought monitoring systems and adapting agricultural practices to enhance resilience.

2. Study Area

The Baribo basin is located between latitudes 11°30′00″ N–12°42′00″ N and longitudes 104°00′00″ E–104°57′00″ E (Figure 1). The basin encompasses an area of 7092 km², with elevations ranging from 0 to 1800 meters above mean sea level (m a.m.s.l.). The basin’s hydrological network consists of several small streams flowing from the west to the east, ultimately discharging into the Tonle Sap Great Lake. The basin is divided into three primary regions: the Northern region (1091 km²), the Central region (2995 km²), and the Southern region (3006 km²).

Land use within the Baribo basin is predominantly forested, accounting for 63.2% of the total area, as reported in [39]. Agricultural activities are concentrated mainly in the eastern part of the basin, occupying 34.4% of the area, with over 95% of this agricultural land dedicated to rice paddy cultivation. The remaining land use comprises urban areas and water bodies.

The Baribo basin’s annual rainfall is primarily influenced by tropical monsoons and isolated tropical cyclones [40]. The variation in the rainfall over the basin area depends on the topography [41]. The Southern region, characterized by low-gradient and low-relief landscapes, receives an average annual rainfall of approximately 1040 mm, while the Northern region adjacent to the Tonle Sap Great Lake experiences higher rainfall, averaging around 1525 mm annually [33].

Three types of rice varieties are commonly grown in Cambodia: long-duration (LD), medium-duration (MD), and early-duration (ED) varieties. The cropping periods for LD, MD, and ED rice varieties normally span six, four, and three months, respectively (Figure 2). LD rice is primarily grown in the rain-fed areas, with the seeding period occurring from mid-May to mid-July, and harvesting taking place between mid-October and mid-December. MD rice is grown during the wet season, from mid-August to mid-December.

ED rice was introduced to Cambodia’s farming system in the 2000s due to its shorter cropping period, reduced water requirements, and potential for higher income compared to LD rice. ED rice can be harvested approximately three months after seeding and can be grown up to three times per year. ED rice varieties are cultivated in three distinct cycles: (1) from early January to late March (ED1), during which farmers utilize standing water and residual soil moisture, supplemented by irrigation; (2) from early May to early August (ED2), when early wet-season rains are utilized, followed by harvesting during a brief dry period associated with the shift of the Inter-Tropical Convergence Zone (ITCZ); and (3) from early September to mid-December (ED3), when farmers rely heavily on wet-season rainfall, with the harvest period coinciding with that of the LD and MD rice varieties. The proportions of land area planted with LD, MD, and ED rice are approximately 20%, 40%, and 33%, respectively [19].

3. Materials and Methods

The SPI and SVI were used in this study to assess meteorological and agricultural droughts, accordingly. These indices were calculated at 3- and 6-month timescales to align with the cropping patterns of LD, MD, and ED rice varieties and a 12-month timescale to assess the annual variability in the rainfall and vegetation greenness density. The rainfall data from 12 stations located within or nearby the Baribo basin (Figure 1) were obtained from the Ministry of Water Resources and Meteorology (MOWRAM). The NDVI data were downloaded from the National Oceanic and Atmospheric Administration (NOAA) website.

Given the ungauged nature of the basin, obtaining a sufficient number of gauges with adequate record lengths for reliable analysis is challenging. Previous studies have indicated that at least 20 years of data are necessary for robust low-flow or drought analysis [42,43,44]. Consequently, the analysis in this study is based on the period from 1985 to 2008, a timeframe chosen for several compelling reasons despite it being a historical period.

First, this period represents one of the most data-rich eras in terms of both the quantity and quality of the meteorological and vegetation data available for the Baribo basin. The 1985–2008 period offers a unique opportunity to conduct a robust and reliable analysis, as it encompasses the longest continuous record with sufficient spatial and temporal coverage.

Furthermore, the selected period captures a range of climatic conditions, including significant drought events, which are critical for understanding the long-term patterns and impacts of drought on agriculture and food security. By examining this extended historical period, this study provides insights into the baseline conditions of and variability in drought, which are essential for contextualizing and interpreting more recent and future trends in the context of climate change.

Finally, the findings from this period remain highly relevant, as they inform the development of long-term strategies and policies for drought management that are based on observed trends and patterns. These insights are crucial for enhancing the resilience of agricultural systems in the Baribo basin and similar regions, ensuring that future policy and management decisions are grounded in a thorough understanding of the historical conditions.

The estimation of the drought indices and the analytical framework are detailed in the following sections.

3.1. Meteorological Drought Assessment

The SPI, developed by McKee, Doesken [45], is a widely used meteorological drought index [46] and only requires monthly rainfall as an input. The estimation process is straightforward, beginning with the accumulation of rainfall data over a specific timescale to capture short-, medium-, and long-term rainfall anomalies. Due to the skewed nature of rainfall data, it is necessary to fit them to an appropriate skewed probability distribution, typically the Gamma distribution with two parameters, as shown in Equation (1):

$$g\left(x\right)={\displaystyle \frac{1}{{\beta }^{\alpha }\mathsf{\Gamma }\left(\alpha \right)}}{x}^{\alpha -1}{e}^{-x/\beta };\mathrm{for}x,\alpha ,\beta >0$$

(1)

where $x$ is the amount of rainfall, and $\mathsf{\Gamma }\left(\alpha \right)$ is the gamma function, which is calculated from Equation (2):

$$\mathsf{\Gamma }\left(\alpha \right)={\int }_0^{\infty }{y}^{\alpha -1}{e}^{-y}dy$$

(2)

where $y$ is the integration variable, and $\alpha$ and $\beta$ are the shape and scale parameters, respectively, which can be estimated based on the maximum likelihood solutions using Equations (3)–(5):

$$\widehat{\alpha }={\displaystyle \frac{1}{4A}}(1+\sqrt{1+{\displaystyle \frac{4A}{3}}})$$

(3)

$$\widehat{\beta }={\displaystyle \frac{\overline{x}}{\widehat{\alpha }}}$$

(4)

$$A=\ln \left(\overline{x}\right)-{\displaystyle \frac{\sum \ln \left(x\right)}{n}}$$

(5)

where $n$ is the number of time series.

The cumulative probability is given by the following:

$$G\left(x\right)={\int }_0^{x}g\left(x\right)={\displaystyle \frac{1}{{\widehat{\beta }}^{\widehat{\alpha }}\mathsf{\Gamma }(\widehat{\alpha })}}{\int }_0^x{x}^{\widehat{\alpha }-1}{e}^{-x/\widehat{\beta }}dx$$

(6)

$$t\left(x\right)={\displaystyle \frac{x}{\widehat{\beta }}}$$

(7)

Substituting Equation (7) in Equation (6), the equation for the incomplete gamma function becomes Equation (8):

$$G\left(x\right)={\displaystyle \frac{1}{\mathsf{\Gamma }\left(\widehat{\alpha }\right)}}{\int }_0^t{t}^{\widehat{\alpha }-1}{e}^{-t}dt$$

(8)

Since the gamma function is undefined for $x$ = 0 and a rainfall distribution may contain zeros, the cumulative probability becomes Equation (9):

$$H\left(x\right)=q+\left(1-q\right)G\left(x\right)$$

(9)

$$q=m/n$$

(10)

where $m$ is the number of zero rainfall time series.

The SPI is derived by transforming the cumulative probability into a standardized normal distribution using Equation (11):

$$SPI={\Phi }^{-1}H\left(x\right)$$

(11)

where ${\Phi }^{-1}$ is the inverse standard normal cumulative distribution function.

In this study, the Gamma distribution was tested and found to fit generally well with the observed rainfall data (Figure S1). The fit of the Gamma distribution to the monthly rainfall in the Lower Mekong region in Cambodia is supported by several previous studies [19,30,31,32,33]. For more details on the SPI calculations, refer to McKee, Doesken [45]. The interpretation of the SPI values is the degree by which the observed rainfall anomaly deviates from the long-term mean. The SPI can reflect abnormally dry or wet conditions but cannot account for the effect of other climate variables, such as temperature and evaporation. The classification of drought based on SPI values is shown in

Table 1. Drought classification based on SPI values [32].

SPI Value	Description
Above 2.0	Extremely wet
1.5 to 2.0	Severely wet
1.0 to 1.5	Moderately wet
−1.0 to 1.0	Near normal
−1.5 to −1.0	Moderate drought
−2.0 to −1.5	Severe drought
Below −2.0	Extreme drought

.

3.2. Agricultural Drought Assessment

The Standardized Vegetation Index (SVI), developed by Peters, Walter-Shea [35], is an index used to assess agricultural drought by indicating vegetation failure or unhealthy conditions that may result from factors such as drought, flooding, crop rotation, or unseasonable coolness [35]. The SVI is calculated using the Normalized Difference Vegetation Index (NDVI), which represents vegetation greenness density. NDVI data are accessible from various satellite databases, including the SPOT series, which provides coarse-resolution data (250 m to 1 km) from 1998 to the decommissioning of SPOT-5 in 2015, as well as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS), which has been operational since 2000 and offers NDVI data at resolutions ranging from 250 m to 1 km.

Despite advancements in technology and the availability of newer instruments, older Advanced Very-High-Resolution Radiometer (AVHRR) data, with a resolution of approximately 4 km and available from 1981 to the present, remain a crucial and irreplaceable resource for historical land surface analysis. Studies such as those by Peters, Walter-Shea [35] and Tian, Brandt [47] have shown that NDVI data obtained from the AVHRR exhibit a strong correlation with vegetation conditions [35]. Consequently, NDVI data derived from the AVHRR were selected for use in this study due to their extensive temporal coverage, which is essential for long-term historical analysis.

The NDVI data were accumulated over the timescales of interest and transformed to normal distribution with zero mean and unit variance using Equation (12). The SVI was then estimated, based on the cumulative distribution function of the NDVI, as shown in Equation (13), where $P(Z<z_i)$ is the probability with the random variable (Z) and z_i is a deviation of the NDVI from the mean. The assumption of a normal distribution underlying the SVI calculation has been demonstrated to effectively capture variability in vegetation indices across diverse ecosystems. This standardization process has proven robust across various climatic regions [35], making it a widely accepted approach for comparing vegetation anomalies on a broad scale. Given this widespread validation, adopting the normal distribution for SVI calculation in this study was considered sufficiently sound. While the NDVI is a widely used index for assessing vegetation conditions, its limitation arises from its nature as a normalized ratio, which restricts relative comparisons across different pixel locations or time periods. The classification of vegetation health conditions based on the SVI is shown in

Table 2. Vegetation health condition classification based on SVI values [35].

SVI Value	Description
0.00 to 0.05	Very poor
0.05 to 0.25	Poor
0.25 to 0.75	Near normal
0.75 to 0.95	Good
0.95 to 1.00	Very good

.

$$Z_i={\displaystyle \frac{X_i-\overline{X}}{s}}$$

(12)

$$\mathrm{S}\mathrm{V}\mathrm{I}=P(Z<z_i)$$

(13)

3.3. Theory of Runs (ToR)

The Theory of Runs (ToR) [48] is a statistical approach applied to sequences for defining drought characteristics, including drought events (DEs) and the drought duration (DD), drought severity (DS), and drought intensity (DI). The ToR has been widely utilized in drought assessment [32,48,49] due to its ability to explain various aspects of drought through a straightforward graphical representation, as illustrated in Figure S2. A DE is defined as a period during which the value of a drought index consistently falls below a critical threshold. Once a DE is identified, the corresponding DD, DS, and DI are subsequently derived.

For the SPI, a DE is identified when SPI values are consistently below 0, with the lowest SPI value being less than or equal to −1. In the case of the SVI, a DE represents a period of poor vegetation conditions, potentially caused by drought, and is identified when SVI values are consistently below 0.5, with the lowest value being less than or equal to 0.25. The DD is defined as the number of months within a DE, while the DS is the sum of the absolute values of the index within the DE. The DI can be defined in two ways: either as the absolute lowest value of the index (DI1) or as the ratio of the DS to the DD within a DE (DI2).

4. Results

4.1. Meteorological Drought

Meteorological drought was assessed using the SPI at three distinct timescales: 3 months (SPI3), 6 months (SPI6), and 12 months (SPI12). The SPI3 and SPI6 analyses provide insights into seasonal rainfall variations and their impact on agriculture, while SPI12 is used to monitor the annual rainfall availability and variability, offering a longer-term perspective on precipitation trends.

4.1.1. Temporal Analysis of Standardized Precipitation Index

The values of SPI3, SPI6, and SPI12 for the three regions within the Baribo basin are presented in Figure 3. The regional average SPI, shown in Figure 3, was calculated using rainfall data from 12 stations and the Thiessen polygon method. The SPI values generally range between −3 and 3. Notably, SPI3 exhibits significant temporal fluctuations, reflecting alternating periods of dry and wet conditions. In contrast, SPI6 and SPI12, which assess drought over longer timescales, display less variability compared to SPI3. Drought events were identified in the Northern and Southern regions in 1989 and 1990, and across the entire Baribo basin during 1993–1994, 1997, and 2002–2006. Continuous wet periods occurred in all three regions from 1996 to 1997 and from 1999 to 2001. SPI values in the Northern region were generally lower than those in the other two regions, with the lowest values recorded in 1990 at −2.44 (SPI3), −2.44 (SPI6), and −2.38 (SPI12). The droughts in the three regions were notably longer and more frequent between 2002 and 2006 compared to earlier drought events.

4.1.2. Drought Characterization by ToR for Standardized Precipitation Index

Tables S1–S3 present the characteristics of each drought event (DE) in terms of the drought duration (DD), drought severity (DS), and drought intensity (DI1 and DI2) across the three timescales for the three regions, as analyzed using the Theory of Runs (ToR). The results indicate that the frequency of DEs decreases as the timescale increases. Droughts were most frequent in the Northern region, particularly at the SPI3 and SPI6 timescales, due to the high variability in the rainfall between the wet and dry seasons. Similar DE values across all regions suggest minimal variation in the annual rainfall over the Baribo basin.

As the timescale lengthened, the consecutive periods of negative SPI values also increased. Tables S1–S3 show that the longest DDs at the SPI3, SPI6, and SPI12 timescales were 9, 17, and 59 months in the Northern region; 13, 30, and 51 months in the Central region; and 15, 47, and 51 months in the Southern region, respectively. These extended drought durations predominantly occurred between 2002 and 2006. The Southern region experienced the longest DD, followed by the Central region at SPI3 and SPI6, which could lead to significant societal and economic consequences. The most prolonged drought was observed in the Northern region (59 months) when assessed using SPI12, reflecting the high variability in the rainfall typical of tropical zones. Similarly, Guo, Bao [32] reported that the longest DD in the Lower Mekong basin extended for approximately 38 months.

Regarding the DS, the maximum severity was consistently found in DEs with the longest DDs, and the DS increased with longer timescales. The most severe droughts for SPI3, SPI6, and SPI12 were 12.01, 17.83, and 43.03 in the Northern region; 10.77, 23.26, and 36.63 in the Central region; and 7.62, 28.19, and 31.41 in the Southern region, respectively. The severity of drought varied across regions and timescales.

The drought intensity decreased with the increasing timescale. The most intense droughts at all timescales were observed in the Northern region. According to Table 1, DI1 values of ≥2 indicate that extreme drought was prevalent in the Northern region, while the Central and Southern regions generally experienced droughts of moderate to severe intensity.

In summary, while all three regions—Northern, Central, and Southern—experienced droughts, the characteristics varied to different extents. When considering all the ToR parameters (DE, DD, DS, DI) together, it is evident that droughts in the Northern region were the most intense and frequent, albeit of shorter duration. In contrast, droughts in the Central and Southern regions were less intense and frequent but tended to last longer. Regarding the Ministry of Agriculture, Forestry and Fisheries (MAFF), short-term drought adaptation mechanisms have improved based on the development under the Rectangular Strategy of the RGC; however, the extended periods of drought are now the main concern for human welfare and food security in Cambodia [50]. Therefore, the prolonged duration of droughts in the Central and Southern regions, though they are less intense and frequent, seems to pose a growing threat to food security, highlighting the need for enhanced long-term adaptation strategies.

4.1.3. Impacts of Droughts on Cropping Patterns by Standardized Precipitation Index

Three months during the rice ripening period—March, July, and November, as illustrated in Figure 2—were selected to assess the impact of drought on rice production. Figures S3 and S4 present the SPI3 and SPI6 values for these months from 1985 to 2008. SPI3 was used to evaluate the impact of drought on the early-duration (ED) rice varieties, as it captures the variability in the rainfall over the entire cropping period. For instance, the SPI3 value for March reflects the accumulated rainfall from January to March, encompassing the full cropping period of ED1. SPI6 was employed to assess the impact of drought on the medium-duration (MD) and long-duration (LD) rice varieties, which require from four to five months and six months, respectively, to mature.

Figure S3 indicates that the ED rice varieties in the Northern region were more frequently and intensely affected by drought compared to those in the Central and Southern regions. Drought events typically occurred in July and November, with July being particularly dry and November marking the end of the rainy season. The ED rice varieties in all three regions were less affected by drought in March. Between 2002 and 2006, both the frequency and intensity of droughts impacting the ED rice varieties increased. Figure S4 further demonstrates that the effects of drought on the rice varieties in the Northern region were more pronounced than those in the Central and Southern regions. Notably, a negative SPI value in November was recorded for five consecutive years across all three regions during the period from 2002 to 2006, indicating widespread and prolonged drought impacts on the rice varieties. This likely resulted in significant damage to the production of the ED3, MD, and LD rice varieties.

The analysis above underscores that droughts inflicted severe damage on all three rice varieties between 2002 and 2006. Moreover, droughts were more frequent in November than in March and July. The November droughts were particularly detrimental to agricultural production, coinciding with the ripening of the three primary rice varieties (ED3, MD, and LD), which collectively account for over 60% of the total annual rice production. The SPI analysis identifies drought as a significant natural disaster contributing to food insecurity in Cambodia. This finding is consistent with data reported by the United Nations Development Programme (UNDP) (http://camdi.ncdm.gov.kh, accessed on 30 August 2024), which documented severe agricultural damage in Kampong Chhnang and Kampong Speu provinces between 2002 and 2006, with a maximum affected area of 78,000 hectares in 2004.

In summary, for the SPI analysis, droughts predominantly occurred between 2002 and 2006. Longer drought durations generally were associated with increased severity, although the intensity tended to decrease over time. The Northern region experienced the most frequent and intense drought events. In this region, droughts were shorter but more severe, while the Central and Southern regions faced less frequent but longer-lasting droughts. These droughts caused significant damage to all three rice varieties, with November droughts being especially harmful, as they coincided with the rice ripening period.

4.2. Agricultural Drought

Agricultural droughts were assessed using the SVI across three distinct timescales: 3 months, 6 months, and 12 months. The analyses of the SVI at 3 months (SVI3) and 6 months (SVI6) were employed to evaluate seasonal variations in the vegetation greenness density. The 12-month timescale (SVI12) was crucial for monitoring annual variations in the vegetation greenness density.

4.2.1. Temporal Analysis of Standard Vegetation Index

The SVI values at the 3-, 6-, and 12-month timescales are presented in Figure 4, with additional details on the drought characteristics provided in Table 2. An important aspect of the SVI is its ability to indicate poor vegetation conditions, which may result not only from droughts but also from floods and crop rotation.

Figure 4 illustrates that the vegetation conditions across the three regions were highly correlated. Periods of poor vegetation conditions were identified from 1988 to 1989, from 1992 to 1994, from 1999 to 2001, and from 2004 to 2006. The slightly improved conditions observed between 2004 and 2006 may be attributed to advancements in agricultural management, adaptation strategies, and the expansion of agricultural areas, which were prioritized as key policies by the Cambodian government beginning in July 2004. Notably, very poor vegetation conditions (SVI < 0.05) were not observed in the Northern region across any timescale. Vegetation conditions associated with longer timescales tended to deteriorate later but persisted longer compared to those associated with shorter timescales.

4.2.2. Drought Characterization by ToR for Standard Vegetation Index

Tables S4–S6 illustrate the properties of each drought event (DE) in terms of the drought duration (DD), drought severity (DS), and drought intensity (DI1 and DI2) across the three timescales in the three regions as analyzed using the Theory of Runs (ToR). The results indicate that the DE characteristics across all three regions were comparable at each timescale. Additionally, the numbers of drought events did not vary significantly between timescales. For instance, the numbers of drought events at SVI3, SVI6, and SVI12 were 6, 3, and 3 in the Northern region; 5, 3, and 3 in the Central region; and 5, 4, and 4 in the Southern region, respectively.

In terms of the DD, as shown in Tables S4–S6, the three regions exhibited similar DD values, with a slight increase observed from shorter to longer timescales. The DD lengths across all timescales in the Central region were comparable. However, in the Northern and Southern regions, the longest DD periods at SVI3 (DD = 23 and 28 months, respectively) were approximately half those observed at SVI6 (DD = 42 and 41 months, respectively) and SVI12 (DD = 43 and 43 months, respectively). The results also indicated that the longest DD across all three regions occurred between 1992 and 1995.

Tables S4–S6 reveal that the minimum drought severity (DS) occurred in different regions across the various timescales. The lowest DS values were observed in the Central region at SVI3 and SVI6 (DS = 1.3 and 2.46, respectively) and in the Southern region at SVI12 (DS = 3.55). These lower SVI values reflect reduced vegetation greenness densities. The findings suggest that the seasonal greenness density in the Central region was poorer compared to those of the other two regions, while the overall annual greenness density was the lowest in the Southern region.

Typically, the SVI value ranges between 0 and 1, representing the probability of vegetation greenness density. As shown in Tables S4–S6, very poor vegetation conditions (DI1 ≤ 0.05) were observed in the Central and Southern regions. Such poor vegetation conditions were not found in the Northern region, where the lowest SVI values across the three timescales ranged from 0.06 to 0.07. The vegetation greenness densities in all three regions between 2004 and 2006 were less severely impacted compared to previous drought events. For DI2, the values across the three regions and timescales showed slight variation, indicating that the Northern region experienced better vegetation conditions than the Central and Southern regions.

The poor vegetation conditions indicated by the SVI may have been caused by either floods or droughts. The conditions observed from 1988 to 1989, from 1992 to 1994, from 1998 to 1999, and from 2004 to 2006 are likely attributable to drought, as the low SVI values during these periods correspond with negative SPI values. Conversely, the poor vegetation conditions from 1999 to 2001 were possibly due to flooding, as positive SPI values were recorded during this time. Similarly, a study by Ly, Kim [51] reported that a major flood occurred in the Lower Mekong River in 2000.

4.2.3. Impact of Drought on Cropping Pattern by SVI

Figures S5 and S6 illustrate drought maps for March, July, and November, corresponding to SVI3 and SVI6, respectively, to evaluate the vegetation greenness density during the cropping period. The SVI3 data indicate poor vegetation conditions across all three selected months (March, July, and November) in both 1994 and 2000 as a common period. However, the years with poor vegetation conditions vary depending on the month. Poor vegetation conditions in March are identified in 1989, 1992, 1993, and 2004. For July, poor vegetation conditions are found in 1993, while for November, they are detected in 1988. The ED rice varieties showed low greenness density in March, July, and November, with a higher frequency of occurrence in March. This may be attributed to the fact that ED1 is typically planted during the dry season, when rice crops rely heavily on diminishing irrigation supplies. As a result, increased water stress during this period can lead to lower greenness density.

The analysis of the LD and MD rice varieties focused on November, as these varieties are cultivated once a year and typically ripen during this month. The SVI6 data revealed poor and very poor greenness densities for the LD and MD rice varieties in 1988, 1993, 1994, and 2000. From 2001 to 2008, these low greenness densities were not observed, although the SVI values remained below zero in 2005 and 2006. In summary, all three rice varieties were substantially damaged in 1988, 1993, 1994, and 2000, with ED1 exhibiting the lowest greenness density among them.

In summary, the analysis of drought using the SVI at the 3-, 6-, and 12-month timescales revealed similar drought events and durations across the Northern, Central, and Southern regions. While the Central region had poorer seasonal greenness density and the Southern region showed the lowest annual greenness, the Northern region maintained better vegetation conditions and did not experience very poor vegetation, unlike the other regions. The ED rice varieties showed low greenness densities, particularly in March, likely because they are planted during the dry season, when irrigation is limited.

4.3. Drought Distribution

Figure 5 and Figure 6 illustrate the distributions of the droughts and vegetation greenness densities across the Baribo basin, respectively. It is important to note that agricultural areas are predominantly located in the eastern parts of the Northern and Central regions and in nearly the entire Southern region [39]. Four DEs were selected to develop the drought map using the SPI and SVI at the 3-, 6-, and 12-month timescales. The selected DEs correspond to months where the SPI indicated at least a severe drought (SPI ≤ −1.5) or the SVI indicated very poor vegetation conditions (SVI ≤ 0.05). The four selected DEs occurred in November 1993, April 1994, August 2000, and November 2004. The following describes the distribution of drought across the entire Baribo basin.

Figure 5 presents the drought distribution map based on the SPI across the Baribo basin. The color gradient on the map represents varying SPI values, with the transition from yellow to red indicating increasingly dry conditions, and that from light to dark blue signifying progressively wetter conditions. Near-normal conditions are depicted in shades of yellow to green. In November 1993 and April 1994, drought conditions affected a large area of the Northern and Central regions. At the same time, the Southern region experienced mostly slight drought (0 < SPI < −1), with moderate drought conditions occurring only in the Northern part of this region. By August 2000, the entire Baribo basin received high rainfall, except for the eastern part, which is the low-lying area near the Tonle Sap Great Lake. In November 2004, all three timescales indicated that drought conditions were primarily concentrated in the Central part of the basin. The analysis of the four selected events showed that drought occurred in the whole Baribo basin; however, the Central region generally experienced a higher drought intensity.

Figure 6 shows the map of the vegetation conditions across the entire basin, based on SVI values at the selected timescales. Different colors on the map represent various SVI values: orange and red indicate poor and very poor conditions, respectively, while light blue and dark blue represent good and very good conditions. Near-normal conditions are shown in green. In November 1993, the greenness density of vegetation in the eastern part of the basin, where it connects to the Tonle Sap Great Lake, was higher than that in other areas. Poor and very poor vegetation conditions were widespread across almost the entire basin in April 1994 and August 2000, with the Central region being the most severely affected. In November 2004, poor vegetation was primarily concentrated in the eastern part of the basin, the low-lying region near the Tonle Sap Great Lake. In summary, the results indicate that the entire basin experienced both flood and drought conditions, with the Central region being the most severely impacted. Notably, the SPI and SVI results suggest that the poor vegetation in 2000 was caused by floods, and the Central region of the Baribo basin faced a higher drought intensity as well as low greenness density than the Northern and Southern regions.

In summary, the spatial analysis indicates that the Central part of the Baribo basin consistently experienced higher drought intensity and lower vegetation greenness compared to the Northern and Southern regions. When some poor vegetation conditions were related to flooding, drought was the dominant driver of the reduced agricultural productivity in the basin. Consequently, these findings underscore the need for drought mitigation strategies to safeguard agricultural productivity and food security.

5. Discussion

The SPI and SVI indices are effective tools for evaluating the impact of drought on agriculture, particularly because they can be calculated at the 3- and 6-month timescales, which align with the cropping periods of the three rice varieties. The SPI assesses the availability of rainfall for the LD, MD, and ED rice varieties during their cropping periods, with negative SPI values indicating insufficient rainfall to meet the crop water requirements. The meteorological drought assessment using the SPI revealed that the Northern region, in particular, experiences the most profound impact, with the highest frequency of drought events (DEs), the greatest drought intensity (DI), and the most severe drought severity (DS) observed across most timescales. In contrast, the Central and Southern regions experience less intense and frequent droughts but longer drought durations (DDs). The SPI identified several drought events throughout the study period, with the droughts occurring between 2001 and 2006 being more severe than those in earlier years of the study period.

The assessment of the vegetation conditions using the SVI reveals a high correlation between the vegetation characteristics across the Northern, Central, and Southern regions. The SVI at the 3- and 6-month timescales effectively captures the greenness density of crops during the growing period. Specifically, SVI values below 0.25 during these periods suggest poor greenness density, indicating that crops may be unhealthy or have failed. This finding is consistent with periods of drought when low SVI values coincided with negative SPI values, indicating reduced precipitation.

However, an interesting discrepancy was noted between 1999 and 2001, when poor vegetation conditions were observed despite the positive SPI values, which suggest the occurrence of floods. This indicates that while the SPI effectively detects drought conditions, it may not capture vegetation stress caused by flooding, such as waterlogging. This highlights the need for complementary indices to assess vegetation health under both drought and flood conditions.

One notable limitation of the SVI was its failure to detect the severe drought of 2004, which caused significant damage to rice production. While drought conditions were widespread, the SVI indicated low greenness density primarily in the eastern part of the Baribo basin, a rice-growing region. This discrepancy may be explained by the expansion of irrigated areas upstream, as part of the RGC development plan, which likely increased the greenness density and masked the full impact of the drought. However, further investigation is needed to confirm whether irrigation efforts during this period were sufficient to offset the drought’s effects.

It is also important to note that low SVI values were observed before the SPI values dropped, as shown in Figure 5 and Figure 6. This early decline in the SVI can likely be attributed to cropping patterns, where a post-harvest period (Figure 2) of several weeks occurs between harvesting rice and planting the next crop. This leads to a temporary reduction in the greenness density, even before rainfall deficits are recorded by the SPI. This finding emphasizes the importance of considering crop calendars and agricultural practices when interpreting SVI data, as short-term fluctuations in vegetation indices may not always reflect water scarcity but rather agricultural cycles. While the SVI is a valuable tool for assessing vegetation health and identifying drought conditions, its limitations in capturing flood impacts and agricultural dynamics should be considered. Additional indices or more variables, such as soil moisture or evapotranspiration rates, may be considered to enhance the accuracy of drought and flood assessments.

This section aims to highlight the impact of drought on food security. Figure 7 illustrates the annual rice yield, average annual yield, and damaged area from 1994 to 2012 (excluding missing data for 1996, 2007, and 2010) in Kampong Speu province, which partially covers the Baribo basin (Figure 1). Data obtained from the MAFF were used to assess the relationship between drought and rice yields, as well as to estimate the extent of damaged areas. Figure 7 shows that the annual average rice yield was approximately 2 tons per hectare (t/ha). Between 1994 and 2008, rice yields were consistently lower than the annual average, except in 1997 and 2006. However, rice yields increased significantly from 2009 to 2012, ranging between 2.4 and 3.14 t/ha. The lowest rice yield was recorded in 2008 (1.19 t/ha), followed by 1994 (1.52 t/ha) and 2004 (1.57 t/ha). Regarding damaged areas, the data reveal that drought affected about 46% of the total rice paddy fields in 2004 and approximately 18% in 1994, 1997, and 1998. Drought also occurred in 1995, 2002, and 2006, but the affected area was less than 6%. The data suggest that the lowest annual rice yield in 2008 was not caused by drought, as no evidence of drought-related damage was found. Rice production was significantly impacted by drought in 1994, 1997, 1998, and 2004, with the most severe damage occurring in 2004.

The SPI results captured varying drought intensity and severity in 1994, 1997, 1998, and 2004. However, the rice yield remained relatively stable at around 1.5 t/ha. This suggests that the drought intensity and severity had a less significant impact on the rice yield compared to the drought duration. The SPI indicated that the drought duration from 1993 to 1994 and from 1997 to 1998 was significantly shorter than that from 2002 to 2006. Concurrently, the area affected by drought was approximately 18% in each of the years 1994, 1997, and 1998, while it was about 46% in 2004. The SVI identified poor greenness density in 1993, 1994, 1997, and 1998, but it showed only slightly poor greenness density between 2002 and 2006. This study aligns with the findings of the UNDP [52], which reported that variations in the annual rainfall (drought intensity) do not necessarily correlate with the extent of drought damage. For example, the UNDP [52] highlighted Kampong Speu province, where paddy production suffered its greatest loss in 2004, with 38,257 hectares of cultivated land affected, despite receiving 921 mm of rainfall. In contrast, 1997 recorded the lowest rainfall at 770 mm, yet the damage was less severe, affecting 14,962 hectares of cultivated land. The duration of absent rainfall (drought duration) appeared to have a greater impact on rice production losses compared to other drought characteristics.

Beyond the study period (1985–2008), drought has continued to be a major hazard that significantly impacts livelihoods, particularly in agriculture, livestock, and water resources, while also posing lingering and sometimes deadly threats to human health [53]. According to the UNDRR [54], Cambodia faces significant disaster risks, especially from floods and droughts, which could reduce its GDP by up to 10% by 2050. Recognizing the severity of these risks, the Royal Government of Cambodia (RGC) has made climate change a central focus of its national policy, as reflected in the Rectangular Strategy. This strategy prioritizes water resource management to mitigate the impacts of both floods and droughts, while also ensuring long-term water security [55].

A critical component of the Rectangular Strategy is its emphasis on integrating sustainable agricultural practices, improving irrigation systems, and strengthening water resource management, which are vital for reducing the country’s vulnerability to prolonged droughts. The strategy also underscores the importance of land tenure security, access to credit, and infrastructure development in enabling farmers to better cope with the economic and environmental challenges posed by drought. Cambodia demonstrated its commitment to addressing these challenges by being one of the first countries to submit a National Adaptation Programme of Action to the UNFCCC in 2006. Furthermore, in 2013, the RGC launched the Climate Change Strategic Plan 2014–2023, outlining a long-term vision for climate-smart development [56]. While the MAFF [50] has reported improvements in short-term drought adaptation, extended drought periods remain a significant threat to human welfare and food security. This is particularly true in the Baribo basin, where our study area is located. Therefore, a comprehensive drought management plan and targeted mitigation strategies are essential to minimize the socioeconomic and environmental impacts of these prolonged droughts.

To improve the effectiveness of the Rectangular Strategy in mitigating drought impacts in the Baribo basin, several targeted policy adjustments are necessary based on the findings from the Standardized Precipitation Index (SPI) and Standardized Vegetation Index (SVI). Both indices underscore that drought is a significant natural hazard contributing to food insecurity in this region, where meteorological and agricultural droughts frequently coincide. First, in response to the observed agricultural droughts as indicated by the SVI, it is critical to promote drought-resilient crops and diversified farming systems tailored to the region’s specific climatic conditions. These initiatives would help reduce crop failure rates, particularly during extended droughts, thereby mitigating the impacts on food security.

Additionally, the findings from the SPI highlight the importance of improving surface water management and enhancing the efficiency of existing irrigation systems to better withstand meteorological droughts. The reliance on rainfall and surface water for agriculture, as evidenced in the Baribo basin, means that more efficient water allocation and storage systems, such as rainwater harvesting and small-scale reservoirs, are crucial for mitigating drought impacts. Furthermore, the integration of early warning systems that use SPI-based meteorological forecasts will help predict drought onset and severity, enabling timely interventions that can reduce the adverse effects on crop productivity and food security. There are potential improvements in the use of drought indices that could enhance future monitoring. Incorporating newer data sources such as remote sensing technologies, satellite-based soil moisture measurements, and vegetation health indices could offer more accurate, real-time monitoring of drought conditions. For instance, integrating the NDVI from satellite data for monitoring changes in water storage could refine both SPI and SVI assessments. These advancements would provide a more comprehensive understanding of drought onset, duration, and intensity, enabling more timely and targeted interventions.

The study’s results suggest that while short-term adaptation mechanisms have been effective in the past, the increasing frequency and duration of droughts necessitate more proactive measures. This underlines the need for a comprehensive national drought contingency plan that incorporates SPI- and SVI-based monitoring. Such a plan would help shift the focus from reactive relief efforts to more sustainable, long-term drought mitigation strategies, tailored to the specific vulnerabilities identified in the Baribo basin. Additionally, building local farmers’ capacity through training in sustainable water management and agricultural practices, informed by the trends observed in the SPI and SVI, will be crucial for enhancing the resilience of the agricultural sector to future droughts.

6. Conclusions

The SPI and SVI proved effective at evaluating meteorological and agricultural droughts in this study. These indices indicate that the Baribo basin has been severely impacted by drought. Based on the analysis using the SPI and SVI, the highest drought intensity and severity in the Baribo basin occurred in 1993 and 1994, while the area experienced severe flooding in 2000. The drought duration significantly increased between 2001 and 2006. Droughts are a major hazard in Cambodia, leading to food insecurity. The most severe damage to rice production occurred in 2004, affecting about 46% of the total cultivated area. The drought intensity (DI) and drought severity (DS) had a less significant impact on the rice yields, whereas the drought duration (DD) had a strong correlation with the extent of damaged rice areas. Rice production significantly increased between 2005 and 2012, reflecting the effectiveness of the Royal Government of Cambodia (RGC)’s Rectangular Strategy for improving agricultural management, as well as adaptations to boost milled rice exports and support food security policies. Despite advancements in short-term drought adaption mechanisms, prolonged droughts continue to pose a significant danger to human welfare and food security in Cambodia. Consequently, it is imperative to establish a thorough drought management plan and mitigation techniques nationwide. This is especially critical for mitigating the effects of extended droughts in the Southern and Central portions of the Baribo basin, as delineated in our study area.