Table 3 shows average annual SPI values for the last 40 years (1982-2022). All the six study sub counties show minimum high negative average annual SPI values less or equal to -2 demonstrating extreme dryness through the entire region.
Table 4 show annual maize yields for the six sub counties studies for the years 2022. All the sub counties show 3 tons/ha which is less than the average maize yields for the entire country between 4-9 tons/ha depending on the variety and environmental characteristics (
Wakabi, 2016).
Table 5 shows annual average SPI values for six study sub counties of Isingiro district. Severity of meteorological drought increases in order: Masha -2.57 > Mbaare -2.40 > Kikagate -2.28 > Isingiro Town Council -2.26 = Rushasha -2.26 > Kashumba -2.05.
Fig 2 presents variations of SPI values for Isingiro Town Council from 1982-2022 divided in two parts: -1982-2002 (to the left); 2003-2022 (to the right). For the years 1998 to 2002, 1982 to 1986, 1998 to 2002, Isingiro Town Council experienced meteorological drought, as depicted by the negative SPI values -1 to -2.8. Wet years that are likely to have experienced some amount of rainfall include: 1987-1991,1996-1997 and extremely wet years that might have received heavy rains include 2020-2022.
Fig 3 presents variations of SPI values for Rushasha Sub County from 1982-2022 divided in two parts: -1982-2002 (to the left); 2003-2022 (to the right). Negative SPI values were obtained for the years 1982-1986, 1990, 1992-1994,1998-2002, 2003,2006,2016-2018 where the sub county experienced meteorological drought and wet years where the area might have obtained more rains include: 1983,1987-1991, 19951997, 2007-2012. Heavy rains and extreme wet years include 2020-2022.
Fig 4 presents variations of SPI values for Kashumba Sub County from 1982-2022 divided in two parts: -1982-2002 (to the left); 2003-2022 (to the right). The sub county experienced meteorological drought during the years 1982-1986,1992-1994,199-2000 and 2006 where negative SPI values were obtained. On the other hand, rainfall and wet years with positive SPI include 1987-1991,1996-1997,2004-2005,2007-2018. Heavy rains and extreme wet years include 2020-2022.
Fig 5 presents variations of SPI values for Kikagate Sub County from 1982-2022 divided in two parts: -1982-2002 (to the left); 2003-2022 (to the right). Negative SPI values were obtained during 1982-1986,1992-19941999-2002,2003 and 2006 for Kikagate Sub County. Positive SPI values and most likely rainfall was obtained during the years: -1987-1991,1986-1997,2004-2005,2007-2019. Heavy rains might have been received resulting in extreme wetness for the sub county during 2020-2022.
Fig 6 presents variations of SPI values for Masha Sub County from 1982-2022 divided in two parts: -1982-2002 (to the left); 2003-2022 (to the right). For Masha Sub County, meteorological drought was obtained during 1982-1986,1992-1995,1986-2006 where negative SPI values were obtained. Positive SPI values were obtained and most likely some rainfall during 183,1987-1991,1995-1997, 2008-2016 in the Sub County. Heavy rains and extreme wetness were obtained during 2020-2022.
Fig 7 presents variations of SPI values for Mbare Sub County from 1982-2022 divided in two parts: -1982-2002 (to the left);2003-2022 (to the right). The sub county experienced meteorological drought with negative SPI values during 1982-1986,1992-1994,1999-2006,2016-2019. Positive SPI values indicating wetness and some rains were obtained for the years 1983, 1987-1991,1995-1997,2007-2015. Heavy rains and extreme wetness were obtained during the years 2020-2022.
Table 6 presents annual rainfall and temperatures for the six study sub counties. Generally, the annual rainfall is low and annual temperatures are high for all the sub counties thus making them most likely to be less suitable for optimum maize productivity.
Fig 8 shows the SPI and rainfall variability for six sub-counties where the study was conducted. Over the last four decades, Kikagate, Isingiro Town Council, Masha and Ngarama obtained negative SPI values between -1.5 to -1.49 indicating vulnerability to meteorological drought of the region. The same sub counties experienced annual rainfall between 1011 mm-1091 mm. To understand better rainfall variability and Sub Counties vulnerable to meteorological drought in Isingiro District, ArcGIS10 software was used and standard procedures were followed based on previous experts to produce a standard map
(Mondol et al., 2017).
To get more understanding of the region, further analysis using QGIS 3.4 revealed that the study sub counties were vulnerable to drought (2001-2015) which has a negative implication on maize productivity in the region.
Fig 9 shows drought vulnerability 2001-2015 for the study sub counties in Isingiro district. More than half the land cover for Mbaare, Kashumba, Ngarama, Kikagate and Isingiro Town Council is vulnerable to prolonged drought and the landscape is degraded to a large extent.
Mapping and monitoring
SPI values can be mapped to show areas experiencing drought or surplus conditions, helping policy makers, water managers and other stakeholders to monitor drought conditions and make informed decisions regarding water resource management, agriculture and disaster preparedness. Overall, the SPI is a valuable tool for drought monitoring, as it provides a standardized measure that can be easily interpreted and compared across different regions and time periods.
Results agree with previous findings that Standardized Precipitation Index (SPI) can have a significant effect on maize productivity, as maize is highly sensitive to water availability throughout its growth cycle. SPI can impact maize productivity as follows:
Drought stress
Negative SPI values indicate drier than average conditions, which can lead to drought stress for maize plants. Insufficient moisture during critical growth stages such as germination, flowering and grain filling can result in reduced plant growth, lower kernel development and ultimately decreased yields.
Water stress during critical growth stages
Maize requires adequate water during critical growth stages to optimize yield potential. If SPI indicates prolonged periods of below-average precipitation during these stages, it can lead to water stress and reduced productivity
(Fisher et al., 2015).
This study is in agreement with this study previous researchers reported that in regions where SPI indicates delayed onset of the rainy season or prolonged dry spells, maize planting may be delayed. Delayed planting can lead to shorter growing seasons, reduced time for crop development and lower yields. Additionally, reduced moisture availability during germination can lead to poor seedling establishment and lower overall plant populations
(Fisher et al., 2015).
Impact on soil moisture availability
SPI reflects not only current precipitation but also precipitation over longer timescales. Prolonged negative SPI values can lead to depletion of soil moisture, further exacerbating water stress for maize plants, especially in rain fed agricultural systems
(Steward et al., 2018a).
Additionally, this study agrees with previous researchers who stated that farmers and agricultural planners often use SPI data to inform decision-making regarding crop selection, planting dates, irrigation scheduling and other management practices. In regions where SPI indicates potential drought conditions, farmers may implement drought-tolerant maize varieties, adopt conservation tillage practices to conserve soil moisture, or invest in irrigation infrastructure to supplement water availability
(Tesfaye et al., 2018).
The study agrees with previous studies which pointed out that SPI variability can lead to yield variability from one growing season to another. This variability can have significant economic implications for maize farmers, affecting their income and livelihoods. In regions heavily reliant on maize production for food security and economic stability, fluctuations in maize productivity due to SPI variations can have far-reaching consequences (
Msowoya and Madani, 2016). Overall, the SPI serves as a valuable tool for assessing the potential impact of precipitation variability on maize productivity, helping farmers and policymakers develop strategies to mitigate the adverse effects of drought and optimize yields in maize-growing regions
(Okal et al., 2020).
This research agrees with previous studies which reported that Standardized Precipitation Index (SPI) on maize productivity has been widely studied, as maize is one of the most important cereal crops globally. Drought Stress and Yield Reduction: Negative SPI values, indicating drought conditions, have been consistently linked to reduced maize yields. Studies have shown that drought stress during critical growth stages such as flowering and grain filling can significantly decrease maize productivity
(Tao et al., 2018; Yosef et al., 2021). On the contrary, Positive SPI values, indicating surplus precipitation, can also influence maize productivity. However, excessive moisture can lead to waterlogging and nutrient leaching thus negatively impacting maize growth and yield
(Jain et al., 2019).
Temporal variability
SPI can exhibit temporal variability, with different timescales (
e.g., monthly, seasonal) influencing maize productivity differently. For instance, prolonged dry spells during specific growth stages may have a more pronounced effect on yield compared to overall seasonal precipitation deficits
(Gbegbelegbe et al., 2017).
At the same time, the impact of SPI on maize productivity varies across different regions due to differences in climate, soil characteristics and farming practices. Studies have highlighted the importance of local-scale assessments to understand the specific effects of SPI on maize yields in different agro ecological zones
(Zhang et al., 2020).
Adaptation strategies
Farmers often employ various adaptation strategies to mitigate the adverse effects of SPI on maize productivity. These may include crop diversification, adoption of drought-tolerant maize varieties, improved water management practices and investment in irrigation infrastructure
(Funk et al., 2019; Alhassan et al., 2020). Modeling Approaches: Statistical and modeling approaches integrating SPI data with crop growth models have been used to assess the relationship between precipitation variability and maize productivity. These studies provide valuable insights into the potential impacts of future climate scenarios on maize yields
(Ruane et al., 2018).
The findings of this study are in agreement with previous studies which reported that prolonged drought degrades landscape hence threatening food security of different regions
(Araya et al., 2015; Katengeza et al., 2019; Steward et al., 2018b). Additionally, the majority of the population in Isingiro depend on agriculture for their livelihoods and meteorological drought negatively affects their crop production hence their livelihoods
(Twongyirwe et al., 2019). Extreme drought and land degradation result in households increased famine and prolonged shortage of food causing widespread diseases and deaths from starvation
(Mulinde et al., 2019; Surendra et al., 2012). The findings also agree with the District Multi-hazard assessment report conducted by the Prime minister’s office which concluded that “the most affected sub-counties by drought were Kikagate and Masha, Rugaaga, Kashumba, Isingiro TC and Ngarama in that order of severity” (
NECOC, 2017). Additionally, household food shortage has enhanced land conflicts between locals and refugees, resulting into migration of people from Isingiro to Tanzania to look for food and livestock feeds for their households (
NECOC, 2017) thus exacerbating conflicts between the two countries.
Moreover, this study agrees with previous researchers who pointed out that prolonged drought has been the major threat to food and income insecurity among smallholder farmers in Isingiro District
(Twongyirwe et al., 2019). Previous scholars classified drought into three categories namely.
Meteorological drought; Agricultural drought and Hydrological drought
(Shobanadevi et al., 2022). The first category is the precursor for the rest and all of them negatively affect crop production and smallholder farmers’ livelihoods. Prolonged drought affects the social, economic and food security of smallholder farmers in most developing counties. It negatively affects the hydrology, soil water, relative humidity, temperature, soil structure and species diversity (
Benton and Newell, 2014;
Lu et al., 2017; Segurado et al., 2016; Woods et al., 2008).
Furthermore, prolonged drought affects agricultural activities such as crop productivity, livestock, fisheries and apiary hence peoples’ livelihoods in various countries
(Azadi et al., 2018). This is exacerbated by smallholder farmers’ high poverty levels and technical inability to mitigate the impacts of prolonged drought in their regions. This study also agrees with previous studies which linked land degradation, drought and desertification with poor socioeconomic development and human welfare
(Mariano et al., 2018). Most of the landscapes affected by drought have also been reported to be degraded hence not suitable for supporting crop production and peoples’ livelihoods
(Webb et al., 2017). Mapping meteorological drought vulnerability and landscape degradation are pertinent for policy review and formulation thus supporting Uganda’s sustainable developmental objectives and motivation for small holder farmers to look for drought-resilient varieties to increase crop productivity
(Shobanadevi et al., 2022). Similarly, previous researchers linked drought with climate change, water resources management and food security in Isingiro District
(Nagasha et al., 2019; Twongyirwe et al., 2019; Zziwa et al., 2015).
