Assessing climate change and adaptive capacity at local scale using observed and remotely sensed data

Weather and Climate Extremes 26 (2019) 100240

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Assessing climate change and adaptive capacity at local scale using observed and remotely sensed data S. Mpandeli a, g, L. Nhamo b, f, *, M. Moeletsi c, h, T. Masupha c, J. Magidi d, K. Tshikolomo e, S. Liphadzi a, D. Naidoo a, T. Mabhaudhi f a

Water Research Commission, 4 Daventry Street, Lynnwood Manor, Pretoria, 0081, South Africa International Water Management Institute, 141 Cresswell St, Silverton, 0184, Pretoria, South Africa Agricultural Research Council, Institute for Soil, Climate and Water (ISCW), Pretoria, 0001, South Africa d Tshwane University of Technology, Geomatics Department, Pretoria, 0001, South Africa e Limpopo Department of Agriculture and Rural Development (LDARD), Polokwane, 0700, South Africa f University of KwaZulu-Natal, School of Agricultural, Earth and Environmental Sciences, Centre for Transformative Agricultural and Food Systems (CTAFS), Pietermaritzburg, 3209, South Africa g University of Venda, School of Environmental Sciences, Thohoyandou, South Africa h University of Limpopo, Risk and Vulnerability Assessment Centre (RVAC), Limpopo, 0727, South Africa b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Adaptation Drought Agriculture Smallholder farming Resilience Risk reduction

Climate variability and change impacts are manifesting through declining rainfall totals and increasing frequency and intensity of droughts, floods and heatwaves. These environmental changes are affecting mostly rural pop­ ulations in developing countries due to low adaptive capacity and high reliance on natural systems for their livelihoods. While broad adaptation strategies exist, there is need to contextualise them to local scale. This paper assessed rainfall, temperature and water stress trends over time in Capricorn District, South Africa, using Standardized Precipitation Index, Thermal Heat Index, and Normalised Difference Vegetation Index (NDVI) as a proxy of water stress. Observed rainfall and temperature data from 1960 to 2015 was used to assess climatic variations, and NDVI was used to assess water stress from 2000 to 2019. Results show a marked increase in drought frequency and intensity, decreasing rainfall totals accompanied by increasing temperatures, and increasing water stress during the summer season. Long-term climatic changes are a basis to develop tailor-made adaptation strategies. Eighty-one percent of the cropped area in Capricorn District is rainfed and under small­ holder farming, exposing the district to climate change risks. As the intensity of climate change varies both in space and time, adaptation strategies also vary depending on exposure and intensity. A combination of observed and remotely sensed climatic data is vital in developing tailor-made adaptation strategies.

1. Introduction Climate change is the greatest threat facing humankind as it is the main cause of the increasing frequency and intensity of extreme weather events such as droughts, floods, heatwaves and cyclones being experi­ enced today (Hales et al., 2003; UNGA, 2012). Of great concern are shifting weather patterns that are impacting food systems and the rising sea levels that are increasing the risk of flooding (UNGA, 2015). Without drastic action today to curb greenhouse gas (GHG) emissions that are the major cause of global warming, adapting to these impacts in the future will be more difficult and costly, and the consequences could be dire (Nhamo et al., 2019c; UNGA, 2015). For example, climate change has

already modified agricultural systems throughout the globe, contrib­ uting to a decline of between 1% and 5% of agricultural production in the last 30 years (Porter et al., 2014). The trend is projected to continue if no action is taken to reduce GHG emissions to acceptable levels (Niang et al., 2014). Southern Africa is anticipated to suffer the most as about 60% of its population live in marginalised rural areas, depending on rainfed agriculture and relying on natural systems for their livelihoods (Nhamo et al., 2019c). Reliance on these climate sensitive sectors ex­ poses the region to the vagaries of climate change and other vulnera­ bilities (Niang et al., 2014). Agricultural productivity is declining, causing severe economic impacts, as agriculture contributes about 17% of regional GDP (increasing to above 28% when middle-income

* Corresponding author. International Water Management Institute, 141 Cresswell St, Silverton, 0184, Pretoria, South Africa. E-mail addresses: [email protected], [email protected] (L. Nhamo). https://doi.org/10.1016/j.wace.2019.100240 Received 14 August 2019; Received in revised form 28 October 2019; Accepted 12 November 2019 Available online 14 November 2019 2212-0947/© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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countries are excluded) and contributing about 13% of the total export value (SADC, 2014; SADC, 2015). Climate change induced shifts in agro-ecological zones in southern Africa are severely affecting agricultural production, as some crops no longer do well under the prevailing harsh conditions (Mabhaudhi et al., 2019; Niang et al., 2014). These shifts require communities to adapt to the changing environment. However, adaptation varies from place to place depending on socio-economic and geographic factors, and that each community normally has its own coping and adaptation mecha­ nisms, until when it is stretched to the limit and would need external support (Chinowsky et al., 2011; Nhamo and Chilonda, 2012; Tripathi and Mishra, 2017). Global adaptation strategies proposed in literature do not always work at community or local level, therefore, policy makers opt for tailor-made strategies that are viable per geographic area (Nguimalet, 2018; Nkoana et al., 2018). The challenge for decision-makers is to assess and identify the socio-economic and geographic characteristics at local level in order to design specific adaptation strategies for individual communities (Brooks and Adger, 2005; Nhamo and Chilonda, 2012). The Household Economic Approach (HEA) has been widely used to assess vulnerability and the impacts of shocks at household and community levels, but it is a generally costly and time-consuming process (Holzmann et al., 2008). However, the use of historical climatic data and remotely sensed geo-information in assessing climate change impacts are a time and cost effective means of developing tailor-made interventions, coping and adaptation strategies at local level. Particularly, remote sensing products are useful for assessing and monitoring the impacts of extreme shocks (drought, floods, cyclones, heatwaves) in near real-time, providing evidence-based intervention options (Chaney et al., 2015). The spatio-temporal characteristics of remote sensing are the main reason for the advances in environmental studies (Suepa et al., 2016; Weigand et al., 2019). Long-term and frequent satellite observations facilitate the monitoring of changes occurring in key biophysical attri­ butes like phenological characteristics, and associating them to climate change by examining their correlations (Suepa et al., 2016). The Moderate-resolution Imaging Spectroradiometer (MODIS) has been successfully used to monitor ecosystem dynamics with apt spatio-temporal resolutions, significantly improving geometric and radiometric properties (Zhang et al., 2006). The Enhanced Vegetation Index (EVI) and the Normalised Difference Vegetation Index (NDVI) are being used to (i) enhance the vegetation signal with improved sensitivity in high biomass regions, (ii) reduce atmospheric and soil effects, and (iii) reduce the impact of smoke from biomass burning (Xiao et al., 2009). Thus, remote sensing is an important tool for assessing climate change and the impact on agricultural production over time, which are neces­ sary for developing context based adaptation strategies. Climate change is modifying rainfall patterns and seasons, causing shifts in agro-ecological zones, and thus, impacting on agricultural productivity (Davis and Vincent, 2017). Some evident climatic changes include increased evapotranspiration and heatwaves due to increasing temperatures, and the increasing frequency and intensity of drought (Davis and Vincent, 2017). The changes are affecting the production of certain types of crops that do not favour hash climatic conditions, and are a source of the spread of pests and diseases, reduced crop yields, and cause shift in optimum growing regions and periods (Nhamo et al., 2019c). The challenge requires urgent measures, particularly in devel­ oping countries, to reduce vulnerability and build resilience, mainly in the agriculture sector through a time series analysis of both remotely sensed and observed data. Climate change impacts and responses are varied as determined by adaptation capacity, intervention mechanisms, effectiveness of avail­ able early warning systems, vulnerability levels, governance and insti­ tutional arrangements and scenario planning strategies (Nhamo et al., 2019c). This study assessed changes in climate over time in the Capri­ corn District in Limpopo Province, South Africa, using observed climatic and remotely sensed data. The analyses allowed an understanding of the

challenges faced by smallholder farmers in the district, which allowed to develop context specific adaptation strategies for the district. The aim was to provide evidence to policy and decision makers on coping and adaptive interventions at local level, and build resilience. Climate change is projected to reduce agriculture land by almost 15%, a scenario that would impact on the country’s food security (Cai et al., 2017; DEA, 2013; Pienaar, 2013; Ziervogel et al., 2014). 2. Materials and methods 2.1. The study area Capricorn District is the central most district of Limpopo Province (Fig. 1), and is dominated by a generally flat topography with isolated mountains. Capricorn District is the provincial centre for economic develop­ ment, as it is home to the provincial capital of Polokwane. It has an area of about 21 700 km2 and a population of over 1 330 436 people (Stats-SA, 2015). Its climate is semi-arid, characterised by wet and hot summer seasons and cool and dry winter seasons. Without the effects of climate change, the rainy season (summer) runs from October to April, and the winter season from May to September (Cai et al., 2017). January is the hottest month with an average temperature of 23 � C and the coldest is June at 13 � C. Mean annual rainfall ranges from 300 mm in the northern half of the district to 1 000 mm in the southern half. Rainfall is highest around January–February, the period with the highest fre­ quency of flooding (DWS, 2003; Mpandeli, 2014). Like the rest of the province, Capricorn District, is endowed with abundant agricultural resources (Cai et al., 2017; Oni et al., 2012). However, the realisation of the agricultural potential is limited by water resources. The semi-arid conditions, coupled with the absence of a pri­ mary water source, contribute to limited water resources availability in the district (Mashamba, 2008). Households in Limpopo Province have, on average, water supplies of about 25 L/capita/day translating to about less than 10 m3/capita/annum; this illustrates a severe level of water scarcity in the province (Tshikolomo et al., 2012). Eighty-one percent of the cultivated land is rainfed and only 19% is irrigated (Cai et al., 2017; Nhamo et al., 2018). The population is predominantly rural, agriculture being the major economic activity (DAFF, 2016; Mostert et al., 2008). Smallholder farmers dominate the district and have a land tenure of only about 2 ha (Graeub et al., 2016), and are faced with a variety of chal­ lenges that include limited access to markets, lack of collateral to access financial support from banks, lack of storage facilitates, ageing equip­ ment, poor roads, high incidence of crop pests such as fall armyworm (Spodoptera frugiperda) on maize and sorghum and leaf miner (Tuta absoluta) on tomatoes, recurring droughts and other extreme weather events, vandalism, poor access to agro-meteorological information, unreliable energy, small land tenure, among others (van Koppen et al., 2017; Von Loeper et al., 2016). 2.2. Data collection Monthly rainfall and temperature data was obtained from the Agri­ culture Research Council (ARC) and South African Weather Services (SAWS). There were seven weather stations with complete data as shown in Fig. 1 (All-Days, Bochum, Hlanganani, Grootfontein, Moko­ pane, Syferkul and Swerwerskraal). Rainfall, relative humidity and temperature data recorded from 1960 to 2015 was used to assess drought and heat stress, and evaluate variations in climate variables. Long-term rainfall and temperature variations were assessed using the Standard Precipitation Index (SPI) and the Thermal Heat Index (THI), respectively. The SPI and THI were considered as they analyse rainfall and temperature over long periods. The MODIS satellite images, downloaded from the National Aero­ nautics and Space Administration’s (NASA) EarthData centre, were used to assess monthly variations in water stress and vegetation vigour from 2

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Fig. 1. Capricorn District in Limpopo Province, South Africa, showing location of weather station in the district and the distribution of irrigated and rainfed areas.

2000 to 2018. Although MODIS operations only started in 2000, its images are important for providing a clear pictorial overview of the spatio-temporal changes occurring in an area in terms of water stress. The monthly variations were assessed through an analysis of time series 16-day composite MODIS NDVI product, MOD13Q1, generated at 250 m spatial resolution (Strahler et al., 1999). The MOD13Q1 product is ob­ tained in H20v11 tile format and was downloaded using R’s MODIStsp package (Busetto and Ranghetti, 2016). The MODIStsp was also used to exact MODIS NDVI from the HDF file, converting it from sinusoidal to UTM 35 S (Busetto and Ranghetti, 2016). Variations in water stress were assessed using monthly intervals over the period under review targeting mainly the cropping season. The analyses allowed defining the cropping calendar according to traceable monthly changes taking place, and allowing formulating tailor-made adaptation strategies. The MOD13Q1 reflectance dataset removes view-angle effects, masks cloud cover and reduces residual atmospheric contamination (Didan et al., 2015).

NDVI ¼

NIR Red NIR þ Red

(1)

For healthy vegetation, this ratio is high due to the inverse rela­ tionship between vegetation brightness in the red and infrared regions of the spectrum. Thus, NDVI values range between 1 and 1 with high NDVI values indicating healthy crops while low NDVI values indicate wilting or water stressed vegetation (Sullivan and Eastin, 1975). Studies have shown positive correlation between plant healthy and NDVI (Ji and Peters, 2003). NDVI threshold used for the both the start and end of the €nsson and Eklundh, 2004). season was 0.33 (Jo Time series MODIS-NDVI data was analysed using the TIMESAT, a €nsson and FORTRAN90 program for extracting seasonal parameters (Jo Eklundh, 2004). The program eliminates outliers and minimises the noise level on the MODIS datasets using the Savitzky-Golay filter, which is based on upper envelope weighted fits to harmonic and asymmetric Gaussian model functions (Jonsson and Eklundh, 2002; Schafer, 2011), through the Signal Package in R. The procedure creates a phenological plot (Fig. 2). Temporal trends shown in Fig. 2 were generated to create important metrics (depicted as a-i) to identify long-term changes in the growing season. The procedure returns parameters like (a) start of the growing season, (b) end of season, and (c) length of the growing season, €nsson and Eklundh, 2004). among other parameters shown in Fig. 2 (Jo The procedure was run for every season from 2000 to 2018 to compare the changes occurring during the growing season over the years.

2.3. Normalised difference vegetation index (NDVI) and the TIMESAT package An indicator for a healthy plant is its chlorophyll content, which is assessed through vegetation indices acquired through remote sensing (Xue and Su, 2017). Vegetation indices refer to spectral combinations of two or more spectral bands that improve vegetation properties and allow for reliable detection of spatio-temporal variations in plant health (Huete et al., 2002; Xue and Su, 2017). Of the many vegetation indices available in literature, the widely used is the NDVI, which is important for monitoring plant greenness and plant vigour (Ji and Peters, 2003; Sullivan and Eastin, 1975). The NDVI is the inverse relationship between the near-infrared (NIR) and the red bands, and is used as a proxy of vegetation water stress, and is expressed as (Pettorelli et al., 2005; Sullivan and Eastin, 1975):

2.4. Standardized Precipitation Index (SPI) The Standardized Precipitation Index (SPI) was used to assess drought frequency. The SPI is a meteorological index based on the cu­ mulative probability of a given rainfall event occurring at a particular rainfall station (McKee, 1995). The index is developed as an expression of precipitation departure from normal over a certain period of time 3

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data availability (Dikmen and Hansen, 2009). Data from only four weather stations (All-Days, Bochum, Grootfontein and Mokopane) was considered in the estimation of heat stress as it had the longest records (2006–2015) for both temperature and relative humidity. The THI is expressed as (Dikmen and Hansen, 2009): THI ¼ ðc1T þ c2Þ

c3ð1

HÞðc1T

(2)

c4Þ

where, T is maximum temperature, H is relative humidity as a fraction of the unit. The constants are c1 ​ ¼ 1.8; c2 ​ ¼ 32; c3 ¼ 0.55; c4 ​ ¼ 26.8. The THI was then interpreted based on threshold values suggested by St. Pierre et al., (St-Pierre et al., 2003) (Table 2). When indices are <72, it indicates no stress, but when the indices increases, the stress becomes more intense. 3. Results and discussion

Fig. 2. Seasonal metrics derived from phenology profile using TIMESAT: (a) start of season, (b) end of season, (c) length of season, (d) base value, (e) time of middle of season, (f) peak value, (g) amplitude, (h) small integrated value, (h þ i) large integrated value. Source: TIMESAT.

3.1. Water stress variations over time The TIMESAT derived data showed high variability in NDVI values over time in Capricorn District, as well as differences between raw data and the Gaussian filtered data (Fig. 3). The amplitude of NDVI values differed for each of the years from 2000 to 2018. Phenological variations were clear with low and high NDVI in winter and summer, respectively. The low amplitude of NDVI values between the years indicated water stress, which normally results in low yields. The peaks and lows in Fig. 3 show the level and degree of mid-season water stress as defined by NDVI values. Besides showing the high rainfall variability, the shape of the graph (Fig. 3) also highlighted the variability in the length of the growing season as the start or end of the rain season changed from time to time. A particular case was the 2003/04 season, which had severe water stress that led to low amplitude resulting from short growing season. The observed high variability in water stress is also shown in Figs. 4 and 5, which are time series monthly variations of NDVI images during the growing season from 2001 to 2016. Fig. 4(a) and (b) represent the months of December and January and Fig. 5(a) and (b) represent the months of February and March, respectively. Similar to Fig. 3, there was high variability in water stress and evident mid-season dry spells. The variability is evident by analysing the NDVI colour differences during the same month. NDVI variations during the month of December indi­ cated variations during the early stages of the growing season. A com­ parison of NDVI values of the same month also showed varying values, indicating high rainfall variability (Figs. 4 and 5). Mid-season dry spells are noticeable by observing changes taking place during the same season, for example, comparing the NDVI colour differences in the month of December 2002, January 2003, February 2003 and March 2003, when rainfall was expected to be at peak. The image for December 2001 showed a healthy vegetation signifying that the rainy season started earlier, but further observation of the months that followed in the same season, there was evidence of water stressed (drying) vegetation. In January 2002, vegetation was already losing its vigour and the situation got worse in February 2002 and March 2002. This suggests drought, which can cause crop failure in rainfed agricul­ ture. Although the rainy season started earlier, it was very short. Another example was the 2002/03 season, where there was evidence of

(Abari et al., 2015). Due to its flexibility, less input data requirements and suitability in various climate regions, the SPI is widely used to assess drought conditions in many areas (Malherbe et al., 2016; Naresh Kumar et al., 2009). The SPI was calculated using a program developed by the National Drought Mitigation Centre (NDMC) (http://drought.unl.edu/archiv e/climdiv_spi/spi/program/spi_sl_6.exe), which is based on the algo­ rithm developed by McKee et al. (1993). The program calculates a time series of the SPI at a given time interval from an input data file con­ taining monthly rainfall of each station. For accumulated drought con­ ditions, calculations were then conducted for each of the months at six different time scale namely 1, 3, 6, 9, 12 and 24 months’ periods respectively, using data from 1960 to 2015 for all the seven stations evenly distributed over the Capricorn district. Classification of drought based on the SPI was adopted from McKee et al. (1993) (Table 1). A drought event is identified whenever the SPI value is negative and the intensity of the drought is recognised when the negative index is very high, while the reverse indicates the intensity of wet conditions, where higher indices may indicate flooding. 2.5. Thermal Heat Index (THI) An assessment of heat stress is necessary as heat affects both live­ stock and crops that do not tolerate harsh weather conditions (Mab­ haudhi et al., 2019). In livestock farming, heat stress mainly affects the production of dairy cattle (Pragna et al., 2017; Rust and Rust, 2013). Heat stress was assessed through the Thermal Heat Index (THI), which is a function of daily maximum temperature and relative humidity �ndez et al., 2011). The choice of the THI was informed by its (Herna successful application in Limpopo Province in previous studies (Nesamvuni et al., 2012; Winsemius et al., 2014). The Dikmen and Hansen equation was preferred as it is the most suitable for areas with limited dry bulb temperature, dew point temperature and solar radiation Table 1 Classification of SPI values. SPI values

Drought/wetness category

� 2.00 1.50 to 1.99 1.00 to 1.49 0 to 0.99 0 to 0.99 1.00 to 1.49 1.50 to 1.99 �2.00

Extreme Drought Severe Drought Moderate Drought Mild drought Mild wet Moderately Wet Severely Wet Extremely Wet

Table 2 Classification of THI values for livestock heat stress.

Source McKee et al., 1993 (McKee et al., 1993)

THI values

Heat stress category

<72 72–78 79–89 90–98 >98

No stress Mild stress Severe stress Extreme stress Death

Source: St. Pierre et al., (2003) (St-Pierre et al., 2003) 4

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Fig. 3. Seasonal phenological profile metrics showing NDVI variations from start to end of the growing season from 2000 to 2019.

drought recurrence in Capricorn District over the short-term (1-month and 3-month SPI), medium-term (6-month and 9-month SPI) and longterm (12-month and 24-month SPI) periods, respectively (Fig. 7). The occurrence of severe to extreme drought events can be depicted at all stations from 1960 to 2015. Between 1960 and 1970, extreme drought was noted across all stations at an average interval of once every three seasons. Subsequently, extreme short-term drought affecting agricultural practices within the growing season were recorded during 1980, 1983, 1984, 1986, 1988 and 1989; of which the most extreme drought (SPI ¼ 3) was recorded at All-Days in February 1983. At Bochum, extreme drought was recorded for two consecutive seasons (1991/92 and 1992/93). After the 1992/93 drought, the area experi­ enced seven extreme droughts, recording larger negative SPI values during November and December of 2001, 2002 and 2004. Furthermore, the 2004 drought persisted until the 2005/06 season at Bochum, Swerwerskraal and Spelonken. There were also reported livestock deaths during these drought periods as well as low milk production affecting rural livelihoods (Du Preez et al., 1990). Mild to severe drought (SPI 0 to 2) had been frequent during the period under review. Prior to 1991, the years, 1961, 1965, 1966, 1968, 1973, 1982 and 1989 marked the most extreme prolonged (6-month and 9-month SPI) drought pe­ riods, with SPI values of between 2 and 3 at all stations. Moreover, it was noted that the severity level of the drought encountered in the district intensified from 1992 to 2015. During this period SPI values < -2 were recorded at All-Days, Bochum, Swerwerskraal and Syferkuil, with Bochum recording a peak of 3 during the 2005/06 season based on the 9-month SPI. The SPI results confirm the NDVI analysis done in the district. When considering the long-term (12-month and 24-month SPI) drought conditions, eight extreme droughts had occurred between 1962 and 2006. The SPI corresponding to long-term drought indicate less drought events as compared to the short and medium-term SPI. Based on the different SPI timescales, it is evident that the area is prone to midseason dry spells during the growing season. Results from the SPI analysis on drought recurrence at every threeyear period, complement the findings from the TIMESAT analysis on recurring extreme vegetation stress during summer seasons and at long periods. The periods showing vegetation water stress conditions coin­ cide with identified drought periods from the SPI results. A further analysis of both satellite and observed data indicate a highly variable climatic characteristic, with extreme peaks and lows. Of note is an observed increasing frequency and intensity in drought recurrence and vegetation stress in recent years.

drought and wilting crops throughput the season. This was ascertained by the shortened length of the growing season in 2002/03 as the growing season had only 125 days (Fig. 6). Actually, there were two successive drought seasons as the 2003/04 growing season had only 113 days (Fig. 6). The trends showed high variability in the growing season with varying starting and ending periods and mid-season dry spells. The intensity and frequency of mid-season dry spells has been increasing during the most recent years affecting the productivity of smallholder farmers. 3.2. Variations in the length of the growing season High variability was also evident in the length of the growing season for the period 2000 to 2018 as shown in Fig. 6. The length of the growing season varied from as little as 113 days during the 2002/03 season to 240 days during the 2017/18 season. Results from the TIMESAT analysis also showed varying start and end dates for the growing season. On few occasions, the growing season started in October, while in most cases it started mid-November. During the 2002/3 and 2003/04 seasons, the growing season started in January indicating shortened rain season and severe drought. The end of the growing season was generally between March and April. The 2002/03 and 2003/04 seasons were noticeably very dry seasons. These are ascertained by the varying base and peak NDVI values, which ranged between 0.24 and 0.28, and 0.31 and 0.59, respectively, over the years. Capricorn District is, therefore, has been experiencing low rainfall, high temperatures and recurring mid-season droughts in recent years. The analyses showed that drought events are occurring at an average of one drought event in every three years. This trend has been identified as cause for the for decreasing agricultural production as indicated by previous studies (Nhamo et al., 2019a; Schreiner et al., 2018). Extreme droughts occurred during the 1991/92, 2003/04 and 2015/16 seasons. The evident increasing and intensity of droughts requires a paradigm shift from the current agricultural norm to one that conforms to the changing climatic conditions. These climate-induced changes present the greatest challenge to policy and decision-makers as they attempt to address the unpredictable climate variations. The focus should be on developing local and tailor-made adaptation strategies from observed trends in rainfall patterns. 3.3. Drought analysis Drought time series shown by the SPI derived graphs indicated 5

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Fig. 4. Monthly variations in NDVI values showing water stress during December (a) and January (b) from 2001 to 2016. Vegetation is expected to be healthy during these early months of the growing season rainy season.

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Fig. 5. Monthly variations in NDVI values showing water stress during February (c) and March (d) from 2001 to 2016. Vegetation is expected to be very healthy during these late months of the growing season rainy season.

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Fig. 6. Variations in the Base and Peak NDVI values and the length of the rainy season from 2000 to 2018.

3.4. Heat stress analysis

3.6. Revised crop calendar for Capricorn district

The calculated Thermal Heat Index (THI) depicted by Fig. 8, in­ dicates high variability of heat stress in the district. The variation is notably evident across all stations for the analysed period. Of note are severe to extreme heat stress conditions during the months when the index ranged from 80 to 90. Heat stress severity is correlated to both levels of relative humidity and temperature. During the analysed period, mean monthly relative humidity indicated values commonly exceeding 80% during summer months and lesser values (>80%) being prevalent from winter to spring. The highest monthly averages of maximum temperature and THI were determined at All-Days and Grootfontein. Heat stress conditions indicated with mean monthly THI of >72 were observed throughout the months, except for Bochum and Mokopane, where lack of heat stress was observed during the winter months. Mean monthly maximum temperature recorded at all stations ranged from �20 � C to �30 � C during winter and summer months, respectively (Fig. 8). Although depending on the breed, lactation stage and milk yield, this could imply detrimental heat stress for dairy cows. Previous studies have shown that dairy cows are affected when THI exceeds 90, as the heat stress reduces milk production (Nesamvuni et al., 2012; Wil­ liams et al., 2016). In addition, when heat stress values were extremely high, there were reports of livestock deaths (Visser, 2017; Williams et al., 2016).

The observed deviations in rainfall, and heat and water stress during the growing season (October to April) are an indication of high climatic variability, shorter rainfall season, which are causing shifts of agroecological zones. Such deviations from the norm demand changes in cropping patterns to suit the current shortened season. Fig. 9 is a revised cropping calendar for Capricorn District derived from the observed changes in rainfall, heat and water stress. Rainfall season used to start in October, but it has shifted to December affecting mainly the cultivation of major cereals (maize and wheat). The current weather pattern re­ quires that either, the planting season for major cereals start in October under irrigation or starts in December using early maturing varieties. However, supplementary irrigation maybe needed even during the months when rainfall is expected to be normal, as there was evidence of mid-season dry spells. Planting of traditional and indigenous crops such as sorghum and millet can still start in October as they are generally drought tolerant and can adapt to the prevailing harsh climatic condi­ tions (Mabhaudhi et al., 2019) or the planting can even start later in December and prolong their cropping season to June (Fig. 9). Current weather patterns in the district favour indigenous crops, which generally do well under the prevailing harsh climatic conditions and do not require a lot of water (Mabhaudhi et al., 2019). 4. Recommendations

3.5. Implications of increasing drought frequency and heat stress

Water management and agriculture need to flourish if ever the world is to achieve the 2030 Global Agenda on Sustainable Development on zero hunger, provision of clean water and sanitation, jobs creation and economic growth, improvement of livelihoods and no poverty (Raidimi and Kabiti, 2017; Zwane and Montmasson-Clair, 2016). To achieve these goals in a sustainable way, policy and decision-makers should adopt radical measures to transform food production systems and the whole agriculture value chain, which include innovative technologies, pro­ cesses and systems that encompass agricultural water management and at the same time ensuring water, energy and food security (Antle et al., 2017; Capalbo et al., 2017). One such systems approach is the WEF nexus, which accounts for synergies and trade-offs among the WEF sectors as they are intrinsically connected, as well as explaining and simplifying the complex relationships and interactions among natural resources (Mabhaudhi et al., 2016; Nhamo et al., 2019b). The WEF nexus ensures that developments in one sector do not affect the other two, and thus it is an integrated approach to resource development, utilization and management (Nhamo et al., 2019b). An integrated approach in resources management ensures meeting the food re­ quirements of a growing population in the advent of depleted water resources through advanced agricultural water management

The SPI and THI analysis also showed that Capricorn District had been subjected to severe heatwaves during the summer season in recent years, as well as sudden heavy storms and cyclones around January to March. Generally, rainfall has been erratic and starting late affecting the length of the growing season, which used to start from October and ending in April, implying that the growing season has been modified, as also evidenced by previous studies (Du Preez et al., 1990; Gantner et al., 2011; Nhamo et al., 2019c). These adverse changes suggest serious implications on the livelihoods of smallholder farmers who are often unable to respond to the shift in onset of rainy season. Temperatures are projected to increase from between 1.5 � C and 4.5 � C by 2 100 (IPCC, 2012; IPCC, 2014). A further increase in temperature implies future increases in the likelihood of extreme weather events that would result in further economic losses in agriculture if no actions are taken to adapt the sector to the prevailing conditions (Rust and Rust, 2013; Shiferaw et al., 2011). These figures are ascertained by global estimations which suggest that if no immediate action is taken to curb greenhouse gas (GHG) emissions, agricultural productivity is will decrease from 15% to 50% by 2080 due to climate change and variability (Ahlenius and UNEP/GRID-Arendal, 2009). 8

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Fig. 7. Standardized Precipitation Index (SPI) time series from 1960/61–2014/15 for stations Bochum (A), All days (B), Swerwerskraal (C), Spelonken (D), Mokopane (E), Grootfontein (F) and Syferkuil (G).

technologies. The management of these resources for sustainability en­ compasses scenario planning and defining tailor-made adaptation stra­ tegies at local level, or even at household level, that would result in resilient communities in the advent of climate variability and change. As climate change is cross-dimensional, affecting all sectors that include, among others, water, food, energy, health, infrastructure, ecosystems and biodiversity, the WEF nexus is an essential systems approach capable of ensuring sustainability and provide policy and decision-making with evidence on priority areas needing intervention

(Nhamo et al., 2019b). Given the importance and urgent need to implement climate change adaptation strategies at local level and adjust to the new norm, the WEF nexus, thus can complement existing tools (e.g. remote sensing) and observed data in building resilient communities for sustainable devel­ opment. The method applied in this study can be replicated in any area and at any scale as the MODIS-NDVI data, as well as climatic variables data are readily available.

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Fig. 8. Mean monthly relative humidity, maximum temperature and Thermal Heat Index (THI) for the period 2006–2015 in (A) Bochum, (B) Mokopane, (C) All days and (D) Grootfontein.

Fig. 9. Revised cropping calendar for selected major cereals (maize, wheat) and alternative indigenous crops (sorghum, millet) for Capricorn District.

4.1. Adaptation strategies for farmers in Capricorn district

Although increases in temperature and carbon dioxide (CO2) may actually boost yields of some crops, conditions like improved nutrient levels, soil water content and water availability must also be met (Brouder and Volenec, 2008; Gornall et al., 2010). Research has also shown that CO2 decreases the nutritional value of food crops as high CO2 concentrations in the atmosphere reduce the protein quantity and that of other important minerals in most cereals and other plant species (Myers et al., 2014; Taub et al., 2008). Climate change has modified agro-ecological zones and making it difficult to grow crops in the same traditional ways and same environments as before (Fan et al., 2008). Science has developed innovative tools that can be used to identify appropriate adaptation options for highly diverse agro-ecological zones. One such tool is the Climate Analogues, whose main theme is to find future agriculture today (Ramírez-Villegas et al., 2011). Developed by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the identifies geographic regions whose present cropping conditions present similar projected future climates and allows communities living in such areas to exchange ideas and learn on agri­ culture practices that work well in those future environments. Such practices promote an understanding of adaptation practices that can be implemented to local context to cope with anticipated modifications in growing conditions over time (Nhamo et al., 2019c).

Table 3 provides some of the observed changes in rainfall, heat stress and vegetation healthy in Capricorn District, which affect crop pro­ duction, as well as the adaptation strategies to be considered to mitigate risk and build resilience. A key component of climate change adaptation is building resilience, which is the capacity of a system to absorb disturbance without collapsing (Folke et al., 2010). Tailor-made adap­ tation strategies developed at local level help to build a resilient system capable of withstanding shocks and rebuilds itself when necessary. Adaptation strategies can be categorised into proactive (those activities undertaken prior to the occurrence) and reactive (activities to cope with the impacts after the occurrence). The effectiveness of the adaptation strategies depends on the intensity of the climate risk, risk knowledge and awareness, associated non-climatic shocks, diversity of income sources, availability and accessibility of technologies and rural support systems. Adapting to climate change is possible through the adoption of strategies such as autonomous adaptation (shifts in planting dates and cultivar substitution) and embracing new technologies and trans­ formational changes (climate-smart agriculture and livelihood diversi­ fication or change), as well as improving on trade policies and encouraging shifts in diets (Nhamo et al., 2019c) (Table 3). 10

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Table 3 Observed climate changes, risks and tailor-made adaptation strategies t. Observed changes

Identified risks

Proposed adaptation strategies

Shortened rain season (now starting in December and ending March)

� Shifting agroecological zones. � Change in crop suitability due to shortened growing duration � Low yields and/or crop failure

� Increasing access to climate information and services � Mainstreaming weather information into agricultural extension support through the use of bulletins to guide preparedness efforts e.g. to understand agricultural start of season to avoid risks associated with replanting’s � Plant early maturing crop varieties � Varying planting dates and varieties � Crop diversification (also include indigenous crops with short growing length) � Ex- and in-situ rainwater harvesting and conservation techniques � Adopt crop diversification system to spread risk of crop failure � Build small water reservoirs; ex- and in-situ rainwater harvesting � Use of water conservation techniques such as mulching, ridging, and minimum tillage to conserve soil water � Adopt heat tolerant animals � Destock in times of severe drought � Promote hay baling through provision and maintenance of appropriate equipment � De-bushing for rangeland rehabilitation, grazing improvement and on-farm income diversification � Support community-level fodder production through provision of seeds � Provide supplementary feeding and mineral feed for cattle � Adopt by venturing into small stock production � Use of appropriate varieties/crops with good heat stress tolerance, e.g. sorghum and millets � Mulching to conserve soil water and also lower soil temperature hence mitigate soil evaporation losses � Intercropping � Access to drought early warning information before the beginning of the season (e.g. relocate livestock to higher grounds before an extreme events) � Promotion of drought tolerant crops (promote seed fairs to enhance local

Mid-season dry spells from October to December

Increasing temperatures from the norm. (so called heat waves)

Increased frequency and intensity of droughts

� Intermittent water stress � Low yields and/or crop failure � Water deficit � Temperature stress

� Affect the production of dairy livestock � Livestock death � Affect crops that do not favour harsh conditions. � Increased water stress in crops � Increased risk of pests and diseases

� Affects rainfed agriculture � Reduced availability of freshwater resources

Table 3 (continued ) Observed changes

Identified risks

Proposed adaptation strategies

�

�

� �

�

Increased risk of flooding-(flash floods/cyclones) from January to March

� Risk of waterborne diseases � Risk of water logging in fields � Risk of soil erosion � Low yields and/or crop failure � Drowning of livestock

� �

�

� � Intra and interseasonal rainfall variability

� Unpredictable weather � Planting date selection � Low yield and/or crop failure

�

�

�

exchange of seed amongst farmers and promote community-level droughttolerant seed production) Diversification (intercropping, market gardening and indigenous fruit trees) Promote uptake of weather index based insurance products for both crops and livestock Stone bunds & terracing for runoff mitigation Use of water conservation principles e.g. 30% þ mulching, rotation and minimum tillage to conserve soil water Promote use of standard contours in-field to facili­ tate appropriate infiltra­ tion and drainage; dead level contours and infiltra­ tion pits to direct run-off to recharge the water table Access to flood forecasting and early warning systems Mainstreaming of flood forecasts into extension services For livestock related waterborne diseases, build at least 2 þ kraals to rotate livestock during time of flooding Agro-forestry for increased water capture Stone bunds and terracing for runoff mitigation Use of climate smart agriculture methods (farmer training on climate smart agricultural technologies that enhance coping capacities of the farmers). Access to climate information and services for informing crop/variety and planting date selection Diversification of croplivestock systems to spread the risk (intercropping, rearing small livestock, market gardening and indigenous fruit trees)

5. Conclusions The study used observed and remotely sensed data to assess climate variability and change from 1960 to 2015 (for observed data) and from 2000 to 2016 (for remotely sensed data) in Capricorn District of Lim­ popo Province in South Africa using the TIMESAT programme, which is designed to extract changes in spectral vegetation indices over long periods. Time series data of NDVI derived satellite spectral measure­ ments was used to access information on seasonal vegetation develop­ ment, which is important for identifying seasonal changes occurring over time. The process derived phenological parameters such as start and end of season using specified thresholds over long periods of time. Extracted long term spectral changes in vegetation indices facilitates human understanding on the functional and structural characteristics of 11

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land cover and cycles of energy and matter, important information for understanding climate change impacts at both large and small spatial scales. Knowledge on long-term time series NDVI data provides infor­ mation on bio-climatic shifts by the extraction of seasonality parame­ ters, and other phenological characteristics, that was used to develop contextualised or tailor-made cropping calendar. Apart from the use­ fulness of the derived information in understanding seasonality changes over time, it is also important on providing evidence for building resil­ ience and adapting to the changed crop growing season. The use of observed data complemented this new knowledge of changing seasons as evidenced by episodes of intra-seasonal droughts, or heavy rainfall over short periods of time during the rainy season as shown by the SPI analysis. Results from THI also indicate periods of extreme tempera­ tures. Observed changes in climate regimes are fundamental in identi­ fying the risks that normally accompany such changes. Identified climatic changes and the associated risks were then used to recommend context-based adaptation strategies specifically for the district. Contextbased adaptation strategies are necessary in that climate change impacts and intensity vary from place to place and therefore, adaptation should be designed according to climatic change occurring in each particular place.

Capalbo, S.M., Antle, J.M., Seavert, C., 2017. Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making. Agric. Syst. 155, 191–199. Chaney, N., Herman, J., Reed, P., Wood, E., 2015. Flood and drought hydrologic monitoring: the role of model parameter uncertainty. Hydrol. Earth Syst. Sci. 19 (7), 3239–3251. Chinowsky, P., et al., 2011. Climate change: comparative impact on developing and developed countries. Eng. Proj. Organ. J. 1 (1), 67–80. DAFF, 2016. Abstract of Agricultural Statistics 2016, Department of Agriculture, Forestry and Fisheries (DAFF), Directorate Statistics and Economic Analysis. South Africa, Pretoria. Davis, C.L., Vincent, K., 2017. Climate Risk and Vulnerability: A Handbook for Southern Africa, second ed. CSIR, Pretoria, South Africa, p. 202. DEA, 2013. Long-Term Adaptation Scenarios Flagship Research Programme (LTAS) for South Africa: Climate Trends and Scenarios for South Africa. Department of Environmental Affairs Pretoria (DEA), Pretoria, South Africa. Didan, K., Munoz, A.B., Solano, R., Huete, A., 2015. MODIS Vegetation Index User’s Guide (MOD13 Series). The University of Arizona, Arizona, USA. Dikmen, S., Hansen, P., 2009. Is the temperature-humidity index the best indicator of heat stress in lactating dairy cows in a subtropical environment? J. Dairy Sci. 92 (1), 109–116. Du Preez, J., Hattingh, P., Giesecke, W., Eisenberg, B., 1990. Heat stress in dairy cattle and other livestock under southern African conditions. III. Monthly temperaturehumidity index⋅ mean values and their significance in the performance of dairy cattle. Onderstepoort J. Vet. Res. 57, 6. DWS, 2003. Limpopo Water Management Area. Overview of Water Resources Availability and Utilization. Department of Water and Sanitation (DWS), Pretoria, South Africa. Fan, J., et al., 2008. Carbon storage in the grasslands of China based on field measurements of above-and below-ground biomass. Clim. Change 86 (3–4), 375–396. Folke, C., et al., 2010. Resilience thinking: integrating resilience, adaptability and transformability. Ecol. Soc. 15 (4). Gantner, V., Miji�c, P., Kuterovac, K., Soli�c, D., Gantner, R., 2011. Temperature-humidity index values and their significance on the daily production of dairy cattle. Mljekarstvo: �casopis za unaprjeđenje proizvodnje i prerade mlijeka 61 (1), 56–63. Gornall, J., Betts, R., Burke, E., Clark, R., Camp, J., Willett, K., Wiltshire, A., 2010. Implications of climate change for agricultural productivity in the early twenty-first century. Philos. Trans. R. Soc. B 365, 2973–2989. https://doi.org/10.1098/ rstb.2010.0158 (Biological Sciences). Graeub, B.E., Chappell, M.J., Wittman, H., Ledermann, S., Kerr, R.B., GemmillHerren, B., 2016. The state of family farms in the world. World Dev. 87, 1–15. Hales, S., Edwards, S., Kovats, R., 2003. Impacts on health of climate extremes. In: McMichael, A.J., et al. (Eds.), Climate Change and Human Health: Risks and Responses. World Health Organisation (WHO), Geneva, Switzerland, pp. 79–96. Hern� andez, A., et al., 2011. Temperature-humidity index (THI) 1917-2008 and future scenarios of livestock comfort in Veracruz, M� exico. Atm� osfera 24 (1), 89–102. Holzmann, P., Boudreau, T., Holt, J., Lawrence, M., O’Donnell, M., 2008. Household Economy Approach: A Guide for Programme Planners and Policy-Makers. Save the Children UK, London, UK, p. 160. Huete, A., et al., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83 (1–2), 195–213. IPCC, 2012. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York, USA. IPCC, 2014. Climate Change 2014–Impacts, Adaptation and Vulnerability: Regional Aspects. Cambridge University Press, New York, USA, p. 688. Ji, L., Peters, A.J., 2003. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens. Environ. 87 (1), 85–98. Jonsson, P., Eklundh, L., 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 40 (8), 1824–1832. J€ onsson, P., Eklundh, L., 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30 (8), 833–845. Mabhaudhi, T., Chibarabada, T., Modi, A., 2016. Water-food-nutrition-health nexus: linking water to improving food, nutrition and health in Sub-Saharan Africa. Int. J. Environ. Res. Public Health 13 (1), 107. Mabhaudhi, T., Chimonyo, V.G.P., Hlahla, S., Massawe, F., Mayes, S., Nhamo, L., Modi, A.T., 2019. Prospects of orphan crops in climate change. Planta 250 (3), 695–708. Malherbe, J., Dieppois, B., Maluleke, P., Van Staden, M., Pillay, D., 2016. South African droughts and decadal variability. Nat. Hazards 80 (1), 657–681. Mashamba, N., 2008. The state of IDP in the Limpopo province. J. Public Adm. 43 (Special Issue 2), 421–435. McKee, T.B., 1995. Drought monitoring with multiple time scales. In: Proceedings of 9th Conference on Applied Climatology, Boston, 1995. American Meteorological Society, Dallas, Texas, USA, pp. 233–236. McKee, T.B., Doesken, N.J., Kleist, J., 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology (17-22 January 1993). American Meteorological Society Boston, MA, Anaheim, California, pp. 179–183. Mostert, T.H., Bredenkamp, G.J., Klopper, H.L., Verwey, C., Mostert, R.E., Hahn, N., 2008. Major vegetation types of the Soutpansberg conservancy and the Blouberg nature reserve, South Africa. Koedoe 50 (1), 32–48. Mpandeli, S., 2014. Managing climate risks using seasonal climate forecast information in Vhembe District in Limpopo Province, South Africa. J. Sustain. Dev. 7 (5), 68–81.

Funding The research was funded by the Water Research Commission (WRC) through the Research Development Branch. Declaration of competing interest The authors declare no conflict of interest. Acknowledgments The authors would like to thank the Water Research Commission (WRC), the International Water Management Institute (IWMI), the Agricultural Research Council of South Africa – Institute for Soil, Climate and Water (ARC-ISCW), Tshwane University of Technology (TUT), Limpopo Department of Agriculture & Rural Development (LDARD) and the University of KwaZulu-Natal (UKZN) through the uMngeni Resilience Project (URP, funded by the Adaptation Fund) and the Sustainable and Healthy Food Systems programme (SHEFS, funded by the Wellcome Trust Grant No. 205200/Z/16/Z), for support in writing this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.wace.2019.100240. References Abari, M.F., Radnezhad, H., Sadeghi, M., 2015. Evaluation of drought selected stations by standardized precipitation index (SPI) on Wetland Gandoman. J. Mater. Environ. Sci. 6 (10), 14. Ahlenius, H., UNEP/GRID-Arendal, 2009. Projected Agriculture in 2080 Due to Climate Change. UNEP/GRID-Arendal, Oslo, Norway. Antle, J.M., et al., 2017. Towards a new generation of agricultural system data, models and knowledge products: design and improvement. Agric. Syst. 155, 255–268. Brooks, N., Adger, W.N., 2005. Assessing and enhancing adaptive capacity. In: Chopra, K., Leemans, R., Kumar, P., Simons, H. (Eds.), Adaptation Policy Frameworks for Climate Change: Developing Strategies, Policies and Measures. Cambridge University Press, Cambridge, UK, pp. 165–181. Brouder, S.M., Volenec, J.J., 2008. Impact of climate change on crop nutrient and water use efficiencies. Physiol. Plant. 133 (4), 705–724. Busetto, L., Ranghetti, L., 2016. MODIStsp: an R package for automatic preprocessing of MODIS Land Products time series. Comput. Geosci. 97, 40–48. Cai, X., Magidi, J., Nhamo, L., van Koppen, B., 2017. Mapping irrigated areas in the Limpopo province. In: South Africa, International Water Management Institute (IWMI), IWMI Working Paper 172, Colombo, Sri Lanka.

12

S. Mpandeli et al.

Weather and Climate Extremes 26 (2019) 100240 SADC, 2015. Revised Regional Indicative Strategic Development Plan 2015-2020. Southern African Development Community (SADC), Gaborone, Botswana. Schafer, R.W., 2011. What is a Savitzky-Golay filter. IEEE Signal Process. Mag. 28 (4), 111–117. Schreiner, B.G., Mungatana, E.D., Baleta, H., 2018. Impacts of Drought Induced Water Shortages in South Africa: Economic Analysis, Water Research Commission (WRC), WRC Report No. 2604/1/18 (Pretoria, South Africa). Shiferaw, B., Prasanna, B.M., Hellin, J., B€ anziger, M., 2011. Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur. 3 (3), 307. St-Pierre, N., Cobanov, B., Schnitkey, G., 2003. Economic losses from heat stress by US livestock industries. J. Dairy Sci. 86, E52–E77. Stats-SA, 2015. Mid-year Population Estimates, 2015. Statistics South Africa (Stats SA), Pretoria, South Africa. Strahler, A.H., et al., 1999. MODIS BRDF/albedo Product: Algorithm Theoretical Basis Document Version 5.0. MODIS Product ID: MOD43, NASA EOS-MODIS Document. Suepa, T., Qi, J., Lawawirojwong, S., Messina, J.P., 2016. Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast Asia. Environ. Res. 147, 621–629. Sullivan, C.Y., Eastin, J.D., 1975. Plant Physiological Responses to Water Stress, Developments in Agricultural and Managed Forest Ecology. Elsevier, pp. 113–127. Taub, D.R., Miller, B., Allen, H., 2008. Effects of elevated CO2 on the protein concentration of food crops: a meta-analysis. Glob. Chang. Biol. 14 (3), 565–575. Tripathi, A., Mishra, A.K., 2017. Knowledge and passive adaptation to climate change: an example from Indian farmers. Clim. Risk Manag. 16, 195–207. Tshikolomo, K.A., Nesamvuni, A.E., Stroebel, A., Walker, S., 2012. Water supply and requirements of households in the Luvuvhu-Letaba water management area of South Africa. Int. J. Bus. Soc. Sci. 3 (3). UNGA, 2012. The Future We Want. 66th Session of the UN General Assembly. United Nations General Assembly, New York, United States of America. UNGA, 2015. Transforming Our World: the 2030 Agenda for Sustainable Development. Resolution Adopted by the General Assembly (UNGA). United Nations (UN), New York, USA. van Koppen, B., et al., 2017. Smallholder Irrigation Schemes in the Limpopo Province, South Africa, 174. International Water Management Institute (IWMI), Colombo, Sri Canka, p. 40 (pp). Visser, C., 2017. Adaptation of local meat goat breeds to South African ecosystems. In: Sim~ oes, J., Guti�errez, C. (Eds.), Sustainable Goat Production in Adverse Environments. Springer, pp. 67–76. Von Loeper, W., Musango, J., Brent, A., Drimie, S., 2016. Analysing challenges facing smallholder farmers and conservation agriculture in South Africa: a system dynamics approach. S. Afr. J. Econ. Manag. Sci. 19 (5), 747–773. Weigand, M., Wurm, M., Dech, S., Taubenb€ ock, H., 2019. Remote sensing in environmental justice research—a review. ISPRS Int. J. Geo-Inf. 8 (1), 20. Williams, R., Scholtz, M., Neser, F., 2016. Geographical influence of heat stress on milk production of Holstein dairy cattle on pasture in South Africa under current and future climatic conditions. S. Afr. J. Anim. Sci. 46 (4), 441–447. Winsemius, H., et al., 2014. The potential value of seasonal forecasts in a changing climate in southern Africa. Hydrol. Earth Syst. Sci. 18 (4), 1525–1538. Xiao, X., Zhang, J., Yan, H., Wu, W., Biradar, C., 2009. Land Surface Phenology: Convergence of Satellite and CO2. Phenology of Ecosystem Processes. Springer, New York, USA, p. 27. Xue, J., Su, B., 2017. Significant remote sensing vegetation indices: a review of developments and applications. J. Sens. 2017. Zhang, X., Friedl, M.A., Schaaf, C.B., 2006. Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. J. Geophys. Res.: Biogeosciences 111 (G4). Ziervogel, G., et al., 2014. Climate change impacts and adaptation in South Africa. Wiley Interdiscipl. Rev.: Clim. Change 5 (5), 605–620. Zwane, M., Montmasson-Clair, G., 2016. Climate Change Adaptation and Agriculture in South Africa: A Policy Assessment (Report Compiled for WWF-SA). South Africa.

Myers, S.S., Zanobetti, A., Kloog, I., Huybers, P., Leakey, A.D., Bloom, A.J., Carlisle, E., Dietterich, L.H., Fitzgerald, G., Hasegawa, T., Holbrook, N.M., 2014. Increasing CO2 threatens human nutrition. Nature 510 (7503), 139. Naresh Kumar, M., Murthy, C., Sesha Sai, M., Roy, P., 2009. On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Meteorol. Appl. 16 (3), 381–389. Nesamvuni, E., Lekalakala, R., Norris, D., Ngambi, J., 2012. Effects of climate change on dairy cattle, South Africa. Afr. J. Agric. Res. 7 (26), 3867–3872. Nguimalet, C.-R., 2018. Comparison of community-based adaptation strategies for droughts and floods in Kenya and the Central African Republic. Water Int. 43 (2), 183–204. Nhamo, L., Chilonda, P., 2012. Climate change risk and vulnerability mapping and profiling at local level using the Household Economy Approach (HEA). J. Earth Sci. Clim. Chang. 3 (123), 2. Nhamo, L., Mabhaudhi, T., Modi, A., 2019. Preparedness or repeated short-term relief aid? Building drought resilience through early warning in southern Africa. Water S. A. 45 (1), 20. Nhamo, L., Mabhaudhi, T., Mpandeli, S., Nhemachena, C., Senzanje, A., Naidoo, D., Liphadzi, S., Modi, A.T., 2019. Sustainability indicators and indices for the waterenergy-food nexus for performance assessment: WEF nexus in practice - South Africa case study, p. 17. Preprint, 2019050359. Nhamo, L., Matchaya, G., Mabhaudhi, T., Nhlengethwa, S., Nhemachena, C., Mpandeli, S., 2019. Cereal production trends under climate change: impacts and adaptation strategies in southern Africa. Agriculture 9 (2), 16. Nhamo, L., van Dijk, R., Magidi, J., Wiberg, D., Tshikolomo, K., 2018. Improving the accuracy of remotely sensed irrigated areas using post-classification enhancement through UAV capability. Remote Sens. 10 (5), 712. Niang, I., Ruppel, O.C., Abdrabo, M.A., Essel, A., Lennard, C., Padgham, J., Urquhart, P., 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 199–1265 (pp). Nkoana, E., Verbruggen, A., Hug�e, J., 2018. Climate change adaptation tools at the community level: an integrated literature review. Sustainability 10 (3), 796. Oni, S., Nesamvuni, A., Odhiambo, J., Dagada, M., 2012. The Study of Agricultural Industry in the Limpopo Province (Executive Summary), Limpopo Province: Department of Agricultural Economics and Extention: Centre for Rural Development. Polokwane, South Africa). Pettorelli, N., et al., 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20 (9), 503–510. Pienaar, P.L., 2013. Typology of Smallholder Farming in South Africa’s Former Homelands: towards an Appropriate Classification System. Stellenbosch University, Stellenbosch, South Africa. Porter, J.R., Xie, L., Challinor, A.J., Cochrane, K., Howden, S.M., Iqbal, M.M., Lobell, D. B., Travasso, M.I., 2014. Food security and food production systems, Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, London, UK, pp. 485–533. Pragna, P., Archana, P.R., Aleena, J., Sejian, V., Krishnan, G., Bagath, M., Manimaran, A., Beena, V., Kurien, E.K., Varma, G., Bhatta, R., 2017. Heat stress and dairy cow: impact on both milk yield and composition. Int. J. Dairy Sci. 12 (1), 1–11. Raidimi, E., Kabiti, H., 2017. Agricultural extension, research, and development for increased food security: the need for public-private sector partnerships in South Africa. S. Afr. J. Agric. Ext. 45 (1), 49–63. Ramírez Villegas, J., Lau, C., Kohler, A.K., Jarvis, A., Arnell, N., Osborne, T.M., Hooker, J., 2011. Climate Analogues: Finding Tomorrow’s Agriculture Today, CGIAR Research Program on Climate Change. Agriculture and Food Security (CCAFS), Cali, Colombia. Rust, J., Rust, T., 2013. Climate change and livestock production: a review with emphasis on Africa. S. Afr. J. Anim. Sci. 43 (3), 255–267. SADC, 2014. SADC Regional Agricultural Policy. Southern African Development Comunity (SADC), Gaborone, Botswana.

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