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Are Smallholder Farmers’ Perceptions of Climate Variability and Change Supported by Climate Records? A Case Study of Lower Gweru in Semiarid Central Zimbabwe

VERONICA MAKUVARO Faculty of Natural Resources Management and Agriculture, Midlands State University, Gweru, Zimbabwe

CYRIL T. F. MUREWI Faculty of Science and Technology, Midlands State University, Gweru, Zimbabwe

JOHN DIMES Department of Employment, Economic Development and Innovation, Toowoomba, Queensland, Australia

IGNATIUS CHAGONDA Faculty of Natural Resources Management and Agriculture, Midlands State University, Gweru, Zimbabwe

(Manuscript received 17 March 2016, in ﬁnal form 28 September 2017)

ABSTRACT

The livelihoods of the majority of people in semiarid areas of developing nations are based on rain-fed agriculture. In the wake of climate variability and change, communities in these regions are the most vulnerable because of their limited capacities to adapt to environmental changes. Smallholder farmers in the study area, Lower Gweru in central Zimbabwe, ascertain that they have observed changes in some rainfall and temperature patterns. These changes include higher temperatures, an increased number of seasons without enough rainfall, and an increased frequency of droughts and lengths of dry spells. The aim of this study was to find out whether farmers’ perceptions are supported by mean and extreme event trends in observed historical climate data. Gweru Thornhill meteorological data were analyzed for significant trends. The analysis showed that temperatures are increasing significantly, consistent with farmers’ observations that temperatures are getting hotter. This study revealed that farmer perceptions on rainfall were not consistent with historical climatic trends. Thus, farmers in the Lower Gweru area may not be a very reliable source of long-term rainfall trends.

1. Introduction Africa, where communities rely heavily on rain-fed agriculture and have limited capacity to adapt (Boko et al. Agriculture is among the sectors that are negatively 2007; UNFCCC 2006). The understanding of how such affected by climate variability and change (Boko et al. climate extremes are changing is vital for planning ap- 2007). Climate variability and change are departures propriate adaptation measures (IPCC 2012; Aguilar from the mean climate of a locality. The mean climate et al. 2009). Reduced yields as a result of unfavorable of a place is the unweighted temporal average over a and/or changing weather and climate leave farmers long period of time (Arguez and Vose 2011). Future vulnerable to food insecurity because of communities’ changes indicate an increase in the frequency and in- limited capacities to adapt to environmental changes. tensity of extreme climate events, such as heat waves, During the last three decades or so, the southern Africa extreme cold spells, droughts, and ﬂoods (Niang et al. region has experienced frequent intense droughts (Vogel 2014). Such changes are likely to have bigger and neg- et al. 2010; Rouault and Richard 2005) that have had a ative impacts on agricultural productivity particularly in negative impact on regional food security. A few studies, using daily data, have been carried out in Zimbabwe (e.g., Corresponding author: Cyril T. F. Murewi, [email protected] Makuvaro 2014; Mazvimavi 2010; Aguilar et al. 2009;

DOI: 10.1175/WCAS-D-16-0029.1 Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/28/21 11:08 PM UTC 36 WEATHER, CLIMATE, AND SOCIETY VOLUME 10

New et al. 2006) to determine the trends in precipitation that climate variability and change were taking a toll on and temperature extremes. their agricultural activities (A. Munodawafa et al. 2009, Zimbabwe is a land-locked country situated in unpublished manuscript). The farmers specifically cited southern Africa. The atmospheric circulation over droughts, flooding, prolonged dry spells, and wet spells southern Africa is dominated by the position and as negatively impacting their farming activities. Exces- strength of the subtropical anticyclones of the Atlantic sive rains resulted in increased livestock diseases, lower and Indian Oceans, and the position of the intertropical crop yields as a result of nutrient leaching, breaking convergence zone (ITCZ). The rainfall mostly occurs down of dam walls, and increasing silt in rivers. On the as a result of the northward and southward migration of other hand, they noted that droughts and prolonged dry the ITCZ and the westerly cloud bands that result from spells resulted in the nonperennial flow of big rivers, the tropical temperate troughs (TTTs) (Mason and Jury reduced yields, and dried-up wells. Other studies have 1997; Usman and Reason 2004; Hart et al. 2010). The also established that rural farmers and communities in moisture supply is mainly from the southwest Indian Africa are indeed aware of climate variability and Ocean (Reason 2007). During drier seasons, anticy- change in their areas (Sanfo et al. 2014; Moyo et al. 2012; clonic circulation patterns dominate the regional airflow Mongi et al. 2010; Akponikpè et al. 2010; Gbetibouo and the ITCZ remains anchored over the north. 2009; Mertz et al. 2009; Maddison 2007; Nhemachena Southern Africa is also affected by tropical cyclones and Hassan 2007). Generally, in these studies farmers (TCs) that develop in the Indian Ocean. Tropical cy- perceived a rise in temperature and drier conditions. In clones may bring in some moisture when they fall over Nhemachena and Hassan (2007), the farmers from land or dry the region when they remain in the Mo- South Africa, Zambia, and Zimbabwe also concurred zambique Channel (Matarira 1990). The rainfall pattern that there were significant changes in the start and end of of southern Africa (including Zimbabwe) is affected by seasons and changes in the frequency of droughts. Sanfo the ENSO signal (Rouault and Richard 2005; Reason et al. 2014, Moyo et al. 2012, Mongi et al. 2010, and Jagadheesha 2005). When there is an El Niño, the Akponikpè et al. 2010, Gbetibouo 2009, and Maddison country mostly experiences droughts, while a La Niña 2007 verified farmers’ perceptions on climate variability often results in favorable rainfall conditions. and change using historical data. In some of the coun- The major challenge in trying to solicit farmers’ per- tries where the investigations were carried out, the his- ceptions is to ascertain how long they recall yesteryear torical data were in agreement with farmers’ perceptions climatic conditions. Farmers whose perceptions were that both rainfall and temperatures had changed; how- considered in this study were mostly in the 51–70-yr-old ever, in other case studies, there was no evidence that age group; hence, the researchers considered it reason- these weather variables had changed. able to work with a climate record of 40 years. The long Smallholder farmers’ perceptions on climate vari- climate record is also ideal for picking up long-term ability and change are vital in determining adaptation trends [World Meteorological Organization (WMO) strategies. Significant changes in climate, rainfall, and requires a period of at least 30 years]. According temperature in particular have a direct influence on to Mertz et al. (2009), rural communities in eastern agricultural productivity and therefore food and nutri- Saloum, Senegal, concurred that temperatures were in- tion security. The objective of this study was to verify creasing throughout the year with cold periods becom- smallholder farmers’ perceptions on climate variability ing shorter and hot ones becoming longer. Hachigonta and change, using historical daily climate data. Similar et al. (2008) argue that information on specific aspects work done in Zimbabwe focused on agroecological re- of a rainy season, such as its start and end and the nature gions (AER) IV and V, which receive low rainfall of the wet and dry spells within it, is vital to farmers’ amounts (less than 650 mm per annum; Vincent and decision-making processes. Therefore, it is important to Thomas 1962) and experience a high mean annual relate trends in the mean climate variables and extreme temperature of 20.58–308C(Muchadeyi et al. 2007). The events with farmers’ perceptions on climate variability agroecological regions in Zimbabwe are a classification and change, as this will provide an appropriate basis for of the agricultural potential—from AER I, which rep- possible adaptation measures. resents the highest altitude and wettest area, receiving According to Mubaya (2010), about 90% of the more than 1000 mm of rainfall per year, to AER V, farmers in Lower Gweru and Lupane, two semiarid which receives the lowest rainfall, amounting to less 2 districts of Zimbabwe, were fully aware of climatic than 450 mm yr 1 and is the driest and the hottest. This variability and change in their surroundings. Case work looks at Lower Gweru, which is in AER III, which studies and interviews conducted in the Lower Gweru receives 650–800 mm per annum, has a relatively longer area in Gweru District revealed that farmers perceived growing season (a mean median of 131 days), and a

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FIG. 1. Mean monthly rainfall for the Gweru meteorological station. lower mean temperature than AERs IV and V (Vincent ward is predominated by wetlands and thus has a high and Thomas 1962). The rainfall season in Zimbabwe water table, Mudubiwa is situated on high ground and normally starts in October and ends in March/April, and therefore has a relatively low water table. Farmers in the peak rainfall period is December–February (DJF). this area practice subsistence farming and market The mean monthly rainfall variation for Gweru is shown gardening. in Fig. 1. In this study, daily data were used, since they Three villages were conveniently selected from each are quicker at reacting to level shifts and changes in ward, and systematic random sampling was employed to trends. While most of the research on the same subject come up with 30 households per village, bringing the of validating farmers’ perceptions of climate variability total number of interviewed farmers to 180. Focus group and change used mean climatic records, our study uses discussions and questionnaires were the main methods both mean and extreme climatic variables. of getting information from the farmers. In focus group discussions, farmers were put in groups of 8–15. Further grouping of farmers by age and gender was done so as to 2. Materials and methods capture different responses from different farmers’ ex- a. Farmers’ location and their perceptions on climate periences on climate variability and change and to variability and change eliminate bias by dominant speakers from large groups. The three age groups used were 30–40 years, 41– Perceptions of farmers from two wards (Mudubiwa 50 years, and 51 years and older. Questions that farmers and Nyama) in the Lower Gweru communal area on were asked included their knowledge of climate vari- climate variability and change were compared with ability and change and their causes, signs they use to trends in average and extreme historical climatic data validate the changes, and start and cessation dates of for the Gweru meteorological station. The communal rainfall, as well as adaptation strategies that they were area is situated about 45 km northwest of Gweru, in undertaking. Gweru District. It lies in AER III, in semiarid central The Lower Gweru smallholder farmer perceptions Zimbabwe. The two wards have an altitude of 1200– as recorded from the baseline survey were as follows: 1345 MSL. increased seasons without enough rainfall, increased Farmer perceptions used in this study were obtained ﬂoods, rains starting late and ending early, extremes in from a baseline survey on ‘‘Building Adaptive Capacity temperatures, long dry spells, and rains coming earlier. to Cope with Increasing Vulnerability due to Climate These perceptions are fairly similar to those identiﬁed Change’’ conducted in the two wards during the 2008/09 for farmers in the semiarid Masvingo and Hwange season, under the International Development Research Districts (Moyo et al. 2012) and Mangwe District Centre Climate Change and Adaptation in Africa (Tshuma and Mathuthu 2014) of Zimbabwe. (IDRC CCAA) project (Mugabe et al. 2010). In that survey, Nyama and Mudubiwa wards (a ward is a geo- b. Meteorological data and analysis graphical location with six–eight villages in it) were conveniently sampled for their proximity to the main Historical data for the Gweru meteorological station road and therefore easy accessibility. While Nyama were obtained from the Department of Meteorological

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TABLE 1. Rainfall and temperature indices used in the study and their deﬁnitions.

Variable Index Measure Temperature Mean Tmax Intensity Mean Tmin Intensity Mean temp Intensity Tmax 90th percentile Frequency Tmin 90th percentile Frequency Heat wave duration 90th percentile heat wave duration (extreme heat wave duration) Frequency % age of days Tmax . 90th percentile Proportion % age of days Tmin . 10th percentile Proportion % age of days Tmin . 90th percentile Proportion Rainfall Mean climatological precipitation Mean Fraction of total precipitation above annual 90th percentile Proportion Fraction of total precipitation above annual 95th percentile Proportion No. of days of precipitation $ 10 mm Frequency Max no. of consecutive dry days (longest dry spell) Frequency Max no. of consecutive wet days (longest wet spell) Frequency Mean wet spell lengths (days) Frequency Greatest 3-day total rainfall Intensity Greatest 5-day total rainfall Intensity Greatest 10-day total rainfall Intensity Simple daily intensity (amount of rain per rain day) Intensity % of total rainfall from events . long-term 90th percentile Proportion No. of events . long-term 90th percentile Frequency

Services, Zimbabwe. Quality control and homogeneity rainfall amount and not followed by a long dry spell tests were performed on the daily meteorological data. during the subsequent weeks’’ (Boyard-Micheau et al. The Statistical and Regional Dynamical Downscaling 2013, p. 8916). Using these criteria, the definition for the of Extremes for European Regions (STARDEX) soft- start of a season adopted by Reason et al. (2005) in ware (http://www.cru.uea.ac.uk/projects/stardex)(Haylock southern Africa is 25 mm in the first two pentads fol- et al. 2006) was used to determine trends in mean and lowed by 20 mm of rainfall in the next 20 days. Dimes extremes of the daily rainfall and temperature data for et al. (2009) point out that a sowing criterion of 20 mm of the Gweru meteorological station. Selected indices for rainfall received within 5 days during a particular sowing average and extreme temperature and rainfall (Table 1) window is common. In Zimbabwe, the sowing window trends were compared to farmer perceptions. These can be taken to be the period between 15 November and indices were linked to extreme events, such as floods 15 January. Days with rainfall greater than 10 mm and and droughts (wet and dry spells), and heat waves. In the greatest amount of rainfall received during 5 con- this study, a wet day was defined as a day on which 1 mm secutive days for September–November (SON) and or more of rainfall was received and a dry day was March–May (MAM) were considered as the start and considered as one that received less than 1 mm of end of a season, respectively. rainfall. Usman and Reason (2004) also used this defi- All computations for the mean and extreme indices nition in their study for southern Africa dry spells. were done relative to the base period 1962–82 for the For the start and cessation of the season, proxy indices Gweru climate data of 1962–2005. were used as a result of the absence of ones that fully c. Indices that were used to validate the farmer describe these parameters of the season. It seems there perceptions are no standard criteria for deciding the start and end of a season as revealed by the variations in defini- Rainfall and temperature indices used in the study tions used by different practitioners (e.g., agronomists, were measures of intensity, frequency, and proportion agroclimatologists, and hydrologists) (Sanfo et al. 2014; of the total (Table 1). Rainfall-related indices used were Boyard-Micheau et al. 2013; Fosu-Mensah 2012; the mean climatological precipitation, the mean wet Hachigonta et al. 2008; Reason et al. 2005). A definition spell lengths, the number of days precipitation was commonly used by agroclimatologists for start of a greater than 10 mm, the greatest 10-day total rainfall, season is ‘‘the first wet day of a spell receiving a given the number of the longest wet and dry spells, the number

Unauthenticated | Downloaded 09/28/21 11:08 PM UTC JANUARY 2018 M A K U V A R O E T A L . 39 of rainfall events greater than the 90th percentile, the found a nonsignificant annual trend in the longest proportion of total rainfall received from rainfall events wet spell for some stations in Zimbabwe during the greater than the 90th percentile, and the amount of period 1955–2006. In a similar extreme rainfall study, rainfall per rainy day. Temperature-related perceptions Makuvaro (2014) established nonsignificant long wet were compared to trends in mean maximum tempera- spell trends for Bulawayo during OND and JFM for the ture, maximum temperature above the 90th percentile period 1970–2007. In the same study, rainfall amounts (hottest day temperature), percentage of days with a and the frequency of heavy precipitation events showed maximum temperature above the 90th percentile (fre- no significant trends ( p . 0.05). Mazvimavi (2010) also quency of hot days), mean minimum temperature, established no significant trends in heavy precipitation minimum temperature above the 90th percentile (hot- for several stations in Zimbabwe for intervals starting test night temperature), and percentage of days with a during the 1892–1941 period and ending in 2000. Aguilar minimum temperature above the 90th percentile (fre- et al. (2009) also noted a nonsignificant trend in quency of hot nights) (Table 1). consecutive dry days (longest dry spell) for Zimbabwe for the period 1955–2006, while Makuvaro (2014) obtained a significant increase in the longest dry spell for 3. Results and discussion Bulawayo station, in western Zimbabwe, during OND. The greatest 3- and 5-day total rainfall indices recor- a. Historical meteorological data ded significant positive trends ( p , 0.05)—0.6493 mm per 3 days ( p 5 0.01) and 1.0171 mm per 5 days ( p 5 1) RAINFALL 0.0187), respectively,—for MAM. These rainfall in- The mean climatological precipitation showed a sig- tensity measures registered nonsignificant trends ( p . nificant ( p 5 0.0385) positive trend for MAM (end of 0.05) for DJF, JJA, and SON, and for the year (results rainfall season) (Fig. 2a) and nonsignificant ( p . 0.05) for these are not shown in the figures). The percentage trends for DJF (peak summer season), SON (start of of total rainfall from events greater than the long-term rainfall season), and JJA. The annual trend for this in- 90th percentile (proportion measure) and the number of dex was also not significant ( p . 0.05). The results for events greater than the long-term 90th percentile (fre- DJF, JJA, and SON are not shown. quency measure) indices revealed nonsignificant trends The number of days with precipitation greater than ( p . 0.05) for the seasons and for the year (the results 10 mm (frequency measure), the greatest 10-day total for these are not shown in the figures). rainfall, and the amount of rain per rainy day (both in- Variations in nature of trends among the cited results tensity measures) revealed significant positive trends (based on climate records) are mainly due to differences for MAM. The three indices showed increases of in seasons and the period of study considered, as well as 2 2 0.0612 days yr 1, 0.9395 mm yr 1, and 0.1092 mm (rain the geographical coverage (single station vs average 2 2 day) 1 yr 1, respectively, with p values of 0.0153, 0.0255, over several stations). Also, rainfall in Zimbabwe is and 0.0139, respectively (Figs. 2b–d). The number of highly variable both spatially and temporally, hence days with precipitation greater than 10 mm and the different outcomes in trend analyses are expected for greatest 10-day total rainfall indices registered non- different locations. significant trends ( p . 0.05) for DJF, JJA, and SON, and 2) TEMPERATURE for the annual season (results for these are not shown in the figures). The amount of rain per rain day index also The long-term time series for Gweru mean maximum recorded a significant ( p 5 0.0033) positive annual trend temperature is indicative of a significant ( p 5 0.003) 2 2 2 of 0.0575 mm (rain day) 1 yr 1 (Fig. 2e). The amount of annual rise of 0.02128Cyr 1 for the 43-yr period of rain per rain day index also registered nonsignificant analysis (Fig. 3). Seasonal trends for this index were also ( p . 0.05) trends for DJF, JJA, and SON. significantly positive for JJA ( p 5 0.003) and SON ( p 5 2 Nonsignificant trends ( p . 0.05) in the mean wet spell 0.0473), being 0.02458 and 0.01858Cyr 1, respectively length, the longest dry spells, and the longest wet spells (Fig. 3). (frequency measures) were obtained for all seasons and The maximum temperature above the 90th percen- for the year. Makuvaro (2014) also found nonsignificant tile (hottest day temperature) showed a significant ( p 5 2 trends in the mean wet spell length for the first half of the 0.0012) annual increase of about 0.02938Cyr 1 for rainfall season, October–December (OND), and for the the study period (Fig. 4a). All 3-month periods ex- second half of the rainfall season, January–March cept MAM also showed significant positive trends: 2 2 (JFM), as well as for the annual trend for Bulawayo 0.02938Cyr 1 ( p 5 0.0453) for DJF, 0.04618Cyr 1 ( p 5 2 station in southwestern Zimbabwe. Aguilar et al. (2009) 0.0005) for JJA, and 0.02478Cyr 1 ( p 5 0.0162) for SON

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FIG. 2. Time series variations with linear trend lines for the rainfall extreme indices for the Gweru meteorological station: (a) MAM seasonal anomalies, (b) MAM seasonal number of days with rainfall greater than 10 mm, (c) MAM seasonal greatest 10- day rainfall total, (d) MAM seasonal simple daily intensity, and (e) annual simple daily intensity.

(Figs. 4b–d, respectively). The percentage of days with and 0.0025 (at p 5 0.0136), respectively, were obtained maximum temperature above the 90th percentile (fre- (Figs. 5b,c, respectively). quency of hot days) showed a signiﬁcant (p 5 0.0009) Unganai (1996) showed a similar (positive) annual 2 annual increase of 0.0021 days yr 1 (Fig. 5a), while for trend of 0.68C in mean maximum temperature and sea- JJA and SON signiﬁcant trends of 0.0026 (at p 5 0.0002) sonal trends ranging from 10.1 to 10.8 for the Harare

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FIG. 3. Time series variations with trend lines for the mean maximum temperature for the Gweru meteorological station (JJA, SON, and annual). meteorological station, north of Zimbabwe, for the pe- The number of days above the 90th percentile of a heat riod 1897–1993. Makuvaro (2014) also found significant wave (the duration of the longest heat wave) showed a 2 increases in maximum temperature above the 90th significant (p 5 0.0018) increase of 0.0997 days yr 1, 2 2 percentile (hottest day temperature) and in hot day and of 0.053 days yr 1 for SON and 0.0253 days yr 1 for frequency during winter (JJA), spring (SON), and DJF (Fig. 9). The trends for the other seasons were in- for the year for Bulawayo station (southwestern significant (p . 0.05). Zimbabwe) for the period 1978–2007. b. Climate variability The mean minimum temperature showed a significant 2 (p 5 0.0002) positive annual trend of 0.02048Cyr 1 over RAINFALL VARIABILITY the analysis period (Fig. 6a). All 3-month periods except 2 DJF had positive trends: 0.02068Cyr 1 (p 5 0.0365) for The rainfall anomalies reveal high variability as 2 MAM, 0.01918Cyr 1 (p 5 0.0310) for SON, (Fig. 6b) depicted in Fig. 2a for MAM. Similar results are ob- 2 and 0.02868Cyr 1 (p 5 0.001) for JJA (Fig. 6a). tained for the other seasons and for the annual series The annual and seasonal (except SON) trends for (not shown). All the precipitation and temperature in- minimum temperature above the 90th percentile dices considered in this study (Table 1) also reveal high (hottest night temperature) were significantly positive temporal variations (Figs. 2, 4, 5, 7, 8, 9). Figures 3, 6 2 (Fig. 7). The annual change was 0.01878Cyr 1 (p 5 show the temporal variations for the mean maximum 0.0058) for the analysis period, while seasonal increases and minimum temperatures, respectively. 2 2 of 0.04528Cyr 1 (p 5 0.0002), 0.0218Cyr 1 (p 5 2 c. Comparison between farmer perceptions and 0.0403), and 0.01578Cyr 1 (p 5 0.0374) were noted for historical climate data JJA, MAM, and DJF, respectively (Fig. 6), for the same period. The percentage of days with minimum temper- 1) INCREASED SEASONS WITHOUT ENOUGH ature above the 90th percentile (frequency of hot nights) RAINFALL showed a significant (p 5 0.0012) annual increase of 0.0018 and an increase of 0.0026 (p 5 0.001) for JJA To verify the farmers’ perception that there were in- (Fig. 8). Similar to findings from this study, Unganai creased seasons without enough rainfall, the mean cli- (1996) established positive annual and seasonal trends in matological precipitation, the longest wet spell, the mean minimum temperature for Harare station, while mean wet spell lengths, the number of days of pre- Makuvaro (2014) found significant increases in mini- cipitation with greater than 10 mm, the greatest 10-day mum temperatures above the 90th percentile (hottest total rainfall, and the amount of rain per rainy were nighttime temperatures) and in hot night frequency for considered. In summary, all the mean and extreme in- SON and JJA, respectively, for Bulawayo station. dices used to verify the farmers’ perception that there

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FIG. 4. Time series variations with linear trend lines for the extreme (90th percentile) maximum temperature for the Gweru meteorological station: (a) annual, (b) DJF, (c) JJA, and (d) SON.

were increased seasons without enough rainfall showed Lower Gweru in this study, Jiri et al. (2015) found that nonsignificant (p . 0.05) trends except for the positive farmer perceptions of reduced annual rainfall agreed trends during MAM. These positive trends for the end of with observed climate records for Chiredzi District in season are indicative of rainfall increases contrary to southeastern Zimbabwe for the period 1980–2011. farmers’ perceptions that there are increased seasons Trends in the mean and extreme rainfall indices, con- without enough rainfall. Increased rainfall amounts at sidered above, did not support Lower Gweru farmers’ the end of the growing season may lead to crop loss due perception that the number of seasons without enough to infestation of crop products by bacterial and fungal rainfall had increased. diseases while the crop is still in the fields awaiting 2) INCREASED FLOODS harvesting. On a positive note, more rainfall toward the end of the season will provide conducive conditions for The farmers’ perception of increased frequency of autumn plowing in preparation for the establishment of floods was validated using the number of the longest wet the subsequent crop. Contrary to results obtained for spells, the number of rainfall events greater than the

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FIG. 5. Time series variations with linear trend lines for the frequency of extreme (90th percentile) maximum temperature for the Gweru meteorological station: (a) annual, (b) JJA, and (c) SON.

90th percentile, the proportion of total rainfall received need to introduce more efficient flood warning systems from rainfall events greater than the 90th percentile, and to alert the farmers of any flooding should it occur. the amount of rainfall per rainy day. The lack of sig- Flooding would also impact negatively on the food and nificant (p . 0.05) annual and seasonal trends in the nutrition security of the farmers, since it may result in number of the longest wet spells, the number of rainfall poor yields because of leaching and other plant growth events greater than the 90th percentile, and the pro- problems associated with waterlogging. portion of total rainfall received from rainfall events 3) RAINS START LATE AND END EARLY greater than the 90th percentile does not support farmers’ perception that floods had increased. The Proxy indices for the start and end of a season were change in amount of rainfall per rainy day was also not used, as there were no climatological indices directly significant except for MAMm which showed a positive linked to definitions of ‘‘start’’ and ‘‘end’’ of the growing 2 2 trend of 0.1092 mm (rain day) 1 yr 1 (p 5 0.0139). season (see section 2b). The days with rainfall greater Generally, the results lacked evidence of an increase in than 10 mm, the amount of rain per rain day, and the heavy precipitation except toward the end of the rain greatest amount of rainfall received during 10 consecu- season (MAM period). An increase in floods suggests a tive days for SON—the most likely rainfall onset

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FIG. 6. Time series variations with linear trend lines for the mean minimum temperature for the Gweru meteorological station: (a) annual and JJA, and (b) MAM and SON. period—showed nonsignificant (p . 0.05) trends. Thus, and an extended growing season (established in this there is insufficient evidence of a delayed start of the study) will probably leave farmers to continue growing season for the period of analysis (1962–2005), as re- the same crops but also to grow medium-maturity va- vealed by nonsignificant (p . 0.05) trends in these rieties as opposed to be confined to short-season vari- proportion, frequency, and intensity of rainfall- eties. The extended season could also allow for double measuring indices. Simelton et al. (2013) also found no cropping by establishing a second early maturing crop, evidence to support perceptions of a late start to the like sugar beans or cowpeas. season by farmers in Botswana and southern Malawi. 4) LONG DRY SPELLS For cropping purposes a significant decrease in rainfall during the likely period for the start of the rains is an The seasonal and annual trends in the consecutive dry indicator of a delayed onset of the season, and farmers days (frequency measure) were analyzed. The longest start planting later only when sufficient rains are re- dry spells trends were not significant (p . 0.05), contrary ceived. The farmers also perceived that the rains were to what farmers in Lower Gweru perceived. Thus, the ending early. To verify this, the study considered the results of this study show no evidence of longer dry spells days with rainfall greater than 10 mm, the amount of rain than the long-term average for Lower Gweru. Increased per rain day, and the greatest amount of rainfall received frequency of long dry spells will compel farmers to during 10 consecutive days for the MAM period of the embark on strategies for managing dry spells, such as rainfall season. All the three indices (measures of pro- growing drought-tolerant crops, employing moisture portion, frequency, and intensity) showed significant conservation techniques, and putting more emphasis on (p 5 0.0153, 0.0139 and 0.0255, respectively) positive livestock enterprises. 2 2 2 trends (0.0612 days yr 1,0.1092mm(rainday) 1 yr 1, Overall, there was limited agreement between 2 2 and 0.9395 mm day 1 yr 1, respectively) (Figs. 2b–d). farmers’ perceptions and historical mean and extreme Thus, these indices contradict farmers’ perception that climate data with respect to rainfall amount and patterns seasons were ending early. If rains start late and end (Table 2). The lack of match between the two sources of early, as perceived by most of the farmers in Lower information found in this study is probably due to the Gweru, it means shortening of the growing season. differences in the reference periods. Whereas extreme However, the analysis from this study indicates no climate trends used in this study are for a relatively long change to the start of the season and a delayed end of time frame (1962–2005), the farmers may not have the season, which means an extended growing season. considered a long period such as that used by the re- A combination of no change in rainfall frequency and searchers in trying to remember past years. Their re- intensity during the greater part of the growing season sponses could have been based on relatively recent years

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FIG. 7. Time series variations with linear trend lines for the extreme (90th percentile) minimum temperature for the Gweru meteorological station: (a) DJF, (b) MAM, (c) JJA, and (d) annual.

and/or on years that were extremely ‘‘bad’’ or ‘‘good’’; showed that farmers’ perceptions were true for partic- furthermore, as alluded to by previous researchers (e.g., ular years, especially those that received below- or Sanfo et al. 2014; Simelton et al. 2013, Gbetibouo 2009), above-average rainfall. these bad and good years are not named as such on the 5) EXTREME TEMPERATURES basis of climatic factors but rather on agricultural output, which is affected by a plethora of factors. During The mean and extreme temperature indices com- the period of the study, southern Africa has been af- puted in section 3a(2) show that temperatures have fected by bad years (1982/83, 1991/92, 2002/03, 2004/05, been increasing as noted by the farmers. The warming 2005/06, and 2007/08 droughts), as well as good years of temperatures also agrees with Aguilar et al. (2009) (1979/80, 1997/98, and 1999/2000 wet rainfall seasons). and New et al.’s (2006) ﬁndings of small increases It appears the farmers are more concerned or conscious in maximum temperature trends for Zimbabwe for about inter- to intraseasonal rainfall variability than the periods 1955–2006 and 1961–2000, respectively. climate change. This assertion is conﬁrmed by the re- Other studies in Zimbabwe also showed upward trends sults of analyses by some researchers (e.g., Sanfo et al. in maximum temperature (e.g., Jiri et al. 2015 for 2014; Simelton et al. 2013; Moyo et al. 2012), which Chiredzi; Moyo et al. 2012 for Hwange and Masvingo).

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FIG. 8. Time series variations with linear trend lines for the frequency of extreme (90th percentile) minimum temperature for the Gweru meteorological station: (a) annual, (b) JJA, (c) SON, (d) DJF.

Elsewhere in Africa, similar results were reported also veriﬁed in this study, call for development of crop (e.g., Sanfo et al. 2014 for Burkina Faso; Mongi et al. varieties and animal breeds that are heat stress 2010 for Tanzania; Gbetibouo et al. 2009 for West tolerant. Africa). From an agricultural viewpoint, the increase in There is much agreement between farmers’ percep- temperature may directly inﬂuence the biophysical tion and historical climate data regarding an increase in processes of plants and animals, leading to poor pro- temperature. Other researchers also found similar re- ductivity. The increase in the extreme temperature sults (e.g., Jiri et al. 2015; Sanfo et al. 2014; Simelton may increase pest and disease incidences and may in- et al. 2013; Mongi et al. 2010; Moyo et al. 2012; crease rates of evaporation, leading to increased crop Maddison 2007). The reduced coherence between the water requirements and reduced water availability. different sources of climate information (farmer per- Thus, these changes could contribute to reduced agri- ceptions and climate records) pertaining to rainfall, cultural productivity and threaten food security in the compared to temperature, could be due to less vari- area. Temperature increases, as noted by farmers and ability in temperature compared to rainfall.

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FIG. 9. Time series variations with linear lines for the longest heat wave duration for the Gweru meteorological station: (a) annual, (b) SON, and (c) DJF.

4. Conclusions positive trends for the end of season. Intensity measures of rainfall (the greatest 10-day total rainfall, the amount This study sought to compare the Lower Gweru of rain per rain day, and simple daily intensity) were farmer perceptions on climate variability and change as mostly nonsignificant except for the end of rainfall sea- of 2008 with trends in historical climate records (1962– son window. The proportion measure for rainfall used— 2005). The mean and extremes from the historical me- that is, the percentage of total rainfall from events teorological data for both rainfall and temperatures are greater than the long-term 90th percentile—was non- characterized by high interannual variability. Some of significant. The local farmers’ perceptions on rainfall the trends in these mean and extreme values were sig- were that they had experienced increased seasons nificant (p , 0.05), while others were nonsignificant. without enough rainfall, increased floods, rains starting Most of the rainfall frequency measures (the mean late and ending early, and long dry spells. The major wet spell length, the longest dry and wet spells, the conclusion from this study was that the Lower Gweru number of events greater than the long-term 90th per- farmers’ perceptions on rainfall amount and pattern did centile) used in this study registered nonsignificant not correspond with climatic trends. Thus, farmers in seasonal and annual trends. However, precipitation Lower Gweru may not be a reliable source of long-term greater than 10 mm revealed significant (p , 0.05) changes in rainfall, but they could provide reliable

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TABLE 2. Summary of comparisons between farmer perceptions the STARDEX software used in this study and for his and historical climate data. assistance with the interpretation of the various extreme Relationship to climate indices. The farmers’ perceptions are an output Farmer perception assessed historical data from a project funded by the International Develop- Increased seasons without Do not match ment Research Centre Climate Change and Adaptation enough rainfall in Africa (IDRC CCAA) and DFiD project (Grant Increased floods Do not match 104144). The views expressed are not necessarily those Long dry spells Do not match of CCAA/IDRC/DFiD. Late start of seasona Do not match Early end of seasona Do not match Extremes in temperatures Agree REFERENCES a Proxy precipitation indices rather than direct ones were used to Aguilar, E., and Coauthors, 2009: Changes in temperature and compare with farmer perceptions. precipitation extremes in western central Africa, Guinea Conakry, and Zimbabwe, 1955–2006. J. Geophys. Res., 114, 1955–2006, https://doi.org/10.1029/2008JD011010. information on inter- and intraseasonal rainfall changes. Akponikpè, P. B. I., P. Johnston, and E. K. Agbossou, 2010: It appears that for a variable climate element like rain- Farmers’ perception of climate change and adaptation fall, studies on comparisons of perceptions and trends strategies in Sub-Saharan West-Africa. Second Int. Conf. on need to be done on smaller time scales, for example, Climate, Sustainability and Development in Semi-Arid Re- gions (ICID 2010), Fortaleza, Ceará, Brazil, IISD, ICID118, decadal periods rather than on long periods. This is 14 pp. particularly important when such studies seek commu- Arguez, A., and R. S. Vose, 2011: The definition of the standard nities’ responses. WMO climate normal: The key to deriving alternative climate On the other hand, their perceptions on temperature normals. Bull. Amer. Meteor. Soc., 92, 699–704, https://doi.org/ changes were in total agreement with the meteorological 10.1175/2010bams2955.1. Boko, M., and Coauthors, 2007: Africa. Climate Change 2007: trends. The mean maximum temperature and the fre- Impacts, Adaptation and Vulnerability, M. L. Parry et al., Eds., quency of hot days were characterized by significant Cambridge University Press, 433–467. (p , 0.05) positive trends for the year and for the dry Boyard-Micheau, J., P. Camberlin, and N. Philippan, 2013: seasons, though the wet seasons showed nonsignificant Regional-scale rainy season onset detection: A new approach trends. The hottest day temperature showed warming based on multivariate analysis. J. Climate, 26, 8916–8928, for most of the seasons and for the year. The longest heat https://doi.org/10.1175/JCLI-D-12-00730.1. , Dimes, J., P. Cooper, and K. P. C. Rao, 2009: Climate change im- wave duration revealed significant (p 0.05) increases pact on crop productivity in the semi-arid tropics of Zimbabwe in the second half of the year and on an annual basis. in the 21st century. Increasing the productivity and sustain- Gweru station meteorological data showed significant ability of rainfed cropping systems of poor smallholder (p , 0.05) increases in the mean minimum temperature farmers: Proceedings of the CGIAR Challenge Program on over the years and for most of the seasons. The hottest Water and Food International Workshop on Rainfed Crop- , ping Systems, E. Humphreys and R. S. Bayot, Eds., CGIAR nights and their frequency registered significant (p Challenge Program on Water and Food, 189–198. 0.05) increases annually and for most of the seasons. The Fosu-Mensah, B. Y., 2012: Modelling maize (Zea mays L.) Lower Gweru farmers had noted extreme temperatures. productivity and impact of climate change on yield and Meteorological data revealed that both the mean and nutrient utilization in sub-humid Ghana. Ph.D. thesis, extremes of maximum and minimum temperatures had Kwame Nkrumah University of Science and Technology, ZEF Ecology and Development Series 87, 171 pp., http://www.zef. generally showed signs of warming. de/fileadmin/webfiles/downloads/zefc_ecology_development/ The study recommends that a broader study on veri- eds_87_fosu-mensah_text.pdf. fying farmer perceptions using historical climatic data Gbetibouo, G. A., 2009: Understanding farmers’ perceptions and across the country be carried out. Findings from such adaptations to climate change and variability: The case of the studies would help smallholder farmers, policy makers, Limpopo Basin, South Africa. International Food Policy Re- and researchers in finding ways to combat climate vari- search Institute Discussion Paper 00849, 36 pp., http://www. ifpri.org/publication/understanding-farmers-perceptions-and- ability and change. adaptations-climate-change-and-variability. Hachigonta, S., C. J. C. Reason, and M. Tadross, 2008: An Acknowledgments. The authors thank staff mem- analysis of onset date and rainy season duration over Zambia. bers from the Department of Meteorological Services, Theor. Appl. Climatol., 91, 229–243, https://doi.org/10.1007/ Zimbabwe, for providing the meteorological data. We s00704-007-0306-4. Hart, N. C. G., C. J. C. Reason, and N. Fauchereau, 2010: Tropical– are also grateful to Dr. Steven Crimp of the Common- extratropical interactions over southern Africa: Three cases of wealth Scientific and Industrial Research Organisation heavy summer season rainfall. Mon. Wea. Rev., 138, 2608– (CSIRO), Canberra, Australia, who facilitated access to 2623, https://doi.org/10.1175/2010MWR3070.1.

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Haylock, M. R., G. C. Cawley, C. Harpham, R. L. Wilby, and C. M. New, M., and Coauthors, 2006: Evidence of trends in daily climate Goodess, 2006: Downscaling heavy precipitation over the extremes over southern and west Africa. J. Geophys. Res., 111, United Kingdom: A comparison of dynamical and statistical D14102, https://doi.org/10.1029/2005JD006289. methods and their future scenarios. Int. J. Climatol., 26, 1397– Nhemachena, C., and R. Hassan, 2007: Micro-level analysis of 1415, doi:10.1002/joc.1318. farmers’ adaptation to climate change in southern Africa. In- IPCC, 2012: Managing the Risks of Extreme Events and Disasters to ternational Food Policy Research Institute Discussion Paper Advance Climate Change Adaptation. C. B. Field et al., Eds., 00714, 31 pp. Cambridge University Press, 582 pp. Niang, I., O. C. Ruppel, M. A. Abdrabo, A. Essel, C. Lennard, Jiri, O., P. Mafongoya, and P. Chivenge, 2015: Smallholder farmer J. Padgham, and P. Urquhart, 2014: Africa. Climate Change perceptions on climate change and variability: A predisposition 2014: Impacts, Adaptation, and Vulnerability, V. R. Barros for their subsequent adaptation strategies. J. Earth Sci. Climatic et al., Eds., Cambridge University Press, 1199–1265. Change, 6, 277, https://doi.org/10.4172/2157-7617.1000277. Reason, C. J. C., 2007: Tropical cyclone Dera, the unusual 2000/01 Maddison, D., 2007: The perception of and adaptation to cli- tropical cyclone season in the South West Indian Ocean and as- mate change in Africa. World Bank Policy Research Working sociated rainfall anomalies over southern Africa. Meteor. Atmos. Paper WPS4308, 51 pp., https://openknowledge.worldbank.org/ Phys., 97, 181–188, https://doi.org/10.1007/s00703-006-0251-2. handle/10986/7507. ——, and D. Jagadheesha, 2005: A model investigation of recent Makuvaro, V., 2014: Impact of climate change on smallholder ENSO impacts over southern Africa. Meteor. Atmos. Phys., productivity in Zimbabwe, using a modelling approach. Ph.D. 89, 181–205, https://doi.org/10.1007/s00703-005-0128-9. thesis, University of the Free State, 296 pp. ——, S. Hachigonta, and R. F. Phaladi, 2005: Interannual vari- Mason, S. J., and M. R. Jury, 1997: Climatic variability and change ability in rainy season characteristics over the Limpopo region over southern Africa: A reﬂection on underlying pro- of southern Africa. Int. J. Climatol., 25, 1835–1853, https://doi. cesses. Prog. Phys. Geogr., 21, 23–50, https://doi.org/10.1177/ org/10.1002/joc.1228. 030913339702100103. Rouault, M., and Y. Richard, 2005: Intensity and spatial extent of Matarira, C. H., 1990: Drought over Zimbabwe in a regional and droughts in southern Africa. Geophys. Res. Lett., 32, L15702, global context. Int. J. Climatol., 10, 609–625, https://doi.org/ doi:10.1029/2005GL022436. 10.1002/joc.3370100605. Sanfo, S., J. P. A. Lamers, M. Mueller, and W. Fonta, 2014: Farmers’ Mazvimavi, D., 2010: Investigating changes over time of annual perception of climate change and variability versus climatic rainfall in Zimbabwe Afr. Hydrol. Earth Syst. Sci., 14, 2671– evidence in Burkina Faso, West Africa. Climate Change in 2679, https://doi.org/10.5194/hess-14-2671-2010. Africa: Negotiations, Translations, and Socio-Political Implica- Mertz, O., C. Mbow, A. Reenberg, and A. Diouf, 2009: Farmers’ tions, Bonn, Germany, Center for Development Research perceptions of climate change and agricultural adaptation (ZEF), 16 pp., https://doi.org/10.13140/2.1.4614.7528. strategies in rural Sahel. Environ. Manage., 43, 804–816, Simelton, E., and Coauthors, 2013: Is rainfall really changing? https://doi.org/10.1007/s00267-008-9197-0. Farmers’ perceptions, meteorological data, and policy impli- Mongi, H., A. E. Majule, and J. G. Lyimo, 2010: Vulnerability and cations. Climate Dev., 5, 123–138, https://doi.org/10.1080/ adaptation of rain fed agriculture to climate change and var- 17565529.2012.751893. iability in semi-arid Tanzania. Afr. J. Environ. Sci. Technol., 4, Tshuma, N., and T. Mathuthu, 2014: Climate change in Zimbabwe: 371–381, https://doi.org/10.5897/ajest09.207. Perceptions of smallholder farmers in Mangwe District. Int. Moyo, M., B. M. Mvumi, M. Kunzekweguta, K. Mazvimavi, J. Humanit. Soc. Stud., 2 (5), 318–325. P. Craufurd, and P. Dorward, 2012: Farmer perceptions on UNFCCC, 2006: Climate change: Impacts, vulnerabilities and ad- climate change and variability in semi-arid Zimbabwe in re- aptation in developing countries. United Nations Framework lation to climatology evidence. Afr. Crop Sci. J., 20 (Suppl. 2), Convention on Climate Change, 64 pp., https://unfccc.int/ 317–335. resource/docs/publications/impacts.pdf. Mubaya, C. P., 2010: Farmer strategies towards climate variability Unganai, L. S., 1996: Historic and future climatic change in Zimbabwe. and change in Zimbabwe and Zambia. Ph.D. thesis, University Climate Res., 6, 137–145, https://doi.org/10.3354/cr006137. of the Free State, 259 pp. Usman, M. T., and C. J. C. Reason, 2004: Dry spell frequencies and Muchadeyi,F.C.,C.B.A.Wollny,H.Eding,S.Weigend,S.M. their variability over southern Africa. Climate Res., 26, 199– Makuza, and H. Simianer, 2007: Variation in village chicken 211, https://doi.org/10.3354/cr026199. production systems among agro-ecological zones of Zimbabwe. Vincent, V., and R. G. Thomas, 1962: An Agricultural Survey of Trop. Anim. Health Prod., 39, 453–461, https://doi.org/10.1007/ Southern Rhodesia. Part I: Agro-Ecological Survey. Federa- s11250-007-9050-0. tion of Rhodesia and Nyasaland, 217 pp. Mugabe, F. T., and Coauthors, 2010: Building adaptive capacity to Vogel, C., I. Koch, and K. V. Zyl, 2010: ‘‘A persistent truth’’— cope with increasing vulnerability due to climate change. Reﬂections on drought risk management in southern Africa. Wea. IDRC Final Tech. Rep. 104144, 175 pp. Climate Soc., 2, 9–22, https://doi.org/10.1175/2009WCAS1017.1.

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