prolonged drought and dry spells. Such finding was similar to results from another study conducted in Iringa whereby temperature was reported to increase with increased prolonged droughts and dry spells (Pauline and Grab, 2018). In the studied villages, farmers mentioned the increase of temperature caused occurrences of drought and dry spells with the most notable years that were 1979/1980; 1987/1988; 1999/2000; 2011/2012; and 2012/2013. The condition was also felt by farmers in other parts of Tanzania, for example, the study by Pauline and Grab (2018) found that farmers in Iringa district, especially at Ruaha Mbuyuni, Ibohora and Ikuvala villages perceived the increasing temperature led to occurrences of drought events in 1992/1993; 1999/2000; 2008/2009; 2009/2010; and 2011/2012. Other studies by Kangalawe and co-workers (2016) as well as Teye and colleagues (2015) recognized similar results in the western part of Tanzania and in the upper east region of Ghana, respectively, that local communities perceived increases in temperatures.
4.5.6 Climate Change Impacts on Maize Production
Interviews with farmers, NGOs, village leaders and district as well as village agriculture experts showed that climate change had negatively affected maize production. The most notable effects included decline in maize production associated by prolonged drought, crop failure, dry crops, crop pest or rodents, crop disease, erratic rainfall, short rainfall and increasing soil aridity (Figure 4.12). Results from this study on the impacts of CC on maize production as depicted in Table 4.5 are alike from findings from some other interrelated studies. For example, Msowoya and colleagues (2016) in the Warm Heart of Africa, found that by the mid-century rain-fed maize in Lilongwe District will decrease up to 14 percent and at the end of the century, there will 33 percent loss of maize due to climate change. Such declines will significantly affect Malawi’s maize production.
The main CC impact on maize production in the study area was crop failure (Figure 4.12) caused by CC. Such pattern was reported before in the study by Nelson and colleagues (2009) who found that maximum temperatures and changes in precipitation patterns have decreased productivity of sorghum by 2 percent and wheat by 35 percent across Sub-Saharan Africa. Similar to studies by Lobell and colleagues (2008) as well as Schlenker and Lobell (2010), it was elaborated that Southern African countries mainly, South Africa and Zimbabwe are the most susceptible to climate change with 30 percent yield losses.
Another concern raised by respondent household heads and other stakeholders was that CC has led to drying of crops due to prolonged dry spells and decrease in precipitation. The observed findings showed that in 1979, 1987, 2000 and 2006, farmers reported drying of maize plants while in their farms. The study by Simba and colleagues (2012) in semi-arid Masvingo Province in Zimbabwe, provided evidence that temperatures increased with maximum rates of evapotranspiration, leading to drying of crops because they were easily moisture stressed. Also studies carried out by Borell (2017) on global food demand rises were able to cite climate change as hitting staple crops in Australia. They held that hot, dry weather led to drying of crops, which contributed to reduction of average yields from estimated 1-1.5 tonnes per hectare to present 0.1-0.5 tonnes per hectare.
Crop pests and diseases were reported to increase by the household head respondents in the study area. Consulted stakeholders associated the increasing of pest and diseases to be caused by CC. This result is consistent with findings by FAO (2008) on climate change and trans-boundary pests and diseases as evident to developing countries with high reliance on farming as being the most susceptible to the current dynamic patterns of plant pests (insects, pathogens, and weeds) and disease (fungi). Hundreds of millions of local farmers depend only on cultivations and for their survival, they have been affected by crop pests and disease. For instance, the outbreak of desert locusts in Africa in 2003-2004 affected more than 12 million hectares in 20 countries (ibid.). The same scenario was also supported by Niang and colleagues (2014) whereby they held that there was an increasing incidence of diseases, pests and weed outbreaks as CC outcomes.
Farmers mentioned that low soil fertility has affected maize production in the study area. This finding also supports views by One Acre Fund who mentioned that the whole area of Ismani plains has declined soil fertility due to extensive farming system with poor management that took place in early 1960s to 1980s. This result is consistent with findings by Croon and co-authors (1984) in Southern highland of Tanzania whereby it was revealed that rapid increase in production by oxen and tractors in Ismani plains contributed to decline in soil fertility due to inadequate replacement of soil nutrients. Higher levels of inputs, mainly seeds, fertilizers, and insecticides together with tractor operations due to poor technical management led to decline in yield level from 3400 kg/ to 1200 kilogramme per hectare (kg/ha) in the early 1970s (ibid.).
Moreover, CC has contributed to occurrences of hunger, leading to offered food relief in the study villages. During the FGD and in-depth interviews, majority of households reported having food shortages that led to need for food relief. For instance, 70 percent of respondent households reported being provided food relief in 2000, 2003, 2005, 2010, 2011 and 2012. This is in the same line from the study done by Fews (2000) on Tanzania Food Security Update: September 15, 2000 whereby it was reported that reliance on rain-fed farming and prevailing drought conditions and inconsistent rainfall in Iringa District contributed to food shortages. For instance, in 2000, the district had food deficit of 3,782 metric tonnes of maize needed to be provided to food insecure households.
4.5.7 Trends of maize yields in relation to annual rainfall records
The study findings showed that there was a negative relationship between rainfall and maize production records because as the rainfall increased, production decreased and vice versa. This result assumes presence of negative correlations between the two variables, although the relation is not significant as indicated by P=0.092 (P ;0.05) Table 4.6. This finding supports USAID (2010) through their report on status of food security in Tanzania. USAID (2010) reported that in 2008 in areas such as Isman, Idodi, Kilosa, and Mpwapwa whereby they experienced floods that washed out planted crops and destroyed buildings as well as bridges. In similar vein, Mar and colleagues (2018) reported occurrence of heavy precipitation in Lower Myanmar that affected flowering period of pulses and negatively affected yield production. It means that not all rainfall may support farmers to obtain higher yields. Some rainfall is erratic and imposes negative effects on farmers.
4.6 Results on Response Strategies against Climate Change Impacts
4.6.1 Farming Response Strategies against CC Impacts used by Farmers
Findings showed adaptation strategies used by household farmers in response to CC impacts in the study area (Table 4.7). Crop diversification, change in planting dates and use of early maturing as well as improved maize varieties were found to be mainly adopted strategies by smallholder farmers in both villages. But use of drought tolerant crop (78%) and crop rotation (60.1%) were also the next opted strategy for respondents from Mkulula, while crop rotation (71.5%), application of chemical fertilizers (42.9%) and manure (37.1%) were found to be the next adopted strategy by farmers from Mkungugu. Irrigation (4.3%) was observed to be the least adopted strategy to farmers from Mkungugu, while application of chemical fertilizers (8.9%) was adopted by minority farmers from Mkulula (Table 4.7).
Table 4.7: Households’ response strategies to climate change at village level
Response Strategies Villages Total (N=150)
Mkungugu (N=70) Mkulula (N=80)
Crop diversification 67.1% 70.1% 68.6%
Change in planting dates 82.9% 82.3% 82.6%
Improved maize variety 77.2% 84.8% 81.3%
Drought tolerant crop 35.7% 78.8% 58.6%
Irrigation 4.3% 00% 2%
Farming to non-farming activities 21.4% 37.1% 30%
Crop rotation 71.5% 60.1% 65.3%
Application of Chemical fertilizer 42.9% 8.9% 24.6
Application of manure 37.1% 18.8% 27.3%
Agriculture diversification (Livestock keeping) 17.1% 11.2% 14%
Total 457.2% 452.1% 454.6%
Source: Field Survey (2018)
Apart from the opted household strategies, choice for adaptation strategies by respondent households was motivated by different reasons as depicted in Figure 4.15.
Figure 4.15: Motivating factors toward CC response strategies by 150 farmers
Source: Field Survey (2018).
As indicated in Figure 4.15, food security, increased yields/outputs, dry spell, rainfall variability, drought, commercial purposes and income have been the most motivating factors for farmers to adopt different strategies. Though on the other side, there are some factors such as unpredictable rains and fear from crop pest and diseases made some farmers not to adopt some strategies. One farmer reported to adopt multiple or more strategies based on profitability of the adopted strategies they earned including capacity to handle them.