**3.1 Maize yields**

*Environmental Issues and Sustainable Development*

temperature [°C], daily solar radiation [MJ/m2

by Okwach and Simiyu [46] and Okwach [47].

able success [49–55].

APSIM requires site-specific data on latitude and longitude, soil texture and depth (m), slope (%) and slope length (m); climate (daily maximum and minimum

growth and phenology (crop type and cultivar name, maturity type, date of 50% flowering and total number of leaves, total biomass at harvest (kg ha−1), grain yield (kg ha−1), final plant population (plts m−2), N and P contents of plant parts, biomass at anthesis (kg ha−1), population at thinning (plts m−2), date of physiological maturity (black layer) and maximum leaf area index (LAI); soil water, nitrogen and phosphorus; residues and manure (crop and manure type, dry weight [kg ha−1], N, C and P content [%], ash content, and ground cover [%]); and management (date of all operations e.g. sowing, harvest, thinning, weeding, tillage and fertilizer applications, sowing depth and plant population, type, rate and depth of fertilizer application, and type (hoe, disc, harrow, etc.) and depth of tillage) to run. These data can be obtained from field trials or secondary sources. However, this study used the APSIM that had been calibrated and validated for Katumani semi-arid area

Daily minimum and maximum temperature, solar radiation and rainfall data for Katumani for the near (2050) and far (2100) future scenarios were downscaled from the National Meteorological Research Centre (CNRM) and Commonwealth Scientific and Industrial Research Organization (CSIRO) climate models using the Statistical Downscaling Model (SDSM) version 4.2 [48] and uploaded in APSIM. Both models, CNRM and CSIRO, have predicted a 1–2.5°C and 10% increase in temperature and rainfall, respectively, by the end of the century (2100) which is consistent with the Intergovernmental Panel on Climate Change (IPCC)'s prediction of 3.2°C and 11% rise in temperature and rainfall, respectively, for Kenya and the rest of East Africa by 2100. SDSM is a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique. It is a hybrid of a stochastic weather generator and regression-based downscaling methods and facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future climate [49]. The tool has been used extensively with remark-

The following eight cropping systems were simulated using the downscaled climate data: (1) Sole short duration maize crop, (2) Sole short duration pigeonpea crop, (3) Sole medium duration pigeonpea crop, (4) Sole long duration pigeonpea crop, (5) Short duration pigeonpea-maize intercrop, (6) medium duration pigeonpea-maize intercrop and (8) long duration pigeonpea-maize intercrop. The model was run to simulate 50 and 100 years under these cropping systems. The growing season was defined to start after 5 consecutive days with volumetric soil water content in the top 100 cm above 70%. The end of the season was deemed to occur when soil water content fell below 50% for 8 consecutive days. KDVI maize variety was used to represent all early maturing (120–150 days to mature) and high yielding maize varieties recommended for semi-arid conditions. Similarly, Mbaazi I, Kat 60/8 and Mbaazi II pigeonpea varieties were used to represent short (100 days to mature), medium (150 days to mature) and long (180–220 days to mature) duration pigeonpea varieties, respectively. Pigeonpea was planted at spacings of 90 cm × 60 cm, 75 cm × 30 cm and 50 cm × 25 cm for the long, medium and short duration varieties, respectively, whilst maize was planted with Triple Super Phosphate (TSP) fertilizer at the recommended rate of 40 kg P2O5 ha−1 at spacing of 90 cm × 30 cm. Other agronomic practices were adopted as currently practiced by farmers such as early planting, timely weeding

] and daily rainfall [mm]); crop

**238**

and thinning.

Long-term yields of maize under variable and changing climate in Katumani are presented in **Figure 1**. Prospects for increased maize production under sole maize crop in Katumani (Machakos County) are high, both in the near (by 2050) and far (2100) future scenarios under the two climate models, CNRM and CSIRO models. Relative to baseline yield of 500 kg ha−1, maize yields are expected to increase by 141 and 10% in 2050 and 2100, respectively, under the CSIRO model. The CNRM model was more optimistic and predicted maize yield increases of 150 and 23% in 2050 and 2100, respectively, under maize sole crop. The increase in yield could be attributed to the projected increase in rainfall of 20–40 mm per year by 2100. The predictions corroborate reports by Waithaka et al. [56] that Kenya's bread basket could shift from the Rift Valley to semi-arid eastern and north-eastern Kenya by 2050. Intercropping maize with pigeonpea will give mixed results. According to the CSIRO model, maize yield will increase by 18 and 15% under maize/Mbaazi I and maize/Mbaazi II intercrops, respectively, in 2050. However, yields under maize/ Kat 60/8 intercrop will decline by 4% in the same period. A similar trend will be observed in 2100 where intercropping maize with pigeonpea will reduce maize yields by 10–20% under the CSIRO model. The projected decline in maize yield could be attributed to high evapotranspiration due to anticipated rise in temperature. According to Thornton et al. [18], high evapotranspiration is bound to cause water scarcity which will adversely affect maize growth. These results agree with Herrero *et al*. [20] who predicted maize yield losses of upto 50% in the ASALs due to climate change, albeit under the Hadley model. Thornton et al. [18], Jones and Thornton [25], and Downing [57] have also predicted a significant decline in yields of maize and other food crops in the East African region due to the same phenomenon. However, the decline in maize yield could be arrested by encouraging farmers to adopt irrigation, conservation agriculture, seed priming, and in-situ water harvesting among other adaptation measures [58].

Conversely, according to the CNRM model, intercropping will increase maize yields by 28 and 11% under maize/short duration pigeonpea and maize/medium

**Figure 1.** *Long-term effect of pigeonpea on maize yield in Katumani under variable and changing climate.* duration pigeonpea intercrops, respectively, by 2050. Maize yields under maize/long duration pigeonpea intercrop will declined by 16%. However, maize yields will increase by 18, 13, and 4% under maize/short duration pigeonpea, maize/medium duration pigeonpea and maize/long duration pigeonpea intercrops, respectively, in the far future (2100). Because of these conflicting results, it is difficult to generalize the impacts of climate change on maize yields from maize/pigeonpea intercrops in Katumani and similar areas in the country. Further simulations involving many GCM model X scenario combinations are therefore required to establish the correct direction of change in maize yields under these systems, whether they will increase or decrease. Meanwhile, the results corroborate observation by Herrero et al. [20] that climate change impacts on maize yields depend on the emission scenario, crop model and the Global Climate Change Model (GCM) used.
