No abstract

The production of agricultural commodities faces increased risk of pests, diseases and other stresses due to climate change and variability. This study assesses the potential distribution of agricultural pests under projected climatic scenarios using evidence from the African coffee white stem borer (CWB), Monochamus leuconotus (Pascoe) (Coleoptera: Cerambycidae), an important pest of coffee in Zimbabwe. A species distribution modeling approach utilising Boosted Regression Trees (BRT) and Generalized Linear Models (GLM) was applied on current and projected climate data obtained from the WorldClim database and occurrence data (presence and absence) collected through on-farm biological surveys in Chipinge, Chimanimani, Mutare and Mutasa districts in Zimbabwe. Results from both the BRT and GLM indicate that precipitation-related variables are more important in determining species range for the CWB than temperature related variables. The CWB has extensive potential habitats in all coffee areas with Mutasa district having the largest model average area suitable for CWB under current and projected climatic conditions. Habitat ranges for CWB will increase under future climate scenarios for Chipinge, Chimanimani and Mutare districts while it will decrease in Mutasa district. The highest percentage change in area suitable for the CWB was for Chimanimani district with a model average of 49.1% (3 906 ha) increase in CWB range by 2080. The BRT and GLM predictions gave similar predicted ranges for Chipinge, Chimanimani and Mutasa districts compared to the high variation in current and projected habitat area for CWB in Mutare district. The study concludes that suitable area for CWB will increase significantly in Zimbabwe due to climate change and there is need to develop adaptation mechanisms.

Soil salinity and sodicity are major factors limiting agricultural productivity in irrigation schemes located in semi-arid areas. A study was conducted to assess the quality of irrigation water used in Mutema Irrigation Scheme located in southeast Zimbabwe to understand how irrigation water quality is related to the chemical quality of soils in the scheme. Irrigation water samples were collected from groundwater and surface sources in 2012 and their hydrochemistry determined while soil samples were collected from irrigated and non-irrigated parts of the scheme in 2006 and 2012 and analysed for selected chemical properties. The results indicated that the groundwater had high concentrations of Na + (4.35 mg/l), Mg 2+ (4.75 mg/l), Cl − (3.6 mg/l) and Electrical Conductivity (EC) (1729 Msm/cm) compared to the surface irrigation water source which had 0.72 mg/l Na + , 2.25 mg/l Mg 2+ , 0.78 mg/l Cl − and 594 Msm/cm EC. The soils in the scheme had higher levels of pH, Sodium Adsorption Ratio (SAR), Exchangeable Sodium Percentage (ESP) and EC which in some blocks exceeded the threshold requirements for cropping. It was found that pH, SAR and ESP were significantly higher (p < 0.05) in irrigated blocks compared to non-irrigated areas of the scheme, indicating an influence of irrigation water on soils characteristics in irrigated plots. Mg 2+ and Ca 2+ in the soils positively correlated with Na + (r 2 = 0.67 and r 2 = 0.57 respectively). The results indicated that the groundwater presents a chloride hazard to the soils in the scheme which are becoming saline and therefore require proper management and choice of crops to sustain crop production.

The interest in using remote sensing data in agriculture, including plant disease assessments, has increased considerably in the last years. The satellite-based Sentinel-2 MultiSpectral Imager (MSI) sensor has been launched recently for multispectral vegetation condition assessment for agricultural and ecosystem applications. The aim of this pilot study conducted in the greenhouse using a hand-held spectroradiometer was to assess the utility of the same wavebands as used in the Sentinel-2 MSI in assessing and modeling coffee leaf rust (CLR) based on the non-linear radial basis function-partial least squares regression (RBF-PLS) machine learning algorithm, compared with ordinary partial least squared regression (PLSR). The RBF-PLS derived models satisfactorily described CLR severity (R 2 =0.92 and RMSE=6.1% with all bands and R 2 =0.78 and RMSE=10.2% with selected bands) when compared with PLSR (R 2 = 0.27 and RMSE = 18.7% with all bands and R 2 = 0.17 and RMSE = 19.8% with selected bands). Specifically, four bands, B2 (490 nm), B4 (665 nm), B5 (705 nm) and B7 (783 nm) were identified as the most important spectral bands in assessing and modeling CLR severity. Better accuracy was obtained for most severe levels of CLR (R 2 =0.71 using all variables) than for moderate levels (R 2 =0.38 using all variables). Overall, the findings of this study showed that the use of RBF-PLS and the four Sentinel-2 MSI bands could enhance CLR severity estimation at the leaf level. Further work will be needed to extrapolate these findings to the crop level using the Sentinel-2 platform.

A study was conducted to evaluate four common coffee (Coffea arabica) varieties in Zimbabwe for drought tolerance and ability to recover. The plants were subjected to drought stress for 21 and 28 days with evaluation of recovery done 14 days after interruptive irrigation. Coffee varieties were not significantly different in initial fresh and dry biomass before stressing (P>0.05). CR95 had significantly accumulated more (P<0.05)dry root mass (0.8 g) than the rest of the varieties after 21 days of drought stress. SL28 and CR95 had an 8.3% increase in dry biomass while Cat128 did not gain any dry biomass after 21 days of drought stress. CR95 had significantly more (P<0.05) total dry biomass after 21 days and 28 days of drought stress while SL28 was consistently the least in both periods. Cat129 had the highest recovery gains in dry root, dry shoot, and total dry biomass after 21 days and 28 days of drought stress. Initial root biomass was negatively correlated with changes in total fresh and dry biomass of young coffee (r>0.60) after both 21 and 28 days of drought stress, indicating that root biomass may be the most important factor determining drought tolerance in coffee varieties.