Experimental Evaluation of Conservation Agriculture with Drip Irrigation for Water Productivity in Sub-Saharan Africa

A field-scale experimental study was conducted in Sub-Saharan Africa (Ethiopia and Ghana) to examine the effects of conservation agriculture (CA) with drip irrigation system on water productivity in vegetable home gardens. CA here refers to minimum soil disturbance (no-till), year-round organic mulch cover, and diverse cropping in the rotation. A total of 28 farmers (13 farmers in Ethiopia and 15 farmers in Ghana) participated in this experiment. The experimental setup was a paired ‘t’ design on a 100 m2 plot; where half of the plot was assigned to CA and the other half to conventional tillage (CT), both under drip irrigation system. Irrigation water use and crop yield were monitored for three seasons in Ethiopia and one season in Ghana for vegetable production including garlic, onion, cabbage, tomato, and sweet potato. Irrigation water use was substantially lower under CA, 18% to 45.6%, with a substantial increase in crop yields, 9% to about two-fold, when compared with CT practice for the various vegetables. Crop yields and irrigation water uses were combined into one metric, water productivity, for the statistical analysis on the effect of CA with drip irrigation system. One-tailed paired ‘t’ test statistical analysis was used to examine if the mean water productivity in CA is higher than that of CT. Water productivity was found to be significantly improved (α = 0.05) under the CA practice; 100%, 120%, 222%, 33%, and 49% for garlic, onion, tomato, cabbage, and sweet potato respectively. This could be due to the improvement of soil quality and structure due to CA practice, adding nutrients to the soil and sticking soil particles together (increase soil aggregates). Irrigation water productivity for tomato under CA (5.17 kg m−3 in CA as compared to 1.61 kg m−3 in CT) is found to be highest when compared to water productivity for the other vegetables. The mulch cover provided protection for the tomatoes from direct contact with the soil and minimized the chances of soil-borne diseases. Adapting to CA practices with drip irrigation in vegetable home gardens is, therefore, a feasible strategy to improve water use efficiency, and to intensify crop yield, which directly contributes towards the sustainability of livelihoods of smallholder farmers in the region

Scaling-up Conservation Agriculture Production System (CAPS) with Drip Irrigation by Integrating MCE Technique and the APEX Model

The conservation agriculture production system (CAPS) approach with drip irrigation has proven to have the potential to improve water management and food production in Ethiopia. A method of scaling-up crop yield under CAPS with drip irrigation is developed by integrating a biophysical model: APEX (agricultural policy environmental eXtender), and a Geographic Information System (GIS)-based multi-criteria evaluation (MCE) technique. Topography, land use, proximity to road networks, and population density were considered in identifying potentially irrigable land. Weather and soil texture data were used to delineate unique climate zones with similar soil properties for crop yield simulation using well-calibrated crop model parameters. Crops water demand for the cropping periods was used to determine groundwater potential for irrigation. The calibrated APEX crop model was then used to predict crop yield across the different climatic and soil zones. The MCE technique identified about 18.7 Mha of land (16.7% of the total landmass) as irrigable land in Ethiopia. Oromia has the highest irrigable land in the nation (35.4% of the irrigable land) when compared to other regional states. Groundwater could supply a significant amount of the irrigable land for dry season production under CAPS with drip irrigation for the various vegetables tested at the experimental sites with about 2.3 Mha, 3.5 Mha, 1.6 Mha, and 1.4 Mha of the irrigable land available to produce garlic, onion, cabbage, and tomato, respectively. When comparing regional states, Oromia had the highest groundwater potential (40.9% of total potential) followed by Amhara (20%) and Southern Nations, Nationalities, and Peoples (16%). CAPS with drip irrigation significantly increased groundwater potential for irrigation when compared to CTPS (conventional tillage production system) with traditional irrigation practice (i.e., 0.6 Mha under CTPS versus 2.2 Mha under CAPS on average). Similarly, CAPS with drip irrigation depicted significant improvement in crop productivity when compared to CTPS. APEX simulation of the average fresh vegetable yield on the irrigable land under CAPS with drip irrigation ranged from 1.8–2.8 t/ha, 1.4–2.2 t/ha, 5.5–15.7 t/ha, and 8.3–12.9 t/ha for garlic, onion, tomato, and cabbage, respectively. CAPS with drip irrigation technology could improve groundwater potential for irrigation up to five folds and intensify crop productivity by up to three to four folds across the nation

Evaluation of CFSR, TMPA 3B42 and ground-based rainfall data as input for hydrological models, in data-scarce regions: The upper Blue Nile Basin, Ethiopia

Accurate prediction of hydrological models requires accurate spatial and temporal distribution of rainfall. In developing countries, the network of observation stations for rainfall is sparse and unevenly distributed. Satellite-based products have the potential to overcome this shortcoming. The objective of this study is to compare the advantages and the limitation of commonly used high-resolution satellite rainfall products (Climate Forecast System Reanalysis (CFSR) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 version 7) as input to hydrological models as compared to sparsely and densely populated network of rain gauges. We used two (semi-distributed) hydrological models that performed well in the Ethiopian highlands: Hydrologiska Byråns Vattenbalansavdelning (HBV) and Parameter Efficient Distributed (PED). The rainfall products were tested in two watersheds: Gilgel Abay with a relatively dense network of rain gauge stations and Main Beles with a relatively scarce network, both are located in the Upper Blue Nile Basin. The results indicated that TMPA 3B42 was not be able to capture the gauged rainfall temporal variation in both watersheds and was not tested further. CFSR over predicted the rainfall pattern slightly. Both the gauged and the CFSR reanalysis data were able to reproduce the streamflow well for both models and both watershed when calibrated separately to the discharge data. Using the calibrated model parameters of gauged rainfall dataset together with the CFSR rainfall, the stream discharge for the Gilgel Abay was reproduced well but the discharge of the Main Beles was captured poorly partly because of the poor accuracy of the gauged rainfall dataset with none of the rainfall stations located inside the watershed. HBV model performed slightly better than the PED model, but the parameter values of the PED could be identified with the features of the landscape

Evaluating hydrologic responses to soil characteristics using SWAT model in a paired-watersheds in the Upper Blue Nile Basin

Watershed responses are affected by the watershed characteristics and rainfall events. The characteristics of soil layers are among the fundamental characteristics of a watershed and they are input to hydrologic modeling similar to topography and land use/cover. Although the roles of soils have been perceived, there are limited studies that quantify the role of soil characteristics on watershed runoff responses due to the lack of field datasets. Using two adjacent watersheds (Ribb and Gumara) which have a significant different runoff response with a similar characterstics except geological settings (including soil characteristics), we studied the effects of soil characteristics on runoff and water balance. The Soil and Water Assessment Tool (SWAT) was used to simulate the surface runoff response at the outlet of the watershed and the optimal model parameters distribution was tested with a non-parametric test for similarity. Results indicated that SWAT model captured the observed flow very well with a Nash-Sutcliffe Efficiency (NSE) of greater than 0.74 and with a PBIAS of less than 10% for both calibration and validation period. The comparison of the optimal model parameter distributions of the SWAT model showed that the watershed characteristics could be uniquely defined and represented by a hydrologic model due to the differences in the soils. Using field observations and modeling experiments, this study demonstrates how sensitive watershed hydrology is to soils, emphasizing the importance of accurate soil information in hydrological modeling. We conclude that due emphasis should be given to soil information in hydrologic analysis

Performance of bias corrected MPEG rainfall estimate for rainfall-runoff simulation in the upper Blue Nile Basin, Ethiopia

In many developing countries and remote areas of important ecosystems, good quality precipitation data are neither available nor readily accessible. Satellite observations and processing algorithms are being extensively used to produce satellite rainfall products (SREs). Nevertheless, these products are prone to systematic errors and need extensive validation before to be usable for streamflow simulations. In this study, we investigated and corrected the bias of Multi-Sensor Precipitation Estimate–Geostationary (MPEG) data. The corrected MPEG dataset was used as input to a semi-distributed hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) for simulation of discharge of the Gilgel Abay and Gumara watersheds in the Upper Blue Nile basin, Ethiopia. The result indicated that the MPEG satellite rainfall captured 81% and 78% of the gauged rainfall variability with a consistent bias of underestimating the gauged rainfall by 60%. A linear bias correction applied significantly reduced the bias while maintaining the coefficient of correlation. The simulated flow using bias corrected MPEG SRE resulted in a simulated flow comparable to the gauge rainfall for both watersheds. The study indicated the potential of MPEG SRE in water budget studies after applying a linear bias correction

Potential of Water Hyacinth Infestation on Lake Tana, Ethiopia: A Prediction Using a GIS-Based Multi-Criteria Technique

Water hyacinth is a well-known invasive weed in lakes across the world and harms the aquatic environment. Since 2011, the weed has invaded Lake Tana substantially posing a challenge to the ecosystem services of the lake. The major factors which affect the growth of the weed are phosphorus, nitrogen, temperature, pH, salinity, and lake depth. Understanding and investigating the hotspot areas is vital to predict the areas for proper planning of interventions. The main objective of this study is therefore to predict the hotspot areas of the water hyacinth over the surface of the lake using the geographical information system (GIS)-based multi-criteria evaluation (MCE) technique. The main parameters used in the multi-criteria analysis were total phosphorus (\u3e0.08 mg L−1), total nitrogen (\u3e1.1 mg L−1), temperature (\u3c26.2 °C), pH (\u3c8.6), salinity (\u3c0.011%), and depth (\u3c6 m). These parameters were collected from 143 sampling sites on the lake in August, December (2016), and March (2017). Fuzzy overlay spatial analysis was used to overlay the different parameters to obtain the final prediction map of water hyacinth infestation areas. The results indicated that 24,969 ha (8.1%), 21,568.7 ha (7.1%), and 24,036 ha (7.9%) of the lake are susceptible to invasion by the water hyacinth in August, December, and March, respectively. At the maximum historical lake level, 30,728.4 ha will be the potential susceptible area for water hyacinth growth and expansion at the end of the rainy season in August. According to the result of this study, the north and northeastern parts of the lake are highly susceptible for invasion. Hence, water hyacinth management and control plans shall mainly focus on the north and northeastern part of Lake Tana and upstream contributing watersheds