Model-based yield gap analysis and constraints of rainfed sorghum production in Southwest Ethiopia

Abstract

For ensuring food demand of the fast growing population in developing countries, quantification of crop yield gaps and exploring production constraints are very crucial. Sorghum is one of the most important climate change resilient crops in the rainfed farming systems of semi-arid tropics. However, there is little information about yield gaps and production constraints. This study aimed at analysing existing yield gaps and exploring major constraints of sorghum production in Southwest Ethiopia. A crop simulation model approach using AquaCrop and DSSAT was used to estimate potential yield and analyse the yield gaps. Model calibration and evaluation was performed using data from field experiments conducted in 2018 and 2019. Sorghum production constraints were assessed using a survey. The actual and water-limited yield of sorghum ranged from 0.58 to 2.51 and 3.6 to 6.47 t/ha, respectively for the period 2003–17. The regional yield gaps of sorghum for the targeted period were 3.02–3.95 t/ha with a mean value of 3.51 t/ha. Majority of respondent farmers considered seasonal rainfall risk (98%), poor soil fertility (86%), lack of improved varieties (78%) and inadequate weed management (56%) as major factors responsible for the existing yield gaps. The mean exploitable yield gap (2.5 t/ha) between water-limited and actual yield showed the level of existing opportunity for improvement in the actual productivity of sorghum. The gaps also call for introduction of proper interventions such as adoption of improved varieties, planting date adjustment, conservation tillage, fertilizer application and on time weed management.

Factors Affecting Tomato Productivity in Western Oromia, Ethiopia: Evidence from Smallholder Farmers

“…Galilea variety was high yielder than Cochoro variety which implies that a higher yielder improved seed can cause a remarkable improvement in tomato production for smallholder farmers. This is in line with [31] that reported that seeds that fulfill the quality requirements have a positive impact on the productivity of land.…”

“…This implies that policy interventions that make improved Gelilea variety seed available to more farmers could enhance productivity. The result is in line with [29,31,38] who stated that the choices accessible to farmers' new high yielder variety at a reasonable price, and at the right place and time contribute to increased crop productivity.…”

“…The number of biotic factors increased the tomato productivity, while it might also decrease productivity. In this direction, [15,31,34] demonstrate that biotic factors reduced crop productivity by up to 60% of productivity in developing countries. According to the result, agricultural credit positively affected tomato productivity, which has been acknowledged by statistical analysis at a 1% level.…”

Simulating the effect of climate change on barley yield in Ethiopia with the DSSAT‐CERES‐Barley model

“…The effect of temperature on species could be cultivar‐specific regarding timing, intensity, and duration of stress (Opole et al., 2018; Prasad & Maduraimuthu, 2014). Studies indicate that treatments and environmental interactions can have a substantial effect on yield differences among cultivars (Chala, 2021; Habte et al., 2020). In the current study, the yield reduction projected for cultivar EH‐1493 (−19.7%) was lower than the reduction projected for cultivar Traveller (−44.4%) averaged across all study areas, models, and RCPs.…”

The impact of climate change on the productivity of cowpea (<em>Vigna unguiculata</em>) under three different socio-economic pathways

“…This finding well agreed with Kanda et al (2021) where they reported that AquaCrop model over-estimated cowpea yield under water deficit conditions. High over-estimations of sorghum yield especially during water deficit conditions have been reported by Habte et al (2020) using AquaCrop and DSSAT models. The SIMPLACE insignificantly simulation of yield during water deficit could be attributed to the possible errors in the determination of canopy senescence (Espadafor et al, 2017).…”