Options for Sustainable Intensification of Maize Production in Ethiopia

Srivastava, Amit Kumar; Mboh, Cho Miltin; Faye, Babacar; Gaiser, Thomas; Kuhn, Arnim; Engida, Ermias; Ewert, Frank

doi:10.3390/su11061707

Cited by 13 publications

(8 citation statements)

References 35 publications

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“…These results are in agreement those of Srivastava et al. (2019) in maize. For caudatum improved varieties GRINKAN and SOUMBA, grain yield was explained specifically by grains number per panicle and 1,000 grain weight.…”

Section: Discussionsupporting

confidence: 94%

“…Nitrogen improves chlorophyll content (SPAD values) allowing plants to make their photosynthesis (Tajul et al, 2013); and panicle number promoted mainly by tillering, while planting density improves panicle number thus causing plant numbers increase per area unit (Manjeet et al, 2017;Zhou et al, 2019). These results are in agreement those of Srivastava et al (2019) in maize. For caudatum improved varieties GRINKAN and SOUMBA, grain yield was explained specifically by grains number per panicle and 1,000 grain weight.…”

Section: Discussionsupporting

confidence: 79%

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Plant density and nitrogen fertilization optimization on sorghum grain yield in Mali

et al. 2021

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Sorghum [Sorghum bicolor (L.) Moench], a staple food crop in Mali, has low yields for several reasons including that many farmers do not have the financial resources to purchase state-of-the-art genetics and fertilizers and information is not available on how to optimize yields for heirloom variety. To improve their economic and environmental sustainability, Mali farmers need to understand how to invest their limited resources. In many situations this involves the use of open-pollinated varieties that have a range of tillering capabilities. This study determined the best population density and nitrogen (N) fertilization combinations for eight locally available sorghum varieties. The research was conducted in 2018 and 2019 and the experimental design was split-split-plot randomized block, the treatments were two plant densities (26,600 and 53,300 plants ha -1 ), three N levels (0 kg ha -1 , 89 kg ha -1 , and 178 kg ha -1 ), and eight varieties that had a range of tillering characteristics. Each treatment was replicated three times and six of the varieties were open pollinated. Results showed that each variety had a different yield response to plant density and N rate. For example, the tall guinea (hybrids FADDA and PABLO) and the short durra-caudatum A12-79 cultivar produced higher yields when planted at 53,300 plants ha -1 and Abbreviations: D1, 26,600 plants ha -1 ; D2, 53,300 plants ha -1 ; MRR, marginal rate of return; N0, nitrogen rate of 0 kg ha -1 ; N1, nitrogen rate of 89 kg ha -1 ; N2, nitrogen rate of 178 kg ha -1 .This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionsupporting

confidence: 79%

Plant density and nitrogen fertilization optimization on sorghum grain yield in Mali

et al. 2021

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“…Crop Yp and Yw were simulated from 1995 to 2019 using LINTUL5 (Addiscott and Whitmore, 1991) embedded into the modelling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management) (Gaiser et al, 2013). The applied LINTUL5 version has been used extensively for cropping systems analysis in SSA (Srivastava et al, 2020(Srivastava et al, , 2019(Srivastava et al, , 2017Faye et al, 2018;Trawally et al, 2015).…”

Section: Crop Simulationmentioning

confidence: 99%

Simulating Regional Cassava Yield Gap in Nigeria

Srivastava

Gaiser

Akinwumiju

et al. 2022

Preprint

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Cassava production is essential for food security in Sub-Saharan Africa and serves as a major calorie- intake source in Nigeria. Here we use a crop model, LINTUL5, embedded into a modeling framework SIMPLACE to estimate potential cassava yield gaps (Yg) in 30 states of Nigeria. Our study of climate parameter influence on the variability of current and potential yields and Yg shows that cumulative radiation and precipitation were the most significant factors associated with cassava yield variability (p = 0.01). The cumulative Yg mean was estimated as 18202 kg∙ha-1, with a maximum of 31207 kg ha-1 in Kano state. Across the states, nutrient limitation accounts for 55.3% of the total cassava yield gap, while the remaining 44.7% is attributed to water limitation. The highest untapped water-limited yields were estimated in States, such as Bauchi, Gombe, and Sokoto, characterized by the short rainy season. Conclusively, the current cassava yield levels can be increased by a factor of five through soil fertility enhancement and with irrigation, particularly in semi-arid regions.

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“…Crop yields were simulated using the LINTUL5 [34] crop model within the SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management) modelling framework [35]. The applied LINTUL5 version has extensively been used for cropping systems analysis in Sub-Saharan Africa [36][37][38][39][40]. Simulation of biomass production is based on intercepted radiation, according to Lambert-Beer's law and light use efficiency.…”

Section: Model Calibration and Evaluationmentioning

confidence: 99%

The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations

et al. 2020

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High-resolution and consistent grid-based climate data are important for model-based agricultural planning and farm risk assessment. However, the application of models at the regional scale is constrained by the lack of required high-quality weather data, which may be retrieved from different sources. This can potentially introduce large uncertainties into the crop simulation results. Therefore, in this study, we examined the impacts of grid-based time series of weather variables assembled from the same data source (Approach 1, consistent dataset) and from different sources (Approach 2, combined dataset) on regional scale crop yield simulations in Ghana, Ethiopia and Nigeria. There was less variability in the simulated yield under Approach 1, ranging to 58.2%, 45.6% and 8.2% in Ethiopia, Nigeria and Ghana, respectively, compared to those simulated using datasets retrieved under Approach 2. The two sources of climate data evaluated here were capable of producing both good and poor estimates of average maize yields ranging from lowest RMSE = 0.31 Mg/ha in Nigeria to highest RMSE = 0.78 Mg/ha under Approach 1 in Ghana, whereas, under Approach 2, the RMSE ranged from the lowest value of 0.51 Mg/ha in Nigeria to the highest of 0.72 Mg/ha in Ethiopia under Approach 2. The obtained results suggest that Approach 1 introduces less uncertainty to the yield estimates in large-scale regional simulations, and physical consistency between meteorological input variables is a relevant factor to consider for crop yield simulations under rain-fed conditions.Atmosphere 2020, 11, 180 2 of 15 Availability of weather data to run crop simulation models at regional scale applications covering several thousands of square kilometers. In this case, models rely on secondary weather products (spatialized weather variables) where weather variables are measured only at a few sites [8].The measured weather variables can be interpolated (generally each variable separately), which is subsequently used to run the crop model under observed climate conditions. Gridded observational weather data is often used also to adjust bias in regional climate model simulations, which serve as a key tool for climate change impact assessment. However, there are several uncertainties with observed weather data. Many observation stations, particularly in Sub-Saharan Africa, are not equipped to measure certain weather variables (wind speed, relative humidity, open pan evaporation, etc.). Thus, these variables are estimated from measured ones based on equations that have been validated in other regions of the world or at the global scale. The climate data measurement networks, in general, are not compatible, due to differences in instrumentation, sensor height and data logging [9]. In the absence of site-specific data, the following data sources can be identified for spatialized crop yield simulation:

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Options for Sustainable Intensification of Maize Production in Ethiopia

Cited by 13 publications

References 35 publications

Plant density and nitrogen fertilization optimization on sorghum grain yield in Mali

Plant density and nitrogen fertilization optimization on sorghum grain yield in Mali

Simulating Regional Cassava Yield Gap in Nigeria

The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations

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