scholarly journals Soil and management‐related factors contributing to maize yield gaps in western Kenya

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Sussy Munialo ◽  
A. Sigrun Dahlin ◽  
Cecilia Onyango M. ◽  
W. Oluoch‐Kosura ◽  
Håkan Marstorp ◽  
...  
Keyword(s):  
Maize Yield ◽  
Related Factors ◽  
Western Kenya ◽  
Yield Gaps

scholarly journals Closing maize yield gaps in sub-Saharan Africa will boost soil N2O emissions

2020 ◽  
Vol 47 ◽  
pp. 95-105 ◽  
Author(s):  
Sonja Leitner ◽  
David E Pelster ◽  
Christian Werner ◽  
Lutz Merbold ◽  
Elizabeth M Baggs ◽  
...  
Keyword(s):  
Maize Yield ◽  
Sub Saharan Africa ◽  
N2o Emissions ◽  
Yield Gaps ◽  
Sub Saharan

scholarly journals Identifying maize yield and precipitation gaps in Uganda

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Terence Epule Epule ◽  
Driss Dhiba ◽  
Daniel Etongo ◽  
Changhui Peng ◽  
Laurent Lepage
Keyword(s):  
Cropping Systems ◽  
Growing Season ◽  
Maize Yield ◽  
Sub Saharan Africa ◽  
World Bank Group ◽  
Precipitation Data ◽  
Yield Data ◽  
Yield Gaps ◽  
Maize Yields ◽  
Growing Season Precipitation

AbstractIn sub-Saharan Africa (SSA), precipitation is an important driver of agricultural production. In Uganda, maize production is essentially rain-fed. However, due to changes in climate, projected maize yield targets have not often been met as actual observed maize yields are often below simulated/projected yields. This outcome has often been attributed to parallel gaps in precipitation. This study aims at identifying maize yield and precipitation gaps in Uganda for the period 1998–2017. Time series historical actual observed maize yield data (hg/ha/year) for the period 1998–2017 were collected from FAOSTAT. Actual observed maize growing season precipitation data were also collected from the climate portal of World Bank Group for the period 1998–2017. The simulated or projected maize yield data and the simulated or projected growing season precipitation data were simulated using a simple linear regression approach. The actual maize yield and actual growing season precipitation data were now compared with the simulated maize yield data and simulated growing season precipitation to establish the yield gaps. The results show that three key periods of maize yield gaps were observed (period one: 1998, period two: 2004–2007 and period three: 2015–2017) with parallel precipitation gaps. However, in the entire series (1998–2017), the years 2008–2009 had no yield gaps yet, precipitation gaps were observed. This implies that precipitation is not the only driver of maize yields in Uganda. In fact, this is supported by a low correlation between precipitation gaps and maize yield gaps of about 6.3%. For a better understanding of cropping systems in SSA, other potential drivers of maize yield gaps in Uganda such as soils, farm inputs, crop pests and diseases, high yielding varieties, literacy, and poverty levels should be considered.

scholarly journals Causes Of Yield Gaps And Strategies For Minimizing The Gaps In Different Crops Of Bangladesh

1970 ◽  
Vol 36 (3) ◽  
pp. 469-476 ◽  
Author(s):  
Mohammad H Mondal
Keyword(s):  
New Technologies ◽  
High Yield ◽  
Credit System ◽  
Yield Gap ◽  
Related Factors ◽  
Integrated Crop Management ◽  
Yield Gaps ◽  
The Difference ◽  
The Government ◽  
Extension Agencies

The concept of yield gaps originated from the studies conducted by IRRI in the seventies. The yield gap discussed in this paper is the difference between the potential farm yield and the actual average farm yield. In Bangladesh, yield gaps exist in different crops ranging up to 60%. According to the recent study conducted by BRRI, the yield gap in rice was estimated at 1.74 t/ha. The existence of yield gaps was as well observed in rice, mustard, wheat and cotton in India. In India, yield gap varied from 15.5 to 60% with the national average gap of 52.3% in irrigated ecosystem. The yield gaps are mainly caused by biological, socio-economic, climate and institutional/policy related factors. Different strategies, such as integrated crop management (1CM) practices, timely supply of inputs including credit to farmers, research and extension collaboration to transfer the new technologies have been discussed as strategies to minimize yield gaps. Suggestions have been made to make credit available to resource-poor small farmers to buy necessary inputs. Reducing transaction cost, simplifying lending procedures and strengthening monitoring mechanism of the current credit system are, however, essential to enable the farmers to avail the credit facility. Efforts should be made to update farmers’ knowledge on the causes of yield gaps in crops and measures to narrow the gaps through training, demonstrations, field visits and monitoring by extension agencies to achieve high yield. The government should realize that yield gaps exist in different crops of Bangladesh and therefore, explore the scope to increase production as well as productivity of crops by narrowing the yield gap and thereby ensure food security. Keywords: Yield gaps; strategies; crops of Bangladesh. DOI: http://dx.doi.org/10.3329/bjar.v36i3.9274 BJAR 2011; 36(3): 469-476

scholarly journals Identifying Ways to Narrow Maize Yield Gaps Based on Plant Density Experiments

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 281
Author(s):  
Jian Li ◽  
Man Wu ◽  
Keru Wang ◽  
Bo Ming ◽  
Xiao Chang ◽  
...  
Keyword(s):  
Management Practices ◽  
Field Experiments ◽  
Plant Density ◽  
Population Level ◽  
Maize Yield ◽  
Limiting Factors ◽  
Stable Range ◽  
Maize Production ◽  
Yield Gaps ◽  
Loss Range

Exploring the maximum grain yields (GYs) and GY gaps in maize (Zea mays L.) can be beneficial for farmer to identify the GY-limiting factors and take adaptive management practices for a higher GY. The objective of this work was to identify the optimum maize plant density range and the ways to narrow maize GY gaps based on the variation of the GYs, dry matter (DM) accumulation and remobilization with changes in plant density. Field experiments were performed at the 71 Group and Qitai Farm in Xinjiang, China. Two modern cultivars, ZhengDan958 and ZhongDan909, were planted at 12 densities, ranging from 1.5 to 18 plants m−2. With increased plant density, single plant DM decreased exponentially, whereas population-level DM at the pre- (DMBS) and post- (DMAS) silking stages increased, and the amount of DM remobilization (ARDM) increased exponentially. Further analysis showed that plants were divided four density ranges: range I (<6.97 plants m−2), in which no DM remobilization occurred, DMBS and DMAS correlated significantly with GY; range II (6.97–9.54 plants m−2), in which the correlations of DMBS, DMAS, and ARDM with GY were significant; range III (9.54–10.67 plants m−2), in which GY and DMAS were not affected by density, DMBS increased significantly, and only the correlation of DMAS with GY was significant; and range IV (>10.67 plants m−2), in which the correlations of DMBS and ARDM with GY decreased significantly, while that of DMAS increased significantly. Therefore, ranges I and II were considered to be DM-dependent ranges, and a higher GY could be obtained by increasing the population-level DMAS, DMAS, and ARDM. Range III was considered the GY-stable range, increasing population-level DMBS, as well as preventing the loss of harvest index were the best way to enhance maize production. Range IV was interpreted as the GY-loss range, and a higher GY could be obtained by preventing the loss of HI and population-level DMAS.

scholarly journals Unravelling the variability and causes of smallholder maize yield gaps in Ethiopia

Food Security ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 83-103 ◽  
Author(s):  
Banchayehu Tessema Assefa ◽  
Jordan Chamberlin ◽  
Pytrik Reidsma ◽  
João Vasco Silva ◽  
Martin K. van Ittersum
Keyword(s):  
Stochastic Frontier Analysis ◽  
National Level ◽  
Total Yield ◽  
Maize Yield ◽  
Sub Saharan Africa ◽  
Yield Gap ◽  
Potential Yield ◽  
Frontier Analysis ◽  
Yield Gaps ◽  
Maize Yields

AbstractEthiopia has achieved the second highest maize yield in sub-Saharan Africa. Yet, farmers’ maize yields are still much lower than on-farm and on-station trial yields, and only ca. 20% of the estimated water-limited potential yield. This article provides a comprehensive national level analysis of the drivers of maize yields in Ethiopia, by decomposing yield gaps into efficiency, resource and technology components, and accounting for a broad set of detailed input and crop management choices. Stochastic frontier analysis was combined with concepts of production ecology to estimate and explain technically efficient yields, the efficiency yield gap and the resource yield gap. The technology yield gap was estimated based on water-limited potential yields from the Global Yield Gap Atlas. The relative magnitudes of the efficiency, resource and technology yield gaps differed across farming systems; they ranged from 15% (1.6 t/ha) to 21% (1.9 t/ha), 12% (1.3 t/ha) to 25% (2.3 t/ha) and 54% (4.8 t/ha) to 73% (7.8 t/ha), respectively. Factors that reduce the efficiency yield gap include: income from non-farm sources, value of productive assets, education and plot distance from home. The resource yield gap can be explained by sub-optimal input use, from a yield perspective. The technology yield gap comprised the largest share of the total yield gap, partly due to limited use of fertilizer and improved seeds. We conclude that targeted but integrated policy design and implementation is required to narrow the overall maize yield gap and improve food security.

Effects of Integrated Use of Calliandra calothyrsus and Maize Stover with Urea on Soil Mineral Nitrogen, Striga Infestation and Maize Yields in Western Kenya

2020 ◽  
pp. 1-11
Author(s):  
Robert O. Nyambati ◽  
Duncan G. Odhiamboz ◽  
Cornelius K. Serrem ◽  
Caleb O. Othieno ◽  
Frank S. Mairura
Keyword(s):  
Block Design ◽  
Maize Yield ◽  
Control Treatment ◽  
Plant Residues ◽  
Calliandra Calothyrsus ◽  
Nutrient Inputs ◽  
Western Kenya ◽  
Maize Stover ◽  
Grain Yields ◽  
Maize Grain

This study investigated the effects of applying different combinations of two contrasting plant residues, Calliandra calothyrsus (Calliandra) and maize stover, with urea on Striga infestation and maize yield in western Kenya. A randomized complete block design (RCBD) with 12 treatments replicated four times was used. The following plant residue: urea combinations was used so as to supply a total of 75 kg ha-1 in each treatment combination; 75:0, 60:15, 45:30, 30:45, 15:60, and 0:75 for five seasons (2007-2009). A control treatment where no nutrient inputs were applied was included. Calliandra applied at 45 kg N ha-1 plus urea (30 kg N ha-1) and maize stover applied 15 kg N ha-1 plus urea (60 kg N ha-1) had consistently lower Striga infestation compared other treatments. Negative linear relationship between maize yield and Striga population were observed in the first three seasons i.e. 2007 LR, 2007 SR and 2008 LR. Overall mean maize grain yields over the five seasons were highest (3.0 t ha-1) under maize stover (30 kg N ha-1) combined with urea (45 kg N ha-1) followed by Calliandra (45 kg N ha-1) combined with urea (30 kg N ha-1) with (2.7 t ha-1). Maize stover (30 kg N ha-1) in combination with urea (45 kg N ha-1) increased maize grain yields relative to the control by 275%, 107% and 155% in the first, second and third seasons respectively. Treatments with Calliandra (45 kg N ha-1) in combination with urea (30 kg N ha-1) increased maize grain yields relative to the control by 191%, and 233% in the first and third seasons respectively. The control and sole maize stover (75 kg N ha-1) had the lowest yields across all the seasons. The optimum application rate for stover was 30 kg N ha-1 nitrogen equivalent while that for Calliandra was 45 kg N ha-1.

scholarly journals Narrowing the Agronomic Yield Gaps of Maize by Improved Soil, Cultivar, and Agricultural Management Practices in Different Climate Zones of Northeast China

2016 ◽  
Vol 20 (12) ◽  
pp. 1-18 ◽  
Author(s):  
Zhijuan Liu ◽  
Xiaoguang Yang ◽  
Xiaomao Lin ◽  
Kenneth G. Hubbard ◽  
Shuo Lv ◽  
...  
Keyword(s):  
Physical Properties ◽  
Agricultural Production ◽  
Northeast China ◽  
Management Practices ◽  
Growing Season ◽  
Maize Yield ◽  
Soil Physical Properties ◽  
Potential Yield ◽  
Climate Zones ◽  
Yield Gaps

Abstract Northeast China (NEC) is one of the major agricultural production areas in China, producing about 30% of China’s total maize output. In the past five decades, maize yields in NEC increased rapidly. However, farmer yields still have potential to be increased. Therefore, it is important to quantify the impacts of agronomic factors, including soil physical properties, cultivar selections, and management practices on yield gaps of maize under the changing climate in NEC in order to provide reliable recommendations to narrow down the yield gaps. In this study, the Agricultural Production Systems Simulator (APSIM)-Maize model was used to separate the contributions of soil physical properties, cultivar selections, and management practices to maize yield gaps. The results indicate that approximately 5%, 12%, and 18% of potential yield loss of maize is attributable to soil physical properties, cultivar selection, and management practices. Simulation analyses showed that potential ascensions of yield of maize by improving soil physical properties PAYs, changing to cultivar with longer maturity PAYc, and improving management practices PAYm for the entire region were 0.6, 1.5, and 2.2 ton ha−1 or 9%, 23%, and 34% increases, respectively, in NEC. In addition, PAYc and PAYm varied considerably from location to location (0.4 to 2.2 and 0.9 to 4.5 ton ha−1 respectively), which may be associated with the spatial variation of growing season temperature and precipitation among climate zones in NEC. Therefore, changing to cultivars with longer growing season requirement and improving management practices are the top strategies for improving yield of maize in NEC, especially for the north and west areas.

scholarly journals Evaluation of crop arrangement and phosphorus rate on performance of maize-common bean intercropping in western Kenya

2020 ◽  
Vol 5 (3) ◽  
pp. 292-298
Author(s):  
Peter A. Opala ◽  
Dorcus O. Ofuyo ◽  
George D. Odhiambo
Keyword(s):  
Grain Yield ◽  
Significant Interaction ◽  
Maize Yield ◽  
Western Kenya ◽  
Land Equivalent Ratio ◽  
P Fertilizer ◽  
P Rate ◽  
Weed Infestation ◽  
P Application ◽  
Plot Design

The effect of phosphorus (P) rate and crop arrangement on the performance of component crops in maize-bean intercropping systems was investigated at two sites; Malanga and Bugeng’i in western Kenya. A split plot design with five crop arrangements in the main plots i.e., one row of maize alternating with one row of beans (conventional), maize and beans planted in the same hole, two rows of maize alternating with two of beans (Mbili), sole maize and sole beans, in a factorial combination with three P rates; 0, 30, and 60 kg ha-1 in the subplots, was used. Bean yields were low (< 1 t ha-1) but they increased with increasing P rate at both sites. Response of maize to P fertilizer was however poor at Malanga mainly due to Striga weed infestation. Yields of beans did not significantly differ among crop arrangements at both sites. At Bungeng’i, there was a significant interaction between P rate and crop arrangement. At this site, the maize yield in the conventional arrangement increased with increasing P rate but for the Mbili arrangement, the grain yield from application of 30 kg P ha-1 was significantly higher than that at 0 kg P ha-1 and similar to that 60 kg P ha-1. Therefore, it is not beneficial to fertilize beyond 30 kg P ha-1 at this site with the Mbili arrangement. Intercropping was beneficial in all crop arrangements (Land equivalent ratio >1) and can therefore be practiced, except for maize and beans planted in the same hole with no P application at Bugeng’i.

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