Modeling potential rain-fed maize productivity and yield gaps in the Wami River sub-basin, Tanzania

2014 ◽  
Vol 65 (2) ◽  
pp. 132-140 ◽  
Author(s):  
Sixbert Kajumula Mourice ◽  
Siza Donald Tumbo ◽  
Amuri Nyambilila ◽  
Cornell Lawrence Rweyemamu
Keyword(s):  
Maize Productivity ◽  
Yield Gaps

Change of Rice Yield Gaps and Influential Climatic Factors in Southern China

2013 ◽  
Vol 38 (5) ◽  
pp. 896-903 ◽  
Author(s):  
Quan-Hong SHI ◽  
Jian-Gang LIU ◽  
Zhao-Hua WANG ◽  
Ting-Ting TAO ◽  
Fu CHEN ◽  
...  
Keyword(s):  
Rice Yield ◽  
Climatic Factors ◽  
Southern China ◽  
Yield Gaps

Agriculture, Diversification, and Gender in Rural Africa: What Lessons Can We Learn?

2018 ◽  
Author(s):  
Agnes Andersson Djurfeldt ◽  
Fred Mawunyo Dzanku ◽  
Aida Cuthbert Isinika
Keyword(s):  
Gender Gap ◽  
Cash Income ◽  
Crop Diversification ◽  
Grain Production ◽  
The Past ◽  
Development Agenda ◽  
Yield Gaps ◽  
Rural Africa ◽  
And Gender ◽  
High Share

Smallholder-friendly messages, albeit not always translated into action, returned strongly to the development agenda over a decade ago. Smallholders’ livelihoods encompass social and economic realities outside agriculture, however, providing opportunities as well as challenges for the smallholder model. While smallholders continue to straddle the farm and non-farm sectors, the notion of leaving agriculture altogether appears hyperbolic, given the persistently high share of income generated from agriculture noted in the Afrint dataset. Trends over the past fifteen years can be broadly described as increasing dynamism accompanied by rising polarization. Positive trends include increased farm sizes, rising grain production, crop diversification, and increased commercialization, while negative trends include stagnation of yields, persistent yield gaps, gendered landholding inequalities, gendered agricultural asset inequalities, growing gendered commercialization inequalities, and an emerging gender gap in cash income. Regional nuances in trends reinforce the need for spatial contextualization of linkages between the farm and non-farm sectors.

Sowing Date and Maize Productivity: II. Kernel Number Determination

Crop Science ◽  
1994 ◽  
Vol 34 (4) ◽  
pp. 1044-1046 ◽  
Author(s):  
A. G. Cirilo ◽  
F. H. Andrade
Keyword(s):  
Sowing Date ◽  
Kernel Number ◽  
Maize Productivity

scholarly journals Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

2021 ◽  
Author(s):  
Mark Cooper ◽  
Kai P. Voss-Fels ◽  
Carlos D. Messina ◽  
Tom Tang ◽  
Graeme L. Hammer
Keyword(s):  
Climate Change ◽  
Crop Improvement ◽  
Target Population ◽  
Crop Productivity ◽  
Climate Change Scenarios ◽  
Global Food Security ◽  
Genotype By Environment ◽  
Improvement Strategies ◽  
Yield Gaps ◽  
On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

Yield gaps in composite carp culture in eastern India

2021 ◽  
Author(s):  
Himansu Kumar De ◽  
Simantini Shasani ◽  
Manoj Kumar Das
Keyword(s):  
Eastern India ◽  
Yield Gaps

scholarly journals Increase maize productivity and water use efficiency through application of potassium silicate under water stress

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. A. Gomaa ◽  
Essam E. Kandil ◽  
Atef A. M. Zen El-Dein ◽  
Mamdouh E. M. Abou-Donia ◽  
Hayssam M. Ali ◽  
...  
Keyword(s):  
Grain Yield ◽  
Water Use Efficiency ◽  
Water Deficit ◽  
Water Use ◽  
Field Experiments ◽  
Foliar Application ◽  
Potassium Silicate ◽  
Water Deficit Stress ◽  
Maize Productivity ◽  
Use Efficiency

AbstractIn Egypt, water shortage has become a key limiting factor for agriculture. Water-deficit stress causes different morphological, physiological, and biochemical impacts on plants. Two field experiments were carried out at Etay El-Baroud Station, El-Beheira Governorate, Agriculture Research Center (ARC), Egypt, to evaluate the effect of potassium silicate (K-silicate) of maize productivity and water use efficiency (WUE). A split-plot system in the four replications was used under three irrigation intervals during the 2017 and 2018 seasons. Whereas 10, 15, and 20 days irrigation intervals were allocated in main plots, while the three foliar application treatments of K-silicate (one spray at 40 days after sowing; two sprays at 40 and 60 days; and three sprays at 40, 60, and 80 days, and a control (water spray) were distributed in the subplots. All the treatments were distributed in 4 replicates. The results indicated that irrigation every 15 days gave the highest yield in both components and quality. The highly significant of (WUE) under irrigation every 20 days. Foliar spraying of K-silicate three times resulted in the highest yield. Even under water-deficit stress, irrigation every fifteen days combined with foliar application of K-silicate three times achieved the highest values of grain yield and its components. These results show that K-silicate treatment can increase WUE and produce high grain yield requiring less irrigation.

scholarly journals Wheat yield gaps across smallholder farming systems in Ethiopia

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
João Vasco Silva ◽  
Pytrik Reidsma ◽  
Frédéric Baudron ◽  
Moti Jaleta ◽  
Kindie Tesfaye ◽  
...  
Keyword(s):  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Wheat Yield ◽  
Farming Systems ◽  
Smallholder Farming ◽  
Yield Gap ◽  
Frontier Analysis ◽  
Ecological Zones ◽  
Relative Contribution ◽  
Yield Gaps

AbstractWheat yields in Ethiopia need to increase considerably to reduce import dependency and keep up with the expected increase in population and dietary changes. Despite the yield progress observed in recent years, wheat yield gaps remain large. Here, we decompose wheat yield gaps in Ethiopia into efficiency, resource, and technology yield gaps and relate those yield gaps to broader farm(ing) systems aspects. To do so, stochastic frontier analysis was applied to a nationally representative panel dataset covering the Meher seasons of 2009 and 2013 and crop modelling was used to simulate the water-limited yield (Yw) in the same years. Farming systems analysis was conducted to describe crop area shares and the availability of land, labour, and capital in contrasting administrative zones. Wheat yield in farmers’ fields averaged 1.9 t ha− 1 corresponding to ca. 20% of Yw. Most of the yield gap was attributed to the technology yield gap (> 50% of Yw) but narrowing efficiency (ca. 10% of Yw) and resource yield gaps (ca. 15% of Yw) with current technologies can nearly double actual yields and contribute to achieve wheat self-sufficiency in Ethiopia. There were small differences in the relative contribution of the intermediate yield gaps to the overall yield gap across agro-ecological zones, administrative zones, and farming systems. At farm level, oxen ownership was positively associated with the wheat cultivated area in zones with relatively large cultivated areas per household (West Arsi and North Showa) while no relationship was found between oxen ownership and the amount of inputs used per hectare of wheat in the zones studied. This is the first thorough yield gap decomposition for wheat in Ethiopia and our results suggest government policies aiming to increase wheat production should prioritise accessibility and affordability of inputs and dissemination of technologies that allow for precise use of these inputs.

scholarly journals Methodology of Analyzing Maize Density Loss in Smallholder’s Fields and Potential Optimize Approach

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 480
Author(s):  
Zhichao An ◽  
Chong Wang ◽  
Xiaoqiang Jiao ◽  
Zhongliang Kong ◽  
Wei Jiang ◽  
...  
Keyword(s):  
Decision Making ◽  
Food Security ◽  
Zea Mays L ◽  
Plant Density ◽  
Density Difference ◽  
Yield Gap ◽  
Farmer Decision Making ◽  
Yield Gaps ◽  
Farmer Survey ◽  
Experimental Plots

Increasing plant density is a key measure to close the maize (Zea mays L.) yield gap and ensure food security. However, there is a large plant density difference in the fields sown by agronomists and smallholders. The primary cause of this phenomenon is the lack of an effective methodology to systematically analyze the density loss. To identify the plant density loss processes from experimental plots to smallholder fields, a research methodology was developed in this study involving a farmer survey and measurements in a smallholder field. The results showed that the sowing density difference caused by farmer decision-making and plant density losses caused by mechanical and agronomic factors explained 15.5%, 5.5% and 6.8% of the plant density difference, respectively. Changing smallholder attitudes toward the value of increasing the plant density could help reduce this density loss and increase farm yields by 12.3%. Therefore, this methodology was effective for analyzing the plant density loss, and to clarify the primary causes of sowing density differences and plant density loss. Additionally, it was beneficial to identify the priorities and stakeholders who share responsibility for reducing the density loss. The methodology has wide applicability to address the sowing density differences and plant density loss in other areas to narrow crop yield gaps and ensure food security.

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