Developing an operational framework to diagnose yield gaps in commercial sugarcane mills

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.

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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

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03812-3 ◽

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.

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Yield gaps in composite carp culture in eastern India

Aquaculture International ◽

10.1007/s10499-021-00720-7 ◽

2021 ◽

Author(s):

Himansu Kumar De ◽

Simantini Shasani ◽

Manoj Kumar Das

Keyword(s):

Eastern India ◽

Yield Gaps

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Wheat yield gaps across smallholder farming systems in Ethiopia

Agronomy for Sustainable Development ◽

10.1007/s13593-020-00654-z ◽

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.

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

Agriculture ◽

10.3390/agriculture11060480 ◽

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

Current Opinion in Environmental Sustainability ◽

10.1016/j.cosust.2020.08.018 ◽

2020 ◽

Vol 47 ◽

pp. 95-105 ◽

Cited By ~ 1

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

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The soybean yield gap in Brazil – magnitude, causes and possible solutions for sustainable production

The Journal of Agricultural Science ◽

10.1017/s0021859615000313 ◽

2015 ◽

Vol 153 (8) ◽

pp. 1394-1411 ◽

Cited By ~ 61

Author(s):

P. C. SENTELHAS ◽

R. BATTISTI ◽

G. M. S. CÂMARA ◽

J. R. B. FARIAS ◽

A. C. HAMPF ◽

...

Keyword(s):

Water Deficit ◽

Precision Agriculture ◽

Crop Management ◽

Sustainable Production ◽

Biodiesel Production ◽

Yield Gap ◽

Soybean Yield ◽

South Central ◽

Crop Simulation ◽

Yield Gaps

SUMMARYBrazil is one of the most important soybean producers in the world. Soybean is a very important crop for the country as it is used for several purposes, from food to biodiesel production. The levels of soybean yield in the different growing regions of the country vary substantially, which results in yield gaps of considerable magnitude. The present study aimed to investigate the soybean yield gaps in Brazil, their magnitude and causes, as well as possible solutions for a more sustainable production. The concepts of yield gaps were reviewed and their values for the soybean crop determined in 15 locations across Brazil. Yield gaps were determined using potential and attainable yields, estimated by a crop simulation model for the main maturity groups of each region, as well as the average actual famers’ yield, obtained from national surveys provided by the Brazilian Government for a period of 32 years (1980–2011). The results showed that the main part of the yield gap was caused by water deficit, followed by sub-optimal crop management. The highest yield gaps caused by water deficit were observed mainly in the south of Brazil, with gaps higher than 1600 kg/ha, whereas the lowest were observed in Tapurah, Jataí, Santana do Araguaia and Uberaba, between 500 and 1050 kg/ha. The yield gaps caused by crop management were mainly concentrated in South-central Brazil. In the soybean locations in the mid-west, north and north-east regions, the yield gap caused by crop management was <500 kg/ha. When evaluating the integrated effects of water deficit and crop management on soybean yield gaps, special attention should be given to Southern Brazil, which has total yield gaps >2000 kg/ha. For reducing the present soybean yield gaps observed in Brazil, several solutions should be adopted by growers, which can be summarized as irrigation, crop rotation and precision agriculture. Improved dissemination of agricultural knowledge and the use of crop simulation models as a tool for improving crop management could further contribute to reduce the Brazilian soybean yield gap.

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

Acta Agriculturae Scandinavica Section B - Soil & Plant Science ◽

10.1080/09064710.2014.976252 ◽

2014 ◽

Vol 65 (2) ◽

pp. 132-140 ◽

Cited By ~ 4

Author(s):

Sixbert Kajumula Mourice ◽

Siza Donald Tumbo ◽

Amuri Nyambilila ◽

Cornell Lawrence Rweyemamu

Keyword(s):

Maize Productivity ◽

Yield Gaps

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Identifying maize yield and precipitation gaps in Uganda

SN Applied Sciences ◽

10.1007/s42452-021-04532-5 ◽

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.

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