Smallholders' coping mechanisms with wheat rust epidemics: Lessons from Ethiopia

Associations between women's bargaining power and the adoption of rust-resistant wheat varieties in Ethiopia

The dynamics in intra-household decision-making are often neglected in literature on the adoption of agricultural innovations. However, households' farm management decisions are often made following negotiations between female and male farmers. These may differ in terms of individual bargaining power and personal preferences. A better understanding of the links between gender roles in household decision-making and the adoption of technologies is postulated to enhance the uptake of innovations in smallholder farming systems. In this study, we use survey data from 1,088 wheat-producing households in Ethiopia to analyze the links between women's role in household decisions concerning crop production and the adoption and turnover rates of rust-resistant wheat varieties. We interviewed female and male respondents from the same households, but separately, which facilitated capturing individual perceptions and the intra-household dynamics in decision-making. To account for observed heterogeneity that may simultaneously determine the level of women's agency and varietal adoption by households, we employed Inverse Probability-Weighted Regression Adjustment (IPWRA). A positive association was found between women's role in decision-making concerning choice of wheat seed and household adoption of rust-resistant wheat varieties and wheat varietal turnover. Spouses may be in agreement or have different opinions regarding their decision-making roles. The disagreement scenario in which the wife claims to have a role in decision-making is associated with lower adoption rates of rust-resistant wheat varieties and less frequent testing of new varties in recent growing seasons, compared to a scenario where both spouses agree that wives do not have a role. We conclude that gender-disaggregated data and the examination of intra-household decision-making can offer novel and valuable insights for designing and implementing strategies to enhance the uptake of agricultural technologies among smallholders. The results emphasize the need to include complementary perspectives on the intra-household decision-making process.

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Context

Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency.

Objective

The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers' fields in contrasting farming systems worldwide.

Methods

A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time.

Results

Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent.

Conclusion

Big data from farmers' fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space.

Significance

The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties' response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS-NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

Global dispersal and diversity of rust fungi in the context of plant health

Long-distance dispersal of plant pathogens at the continental scale may have strong implications on plant health, in particular when incursions result in spread of disease to new territories where the disease was previously absent or insignificant. These dispersions may be caused by airborne transmission of spores or accidental spread via human travel and trade. Recent surveillance efforts of cereal rust fungi have demonstrated that incursion of new strains with superior fitness into areas where the disease is already established may have similar implications on plant health. Since dispersal events are highly stochastic, irrespective of transmission mechanism, critical mitigation efforts include preparedness by coordinated pathogen surveillance activities, host crop diversification, and breeding for disease resistance with low vulnerability to sudden changes in the pathogen population.

Gender differences in smallholders' socioeconomic networks and acquisition of seed of improved wheat varieties in Ethiopia

Enhancing farmers' access to improved seeds is essential to increase productivity and ensure food security in the Global South. However, for many socially marginalized groups, seed access is constrained by the weak institutions governing the input supply chains and the dissemination of information. Using cross-sectional survey data collected from 1,088 farming households in three major wheat-growing regional states of Ethiopia in 2021, this paper assesses empirically how participation in different socioeconomic institutions by men and women farmers shapes their access to and acquisition of seed of improved wheat varieties. The results show that the seed market in the study area is largely informal, where the recycling of wheat seeds from the previous season is a common practice among both male- and female-headed households. However, a significant difference exists between male- and female-headed households regarding patterns of varietal use, with male farmers growing newer wheat varieties more frequently. Men are also more active than women in local social and economic institutions, and their participation is positively associated with the adoption of new wheat varieties. Thus, strengthening the local social and economic institutions and supporting equitable participation of both male- and female-headed households in these institutions could facilitate the diffusion of quality seeds of improved and recently released wheat varieties in countries where the informal seed system plays a major role in seed acquisition.

Gender, caste, and heterogeneous farmer preferences for wheat varietal traits in rural India

The research on crop genetic enhancement has created a continuous flow of new, improved germplasm for the benefit of farmers and consumers of the Global South during and after the Green Revolution. Understanding farmers’ heterogeneous preferences for varietal traits in different market segments and incorporating the prominent ones in crop breeding programs are expected to facilitate a faster diffusion of these new varieties. Albeit knowing little about farmers’ trait preferences in South Asia, public-sector breeding programs prioritize yield enhancement and risk reduction over other varietal traits. Against this backdrop, we examined wheat farmers’ preferences for varietal traits in Central India, where the prevailing varietal turnover rate has been meager. We conducted a ranking exercise among 120 individuals, followed by a sex-disaggregated survey with a choice experiment among 420 farm-households in 2019. The lowest varietal turnover rate was observed for the socially marginalized castes. Most women respondents were not actively involved in making decisions related to wheat cultivation, including varietal selection. However, the results indicate that marginalized caste and women farmers are open to experimentation with new varieties, as shown by their positive willingness to pay for improved varietal traits. Across the gender and caste groups, grain quality attributes (especially chapati quality) were ranked high, above the yield-enhancing and risk-ameliorating traits. From the observed patterns, one could deduce that developing and disseminating improved varieties with better grain quality and targeting women and marginalized social groups in varietal dissemination programs could enhance farmer adoption of new, improved germplasm and wheat productivity in Central India.

Wheat rust epidemics damage Ethiopian wheat production: A decade of field disease surveillance reveals national-scale trends in past outbreaks

Wheat rusts are the key biological constraint to wheat production in Ethiopia-one of Africa's largest wheat producing countries. The fungal diseases cause economic losses and threaten livelihoods of smallholder farmers. While it is known that wheat rust epidemics have occurred in Ethiopia, to date no systematic long-term analysis of past outbreaks has been available. We present results from one of the most comprehensive surveillance campaigns of wheat rusts in Africa. More than 13,000 fields have been surveyed during the last 13 years. Using a combination of spatial data-analysis and visualization, statistical tools, and empirical modelling, we identify trends in the distribution of wheat stem rust (Sr), stripe rust (Yr) and leaf rust (Lr). Results show very high infection levels (mean incidence for Yr: 44%; Sr: 34%; Lr: 18%). These recurrent rust outbreaks lead to substantial economic losses, which we estimate to be of the order of 10s of millions of US-D annually. On the widely adopted wheat variety, Digalu, there is a marked increase in disease prevalence following the incursion of new rust races into Ethiopia, which indicates a pronounced boom-and-bust cycle of major gene resistance. Using spatial analyses, we identify hotspots of disease risk for all three rusts, show a linear correlation between altitude and disease prevalence, and find a pronounced north-south trend in stem rust prevalence. Temporal analyses show a sigmoidal increase in disease levels during the wheat season and strong inter-annual variations. While a simple logistic curve performs satisfactorily in predicting stem rust in some years, it cannot account for the complex outbreak patterns in other years and fails to predict the occurrence of stripe and leaf rust. The empirical insights into wheat rust epidemiology in Ethiopia presented here provide a basis for improving future surveillance and to inform the development of mechanistic models to predict disease spread.

Ethiopia's transforming wheat landscape: tracking variety use through DNA fingerprinting

Ethiopia is the largest wheat producer in sub-Saharan Africa yet remains a net importer. Increasing domestic wheat production is a national priority. Improved varieties provide an important pathway to enhancing productivity and stability of production. Reliably tracking varietal use and dynamics is a challenge, and the value of conventional recall surveys is increasingly questioned. We report the first nationally representative, large-scale wheat DNA fingerprinting study undertaken in Ethiopia. Plot level comparison of DNA fingerprinting with farmer recall from nearly 4000 plots in the 2016/17 season indicates that only 28% of farmers correctly named wheat varieties grown. The DNA study reveals that new, rust resistant bread wheat varieties are now widely adopted. Germplasm originating from CGIAR centres has made a significant contribution. Corresponding productivity gains and economic benefits have been substantial, indicating high returns to investments in wheat improvement. The study provides an accurate assessment of wheat varietal status and sets a benchmark for national policy-makers and donors. In recent decades, the Ethiopian wheat landscape has transformed from local tetraploid varieties to widespread adoption of high yielding, rust resistant bread wheat. We demonstrate that DNA fingerprinting can be applied at scale and is likely to transform future crop varietal adoption studies.

Misidentification by farmers of the crop varieties they grow: Lessons from DNA fingerprinting of wheat in Ethiopia

Accurate identification of crop varieties grown by farmers is crucial, among others, for crop management, food security and varietal development and dissemination purposes. One may expect varietal identification to be more challenging in the context of developing countries where literacy and education are limited and informal seed systems and seed recycling are common. This paper evaluates the extent to which smallholder farmers misidentify their wheat varieties in Ethiopia and explores the associated factors and their implications. The study uses data from a nationally representative wheat growing sample household survey and DNA fingerprinting of seed samples from 3,884 wheat plots in major wheat growing zones of Ethiopia. 28-34% of the farmers correctly identified their wheat varieties. Correct identification was positively associated with farmer education and seed purchases from trusted sources (cooperatives or known farmers) and negatively associated with seed recycling. Farmers' varietal identification thereby is problematic and leads to erroneous results in adoption and impact assessments. DNA fingerprinting can enhance varietal identification but remains mute in the identification of contextual and explanatory factors. Thus, combining household survey and DNA fingerprinting approaches is needed for reliable varietal adoption and impact assessments, and generate useful knowledge to inform policy recommendations related to varietal replacement and seed systems development.