Consultative Group on International Agricultural Research

Freshwater pollution is, together with climate change, one of today’s most severe and pervasive threats to the global environment. Comprehensive and spatially explicit scenarios covering a wide range of constituents for freshwater quality are currently scarce. In this Global Perspective paper, we propose a novel model-based approach for five water quality constituents relevant for human and ecosystem health (nitrogen, biochemical oxygen demand, anthropogenic chemicals, fecal coliform, and arsenic). To project the driving forces and consequences for emissions and impacts, a set of common data based on the same assumptions was prepared and used in different large-scale water quality models including all relevant demographic, socioeconomic, and cultural changes, as well as threshold concentrations to determine the risk for human and ecosystem health. The analysis portrays the strong links among water quality, socio-economic development, and lifestyle. Internal consistency of assumptions and input data is a prerequisite for constructing comparable scenarios using different models to support targeted policy development.

Orphan crops are crops that are of substantial importance to food security and economic growth at a local or regional scale, yet lacking investment in crop improvement and seed systems development. Tef is an example of such an orphan crop. It is vital to economy and food systems in the Horn of Africa, yet investment in breeding and agronomy is very limited. Since almost 20 years, the Syngenta Foundation for Sustainable Agriculture has invested in tef, supporting work to both develop and disseminate improved varieties to farmers in Ethiopia. To date, this has led to the release of four improved varieties. As the project also invested in the development of seed systems for improved tef varieties, it allowed us to monitor seed production and variety adoption over time. The data obtained from seed production monitoring over 7 years and 4 varieties from both formal and informal seed systems shows a total of 1227 tons of tef seed from improved varieties delivered to farmers in Ethiopia. Assuming an average genetic gain of 0.4 tons per hectare, this suggests that the value generated to farmers and local value chains from tef breeding and seed systems development exceeds the investment by an order of at least 2.5. With this paper, we want to make a case for more long-term investment in breeding and seed systems development and stimulate replication of the approach to other orphan crops. We further want to call for a continued investment in tef crop improvement and seed systems development.

Measurement and maladaptation are being weaponized

Cassava is an important food crop in western Kenya, yet its production is challenged by pests and diseases that require routine monitoring to guide development and deployment of control strategies. Field surveys were conducted in 2022 and 2023 to determine the prevalence, incidence and severity of cassava mosaic disease (CMD) and cassava brown streak disease (CBSD), whitefly numbers and incidence of cassava green mite (CGM) in six counties of western Kenya. Details of the encountered cassava varieties were carefully recorded to determine the adoption of improved varieties. A total of 29 varieties were recorded, out of which 13 were improved, although the improved varieties were predominant in 60% of fields and the most widely grown variety was MM96/4271. The CMD incidence was higher in 2022 (26.4%) compared to 2023 (10.1%), although the proportion of CMD attributable to whitefly infection was greater (50.6%) in 2023 than in 2022 (18.0%). The CBSD incidence in 2022 was 6.4%, while in 2023 it was 4.1%. The CMD incidence was significantly lower (5.9%) for the improved varieties than it was for the local varieties (35.9%), although the CBSD incidence did not differ significantly between the improved (2.3%) and local varieties (9.7%). Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV) were both detected. Most infections were single CBSV infections (82.9%), followed by single UCBSV (34.3%) and coinfection with both viruses (16.7%). Whiteflies were more abundant in 2023, in which 28% of the fields had super-abundant populations of >100/plant, compared to 5% in 2022. KASP SNP genotyping designated 92.8% of the specimens as SSA-ECA for 2022, while it was 94.4% for 2023. The cassava green mite incidence was 65.4% in 2022 compared to 79.9% in 2023. This study demonstrates that cassava viruses, whiteflies and cassava green mites continue to be important constraints to cassava production in western Kenya, although the widespread cultivation of improved varieties is reducing the impact of cassava viruses. The more widespread application of high-quality seed delivery mechanisms could further enhance the management of these pests/diseases, coupled with wider application of IPM measures for whiteflies and mites.

The phenotyping needs to be optimized and aim for the desired precision at low costs because selection decisions are mainly based on muti-environmental trials. This might be possible by applying relationship measurements and genomic prediction on sparse phenotyping designs in plant breeding. Our research used genomic data and relationship measurements between the training (Full testing genotypes) and testing set (Sparse testing genotypes) to optimize the line allocation of genotypes to subsets in sparse testing. Different sparse phenotyping methods were mimicked based on the percentage (%) of lines in the full set, the number of partially tested lines, the number of tested environments, balanced and unbalanced methods for allocating the lines among the environments. The eight relationship measurements were utilized to calculate relatedness between full and sparse set genotypes. The accuracy measurements were calculated and used on the relationship measurements to identify the performance of the relationship measurements on sparse phenotyping designs. The results demonstrate that balanced and allocating 50% of lines to the full set designs have a higher Pearson correlation in terms of accuracy measurements than assigning the 30% of lines to the full set and balanced sparse methods. By reducing untested environments per sparse set, the accuracy of measurements is enhanced but reduces saved phenotypic cost compared with the complete phenotyping experiments. The relationship measurements exhibit a low significant Pearson correlation ranging from 0.20 to 0.31 using the accuracy measurements in sparse phenotyping experiments. The positive Pearson correlation shows that the maximization of the accuracy measurements can be helpful to the optimization of the line allocation on sparse phenotyping designs.

A validated marker system is crucial to running an effective genomics-assisted breeding program. We used 36 Kompetitive Allele-Specific PCR (KASP) markers to genotype 376 clones from the biofortified cassava pipeline, and fingerprinted 93 of these clones with DArTseq markers to characterize breeding materials and evaluate their relationships. The discriminating ability of the 36-quality control (QC) KASP and 6602 DArTseq markers was assessed using 92 clones genotyped in both assays. In addition, trait-specific markers were used to determine the presence or absence of target genomic regions. Hierarchical clustering identified two major groups, and the clusters were consistent with the breeding program origins. There was moderate genetic differentiation and a low degree of variation between the identified groups. The general structure of the population was similar using both assays. Nevertheless, KASP markers had poor resolution when it came to differentiating the genotypes by seed sources and overestimated the prevalence of duplicates. The trait-linked markers did not achieve optimal performance as all markers displayed variable levels of false positive and/or false negative. These findings represent the initial step in the application of genomics-assisted breeding for the biofortified cassava pipeline, and will guide the use of genomic selection in the future.

Undernutrition and micronutrient deficiencies such as anemia are considered significant public health challenges in Bangladesh, which enhancing fish consumption is a well-established food-based intervention to address these. This paper documents the establishment of community-based fish chutney production and reports the impact of its consumption on mid-upper arm circumference (MUAC) and hemoglobin (Hb) levels among targeted 150 pregnant and lactating women (PLW) in rural Bangladesh. A fish chutney was developed using locally available ingredients followed by a series of laboratory tests, including nutrient composition, shelf-life and food safety. A community-based fish chutney production process was designed to: (1) supply locally available ingredients for processing; (2) establish two fish drying sites; (3) initiate a community-based production site; and (4) distribute fish chutney to PLW for one year by six women nutrition field facilitators. Then a pre- and post-intervention study was designed for a selected 150 PLW to receive 30 g of fish chutney daily for 12 months. Differences in mean MUAC and Hb levels pre- and post-consumption were analyzed using one-way analysis of variance. Consumption of 30 g of fish-chutney resulted in significant increases of the mean values of Hb levels and MUAC among the targeted PLW.

Genomic selection (GS) is revolutionizing plant breeding. However, its practical implementation is still challenging, since there are many factors that affect its accuracy. For this reason, this research explores data augmentation with the goal of improving its accuracy. Deep neural networks with data augmentation (DA) generate synthetic data from the original training set to increase the training set and to improve the prediction performance of any statistical or machine learning algorithm. There is much empirical evidence of their success in many computer vision applications. Due to this, DA was explored in the context of GS using 14 real datasets. We found empirical evidence that DA is a powerful tool to improve the prediction accuracy, since we improved the prediction accuracy of the top lines in the 14 datasets under study. On average, across datasets and traits, the gain in prediction performance of the DA approach regarding the Conventional method in the top 20% of lines in the testing set was 108.4% in terms of the NRMSE and 107.4% in terms of the MAAPE, but a worse performance was observed on the whole testing set. We encourage more empirical evaluations to support our findings.

Common wheat (Triticum aestivum) is a hexaploid crop comprising three diploid sub-genomes labeled A, B, and D. The objective of this study is to investigate whether there is a discernible influence pattern from the D sub-genome with epistasis in genomic models for wheat diseases. Four genomic statistical models were employed; two models considered the linear genomic relationship of the lines. The first model (G) utilized all molecular markers, while the second model (ABD) utilized three matrices representing the A, B, and D sub-genomes. The remaining two models incorporated epistasis, one (GI) using all markers and the other (ABDI) considering markers in sub-genomes A, B, and D, including inter- and intra-sub-genome interactions. The data utilized pertained to three diseases: tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB), for synthetic hexaploid wheat (SHW) lines. The results (variance components) indicate that epistasis makes a substantial contribution to explaining genomic variation, accounting for approximately 50% in SNB and SB and only 29% for TS. In this contribution of epistasis, the influence of intra- and inter-sub-genome interactions of the D sub-genome is crucial, being close to 50% in TS and higher in SNB (60%) and SB (60%). This increase in explaining genomic variation is reflected in an enhancement of predictive ability from the G model (additive) to the ABDI model (additive and epistasis) by 9%, 5%, and 1% for SNB, SB, and TS, respectively. These results, in line with other studies, underscore the significance of the D sub-genome in disease traits and suggest a potential application to be explored in the future regarding the selection of parental crosses based on sub-genomes.

Peste des petits ruminants (PPR) is a highly contagious viral disease and one of the deadliest affecting wild goats, sheep, and small ruminants; however, goats are generally more sensitive. The causative agent is the Peste des Petits Ruminants virus (PPRV), which is a single-stranded RNA virus of negative polarity belonging to the Paramyxoviridae family. In February 2020, an active outbreak of PPR was reported in a herd of a transhumant farmer in the village of Gainth Pathé (department of Kounguel, Kaffrine region, Senegal). Of the ten swabs collected from the goats, eight returned a positive result through a quantitative real-time PCR. The sample that yielded the strongest signal from the quantitative real-time PCR was further analyzed with a conventional PCR amplification and direct amplicon sequencing. A phylogenetic analysis showed that the sequence of the PPR virus obtained belonged to lineage IV. These results confirm those found in the countries bordering Senegal and reinforce the hypothesis of the importance of animal mobility between these neighboring countries in the control of PPRV. In perspective, following the discovery of this lineage IV in Senegal, a study on its dispersion is underway throughout the national territory. The results that will emerge from this study, associated with detailed data on animal movements and epidemiological data, will provide appropriate and effective information to improve PPR surveillance and control strategies with a view to its eradication.

Cassava (Manihot esculenta Crantz) is an essential crop with increasing importance for food supply and as raw material for industrial processing. The crop is vegetatively propagated through stem cuttings taken at the end of the growing cycle and its low multiplication rate and the high cost of stem transportation are detrimental to the increasing demand for high-quality cassava planting materials. Rapid multiplication of vegetative propagules of crops comprises tissue culture (TC) and semi-autotroph hydroponics (SAH) that provide cost-effective propagation of plant materials; however, they contrast the need for specific infrastructure, special media and substrates, and trained personnel. Traditional methods such as TC and SAH have shown promise in efficient plant material propagation. Nonetheless, these techniques necessitate specific infrastructure, specialized media and substrates, as well as trained personnel. Moreover, losses during the intermediate nursery and adaptation stages limit the overall effectiveness of these methods. Building upon an earlier report from Embrapa Brazil, which utilized mature buds from cassava for rapid propagation, we present a modified protocol that simplifies the process for wider adoption. Our method involves excising single nodes with attached leaves from immature (green) cassava stems at 2 months after planting (MAP). These nodes are then germinated in pure water, eliminating the need for specific growth substrates and additional treatments. After the initial phase, the rooted sprouts are transferred into soil within 1–8 weeks. The protocol demonstrates a high turnover rate at minimal costs. Due to its simplicity, cost-effectiveness, and robustness, this method holds significant promise as an efficient means of producing cassava planting materials to meet diverse agricultural needs.

Cocoa is the economic backbone of Côte d’Ivoire and Ghana, making them the leading cocoa-producing countries in the world. However, cocoa farming has been a major driver of deforestation and landscape degradation in West Africa. Various stakeholders are striving for a zero-deforestation cocoa sector by implementing sustainable farming strategies and a more transparent supply chain. In the context of tracking cocoa sources and contributing to cocoa-driven deforestation monitoring, the demand for accurate and up-to-date maps of cocoa plantations is increasing. Yet, access to limited reference data and imperfect data quality can impose challenges in producing reliable maps. This study classified full-sun-cocoa-growing areas using limited reference data relative to the large and heterogeneous study areas in Côte d’Ivoire and Ghana. A Sentinel-2 composite image of 2021 was generated to train a random forest model. We undertook reference data refinement, selection of the most important handcrafted features and data sampling to ensure spatial independence. After refining the quality of the reference data and despite their size reduction, the random forest performance was improved, achieving an overall accuracy of 85.1 ± 2.0% and an F1 score of 84.6 ± 2.4% (mean ± one standard deviation from ten bootstrapping iterations). Emphasis was given to the qualitative visual assessment of the map using very high-resolution images, which revealed cases of strong and weak generalisation capacity of the random forest. Further insight was gained from the comparative analysis of our map with two previous cocoa classification studies. Implications of the use of cocoa maps for reporting were discussed.

Climate change has been designated the most serious environmental challenge of the twenty-first century, and it will remain so in the future. To adapt to the ill effects of climate change and variability, location-specific climate analysis is essential to provide actionable evidence for informed decision making. The goal of this research was to evaluate current and future rainfall and temperature trends in central Ethiopia. We took 33 (1986–2018) years of daily rainfall and temperature data from ten stations from the National Meteorological Services Agency. The Markov chain model was used to fill in the missing values. Trends of climate variables and their extremes were analyzed using the Mann–Kendall trend test. The results showed that rainfall exhibited significant increasing trends (p < 0.05) at 80% of the stations included in the study. The start of the Kiremit season varied from June (Jun 4 at Bako) to July (Jul 1 at Shashemane) among stations; it starts June 4, June 6, and June 24 at Bako, Ambo, and Melkassa. For Asgori, the cessation date was October 1, and for Ambo, it was October 17. The average growing season varies from 95 to 136 days, and we found the shortest and longest growing seasons at Melkassa (95 days) and Ambo (136 days). The Sen’s slope results showed that rainfall increased in most of the study sites from 0.2* to 12.44*, whereas it decreased from − 0.12* to − 0.21*. This has implications for agricultural practices and crop and variety choices for major crops grown in the area. To solve this problem, policymakers must work on creating awareness for the local community, which can help them withstand future climate change.