Agrobiodiversity and climate adaptation: insights for risk management in small-scale farming

Agriculture is a dynamic activity that sustains food and other goods for global human population. Aiming to ensure global food security, the sector has evolved dramatically, especially over the last century with the introduction of high-yielding crops, improved technology and pathogen resistant varieties, to name a few. According to the Food and Agriculture Organisation of the United Nations (FAO), the food security issue is still present. In 2019, around 26% of the world population experienced either a moderate or severe level of food insecurity. Climate change makes the challenge of food security even more pressing. It is argued that increased agrobiodiversity through farm diversification and varietal selection can help farmers to cope with the negative effects of climate change while ensuring food security. However, such approaches have been difficult to scale up. One could argue that we often lack information to understand the contexts that drive farmers’ adaptation decisions and how to develop recommendations for adaptation. In this thesis, I developed methods and tools to support farmers and stakeholders in adapting to a changing climate. I present results from three continents to improve the understanding of the food systems at the farm level, and specifically in smallholder farming. I provide insights for the different biological levels: species level, focusing on trees as slow grower organisms for interspecific diversification; varieties level, looking for locally adapted phenotypes; and genotype level, focusing on G × E interactions to support crop breeding for intraspecific diversification. From the first part of the study, conducted in Central America, the results showed that farmers have a clear preference to a set of adaptation strategies, with reforestation (agroforestry) as the first choice (Paper 1). Crop variety management is the least preferred choice of the top-5. In the second part of the study, I assessed the impacts of climate change on the habitats of the 100 most common tree species used in coffee (Coffea arabica L.) and cocoa (Theobroma cacao L.) agroforestry in Central America (Paper 2). The results showed that the most preferred trees are the most vulnerable, but farmers could re-think the agroforestry designs using a portfolio of underutilised species already present in low densities at the current systems. In the third part of the study, I employed a citizen science approach that can scale variety testing and help farmers to select the right crop variety for their farms (Paper 3). I tested this approach with common bean (Phaseolus vulgaris L.) in Nicaragua, bread wheat (Triticum aestivum L.) in India and durum wheat (Triticum durum Desf.) in Ethiopia. The results showed that the approach reduces geographic sampling bias and could be scaled to provide tailored recommendations for crop variety management. I also show, with durum wheat genotypes in Ethiopia, that linking the farmer-generated data to scientist-generated data can support breeding programs targeting challenging crop production environments using a data-driven decentralised approach (Paper 4). The approach is fully replicable, and part of its workflow is presented in this thesis (Paper 5 and Paper 6). Overall, the results of this thesis should be seen as starting point to develop lines of research that support recommendations to adapt agricultural systems to a changing climate.Landbruk er en dynamisk aktivitet som skal sikre mat og andre varer som verdens befolkning til enhver tid trenger. Med global matsikkerhet som mål har landbruket utviklet seg mye, særlig det siste århundre, blant annet ved å ta i bruk høytytende grøder, forbedret dyrkningsteknikk og sorter som er resistente mot sykdommer. I følge FNs organisasjon for ernæring og landbruk (FAO) er matsikkerhet fortsatt et aktuelt tema. I 2019 opplevde rundt 26% av verdens befolkning en moderat eller alvorlig grad av usikkerhet rundt tilgangen på mat. Klimaendringer gjør utfordringene rundt matsikkerhet enda mer krevende. Hele matvaresystemet må endres for å takle klimaendringer og samtidig sikre nok mat til alle. Det hevdes at økt biologisk mangfold i landbruket kan hjelpe bønder i å takle klimaendringene og samtidig sikre matproduksjonen, dette gjennom mer variasjon i hva som dyrkes på gårdsnivå og gjennom et bedre utvalg av sorter. Slike tilnærminger har imidlertid vist seg å være vanskelige å skalere opp. Man kan hevde at vi ofte mangler tilstrekkelig med informasjon for fullt ut kunne forstå hva som avgjør bønders valg knytta til klimatilpasning - og videre hvordan man så skal kunne utvikle rådgivingen for dette. I denne avhandlingen har jeg utviklet metoder og verktøy som kan brukes for å hjelpe bønder og andre i å tilpasse seg til endringer i klima. Jeg presenterer resultater fra tre ulike kontinent, dette for gi eksempel på hvordan en økt forståelse av matvaresystemene kan fungere på gårdsnivå, og særlig på små gårder. Jeg går inn på ulike biologiske nivå: på artsnivå, med fokus på trær som vokser langsomt og som gir stor diversitet mellom arter; på sortsnivå, ved å søke å finne lokalt tilpassa fenotyper; og på genotypenivå, ved å fokusere på samspillet mellom gener og miljø (G × E), dette for å støtte foredlingsarbeid for økt diversitet innenfor arter. Resultater fra første delen av studiet som ble gjennomført i Mellom-Amerika viste at bønder har en klar preferanse for et sett av strategier i forhold til klimatilpasning, med agroskogbruk som førstevalg (Artikkel 1). Sortsvalg er det minst foretrukne valget av topp fem. I andre del av studiet undersøkte jeg hvor egnet dagens vokseplasser i Mellom-Amerika er for de 100 vanligste trærne som anvendes innenfor agroskogbruket med kaffe (Coffea arabica L.) og kakao (Theobroma cacao L.), dette med tanke på framtidige klimascenarier (Artikkel 2). Resultatene viste at de mest foretrukne trærne er de mest sårbare og at bøndene burde tenke nytt i forhold til utforming av agroskogbruket, dette ved å ta i bruk en rekke mindre anvendte arter som likevel finnes i dagens system. I tredje del av studiet anvendte jeg grasrotforskning som tilnærming for å skalere opp sortsforsøk og hjelpe bønder med å velge riktig sort for gårdene sine (Artikkel 3). Jeg undersøkte dette i bønner (Phaseolus vulgaris L.) i Nicaragua, vanlig brødhvete (Triticum aestivum L.) i India og durumhvete (Triticum durum Desf.) i Etiopia. Resultatene viste at en slik tilnærming reduserer feilkilder knyttet til geografisk representasjon og kan skaleres opp for å gi mer skreddersydde løsninger for bruk av sorter. I arbeidet med ulike genotyper av durumhvete i Etiopia viser jeg at foredlingsprogrammer kan styrkes at ved å koble grasrot-genererte data til forsker-genererte data gjennom en desentralisert tilnærming (Artikkel 4). Tilnærmingen er fullt mulig å gjenta, og en del av arbeidsflyten er presentert i avhandlingen (Artikkel 5 og 6). Samlet sett bør resultatene fra denne avhandlingen sees som en start på å utvikle en forskningen som kan bistå med anbefalinger slik at landbruket bedre kan tilpasse seg til et klima i endring.publishedVersio

The future of coffee and cocoa agroforestry in a warmer Mesoamerica

Climate change threatens cofee production and the livelihoods of thousands of families in Mesoamerica that depend on it. Replacing cofee with cocoa and integrating trees in combined agroforestry systems to ameliorate abiotic stress are among the proposed alternatives to overcome this challenge. These two alternatives do not consider the vulnerability of cocoa and tree species commonly used in agroforestry plantations to future climate conditions. We assessed the suitability of these alternatives by identifying the potential changes in the distribution of cofee, cocoa and the 100 most common agroforestry trees found in Mesoamerica. Here we show that cocoa could potentially become an alternative in most of cofee vulnerable areas. Agroforestry with currently preferred tree species is highly vulnerable to future climate change. Transforming agroforestry systems by changing tree species composition may be the best approach to adapt most of the cofee and cocoa production areas. Our results stress the urgency for land use planning considering climate change efects and to assess new combinations of agroforestry species in cofee and cocoa plantations in Mesoamerica

Conservation gaps in traditional vegetables native to Europe and Fennoscandia

Vegetables are rich in vitamins and other micronutrients and are important crops for healthy diets and diversification of the food system, and many traditional (also termed underutilized or indigenous) species may play a role. The current study analyzed 35 vegetables with a European region of diversity with the effort to map the conservation status in Fennoscandia and beyond. We mapped georeferenced occurrences and current genebank holdings based on global databases and conducted conservation gaps analysis based on representativeness scores in situ and ex situ. Out of the 35 target species, 19 got at a high priority score for further conservation initiatives, while another 14 species got a medium priority score. We identified a pattern where traditional vegetables are poorly represented in genebank holdings. This corresponds well to a lack of attention in the scientific community measured in number of published papers. Considering the grand challenges ahead in terms of climate change, population growth and demand for sustainability, traditional vegetables deserve greater attention. Our contribution is to provide a basis for conservation priorities among the identified vegetables species native to Fennoscandia

ag5Tools: An R package for downloading and extracting agrometeorological data from the AgERA5 database

Agrometeorological data is important in agricultural research, especially in agronomy and crop science, for investigating genotype by environment interactions. The AgERA5 dataset from the Copernicus Climate Data Store provides free and public access to global gridded daily agrometeorological data, from 1979 to present, with ready to use variables tailored for agricultural and agro-ecological studies. We developed the R package ag5Tools, which provides a simplified interface for downloading and extracting AgERA5 data. The package facilitates extracting time-series data for sets of geographic points in a format that can be conveniently used in statistical models applied in agricultural research. The use of the package is demonstrated with a synthetic dataset of multi-location trials in Arusha, Tanzania

Climate variability indices for ecological and crop models in R: the climatrends package.

Abiotic factors play an important role in most ecological and crop systems that depend on certain levels of temperature, light and precipitation to initiate important physiological events (Schulze et al., 2019). Understanding how these factors drive the physiological processes is a key approach to provide recommendations for adaptation and biodiversity conservation in applied ecology studies. The package climatrends aims to provide the methods in R (R Core Team, 2020) to compute precipitation and temperature indices that serve as input for climate and crop models (Kehel et al., 2016; van Etten et al., 2019), trends in climate change (Aguilar et al., 2005; de Sousa et al., 2018) and applied ecology (Liu & El-Kassaby, 2018; Prentice et al., 1992

The tricot approach. Guide for large-scale participatory experiments

Triadic comparison of technology options (tricot) is a research methodology that helps farmers to identify the most suitable technologies for the local conditions of their farm. Tricot (read: ‘try-cot’) engages farmers as ‘farmer researchers’ in the testing or validation of new crop varieties and other promising technologies. Tricot is supported by the ClimMob digital platform (https://climmob.net). This guide provides an introduction to tricot and each of the steps in the experimental cycle

Combining legacy data from heterogeneous crop trials to identify genotype by environment interactions using model-based recursive partitioning

Crop variety trials are important to generate insights on variety environmental adaptation, but this requires that varieties should be tested in a wide range of environments to consider the complexity of genotype by environment interactions. Given the substantial costs of collecting trial data, agricultural science needs to maximize the insights extracted from existing data. An alternative is to combine data from different trials performed in different environments using a data synthesis approach. Analyzing aggregated data from different trials could be challenging as datasets are often heterogeneous. Previous research has shown that ranking-based methods can deal with heterogeneous data from different trials to gain insights in average performance of genotypes, but not in responses to different environmental conditions. We show that such insights can be obtained from heterogeneous legacy field trial data by means of model-based recursive partitioning, using climatic covariates from open access databases. We applied this strategy to analyze the reaction of different banana cultivars to black leaf streak disease across several environments. This data-driven approach allowed to integrate heterogeneous datasets, which differ in measurements scales, experimental design, and testing environments. In our preliminary results, we found that cultivar reaction to black leaf streak disease is driven by both genotypic and climatic factors. The main agroclimatic variables identified by our model are the diurnal temperature range (DTR) and maximum length of consecutive days with rain >= 1 mm (MLWS). We show the potential of this method, which allows to gain cumulative insights in genotype by environment interactions as more trial data becomes available

Future market segments for hybrid maize in East Africa

The current seed product market segmentation by the Consultative Group on International Agricultural Research (CGIAR) for maize in East Africa includes four segments, which differ by agro-ecological zone and maturity class. However, considering the lengthy period required to produce a variety, from initial design to commercial production, a critical question should be asked: what are future segments that should be considered in discussions on current breeding investments? Video-based product concept testing (VPCT) is a novel approach for gathering insights from farmers about their varietal preferences to inform future market segmentation. This brief explains the conceptual and methodological underpinnings of VPCT. We present an application of the tool in hybrid maize. Seven new product concepts (representing potential future market segments) were identified based on discussions with breeders, seed companies and farmers, which we labelled: home use, intercropping, drought avoidance, nutritious, feed (yellow), green maize and food and fodder. These future concepts, together with the resilient benchmark product concept (the current breeding target), were evaluated through triadic comparisons with 2400 farmers in Kenya and Uganda. In Uganda, the drought avoidance concept ranked high, while in Kenya the intercropping concept stood out. Concept testing provides a strong case for new investments to integrate agronomic practices and preferences of farmers into breeding, on-farm testing and seed systems. Future work will estimate the implications of increased availability (and uptake) of these future segments on the current segmentation

Data-driven, participatory characterization of farmer varieties discloses teff breeding potential under current and future climates

In smallholder farming systems, traditional farmer varieties of neglected and underutilized species (NUS) support the livelihoods of millions of growers and consumers. NUS combine cultural and agronomic value with local adaptation, and transdisciplinary methods are needed to fully evaluate their breeding potential. Here, we assembled and characterized the genetic diversity of a representative collection of 366 Ethiopian teff (Eragrostis tef) farmer varieties and breeding materials, describing their phylogenetic relations and local adaptation on the Ethiopian landscape. We phenotyped the collection for its agronomic performance, involving local teff farmers in a participatory variety evaluation. Our analyses revealed environmental patterns of teff genetic diversity and allowed us to identify 10 genetic clusters associated with climate variation and with uneven spatial distribution. A genome-wide association study was used to identify loci and candidate genes related to phenology, yield, local adaptation, and farmers' appreciation. The estimated teff genomic offset under climate change scenarios highlighted an area around lake Tana where teff cropping may be most vulnerable to climate change. Our results show that transdisciplinary approaches may efficiently propel untapped NUS farmer varieties into modern breeding to foster more resilient and sustainable cropping systems