Climate-Smart Plant Breeding Objectives
Modern agriculture faces enormous challenges to produce sufficient healthy food and quality industrial crops, and preserve wildlife, natural resources and biodiversity, under changing and variable climatic conditions.
Long term changes in climatic parameters such as temperature, rainfall, humidity creates extra pressure of climate change impact. Climate change is not only more heat and drought but also a more variable climate. Agriculture is one of the most vulnerable sectors to climatic hazards.
Breeding climate-smart varieties responds not only to climatic requirements but also to the preferences of producers and end consumers. Climate-smart varieties allow producers to have very good harvests in an unfavorable environment marked by climate variability.
Plant breeders, what are the key traits to study for precise breeding and rapid adoption of climate-smart varieties? To which breeding objectives a variety program must respond?
“Plant breeding innovation plays a key role in successfully and sustainably addressing the challenges linked to climate change and food security” Magloire Oteyami¹
1- Climate change or climate variability
In arid or semi-arid regions, the rains vary a lot from year to year, as the climate becomes more unstable and less predictable and involves a shift in seasons and more abiotic or biotic stress. In this context, the choice of varieties is particularly difficult. To this end, the interannual variability of precipitation constitutes a strong constraint for the varietal choice. The farmer requests varieties that provide him with a stable income and therefore not very sensitive to climatic variations².
For example, in France “we had already reached 1°C of global warming. This is equivalent to 1.8°C on earth, an increase of 0.2°C per year "underlines Nathalie De Noblet³. This therefore translates into stronger and concentrated rains, heat peaks in summer and autumn. This climatic variability has repercussions. The most significant example is rice. Rice, exposed to more than 25 cm of water or full immersion, is seriously damaged and results in large losses⁴.
The climate impacts on rice production are strongly seasonally modulated and differ considerably by region. As expected, rainfed upland rice production systems are more sensitive to soil moisture variability than irrigated paddy rice. About 10% of the variance in rice production anomalies on the national level co-varies with soil moisture changes⁵.
“If the winter has been particularly rainy and the water tables are well recharged, the soil and crops have been suffering from the lack of rain in recent months” Anais Stas, RTBF⁶
2- Key traits of climate-smart varieties
In a context of climatic variability, the key traits of varietal selection must consider the environment because of the interannual variability of climate. Thus, to make this selection safer and to formulate recommendations adapted to each situation, the key parameters to observe in developing climate-smart varieties fall into two categories. It’s first about the genotype, then the environment and finally, the interaction between genotype (G) and environment (E). This therefore contributes to reducing the impact of climatic hazard on the accuracy of comparisons between varieties.
For this purpose, the features of the environment are climatic data (rain, temperature, relative humidity) while those of the genotype are linked to genetic parameters (origin of the species, heritability, General aptitude for combination, specific aptitude for the combination, level of ploidy, selective value of the genotype, relative selective value and allelic frequency).
GxE interactions must be measured for better adaptability of varieties to the consequences of climatic variations (drought, flood, insect pests, and diseases). These climatic constraints affect the level of food availability in environments vulnerable to climatic variations. With this in mind, it is urgent to take the following parameters into account in the G x E model:
- Tolerance to abiotic stresses (drought, flooding, lodging, cold etc.) tolerance to biotic stresses (disease, virus, fungus, weeds, insects, birds)
- Average of all tests (M); Effect of a trial (Ei); Effect of repetition (Ej); Effect of a genotype (Gk);
- Interaction of genotype k and trial i (GkEi); residual error, number of trials, number of repetitions per trial.
“In breeding of varieties, the criterion of yield is less important today than resistance to disease and climate change” Sebastien Chatre, research director, RAGT 2n⁷
3- Climate-smart plant breeding objectives
Wheat breeding for bread at RAGT 2n
To meet climate evolution requirements, the breeder needs a broad genetic base. This germplasm diversity must be made up of wild species (high phenotypic variability) and cultivated species (elite cultivars).
The main breeding objective for a competitive climate-smart variety will combine, on the one hand, an adaptation to the new climate and its variability, and on the other hand, good yields for the producer and quality for the end consumer.⁸
The former will include sub-objectives like various stress resistances, earliness and hardiness, while the latter will be based on agronomic improvement traits such as photosynthetic rate, N fixing, disease and pest resistances, and quality traits such as glycaemic index, protein rate, nutritional value, taste, color etc.⁹
Further objectives will be needed to match with the crop production of a specific seed market, such as local resources (soil, water, inputs) or agricultural practices (organic, conventional, farm mecanization).
A trick of climate-smart breeding programs is to select varieties hardy enough to face climate vagaries but still competitive when annual forecasts are accurate. Such breeding objectives promise long-term successes, mostly with the increase of climate variability ahead, but require strong IT tools to handle multiple trials and heterogeneous data.
“Plant breeding must be able to meet a wide range of requirements, hence the importance of genetic diversity and the management of germplasm.” Magloire OTEYAMI⁸
Implementing climate-smart breeding objectives
Digital forage Corn data collection at Caussade Semences
Plant breeders, you elaborate complex strategies to frame your next climate-smart breeding programs. What a puzzle to set up objectives matching with the potential of your germplasm, the desires of end consumers, farmers' needs and weather forecast!
Powerful plant breeding tools are the key to take up this challenge and reach the good decisions: Get accurate phenotype and environmental data with IOT sensors, characterize your trial network with diversified co variables, explore your materials with a germplasm management system, and analyze GxE interactions with intuitive analysis tools and interactive graphs.
1 - Genetic and Plant breeding of crop for climate change and productivity Alaye H. Magloire Firmin OTEYAMI, Agronomist, Geneticist-breeder in Benin, author of this article.
2- Mixed PLS models to predict genotype x environment interaction, I.Dieng
3- Climate change and impacts on agriculture, Nathalie De Noblet
4- Submergence tolerance in rice varieties irrigated and rainfed lowland, Magloire Oteyami
5- Climate variability impacts on rice production in the Philippines, Malte F. Stuecker
6- Une nouvelle céréale pour rendre nos cultures résilientes à la sécheresse, Anaïs Stas, RTBF link
7- Les semenciers privilégient la résistance aux maladies, Laurent Marcaillou, Les Echos link
8- Can wheat beat the heat? The future of wheat breeding in Europe, Dr. Richard Summers, Cereal Breeding Lead RAGT 2n, EFM Conference, 2011
9- Plant breeding for the future - the time is now, CGIAR, 2019
10- Evaluation and Breeding of lowland rice varieties for their yield and tolerance to biotic and abiotic stresses in Benin and Togo: implication for genetic improvement of rice and food security in West Africa, Magloire Oteyami
- Climate variability impacts on rice production in the Philippines, Malte F. Stuecker, Michelle Tigchelaar, Michael B. Kantar
- Flooding - Drought: © IRRI
- Wheat breeding for bread at RAGT 2n: ©RAGT
- Digital forage Corn data collection at Caussade Semences: © Doriane © Caussade Semences Group