Genetic gain is the amount of increase in performance achieved annually through artificial selection. Genetic gain estimation is vital for any crop breeding program to analyze its strengths and weaknesses and to plan future breeding activities. The objective of the study was to assess and monitor yield trends and to estimate the annual rate of increase in yield for breeding lines in the highly variable drought-prone rainfed lowland environments of eastern and southern India. The trends were dissected to quantify the contributions of varietal selection and crop management to the improvement in grain yield. Comparisons draw on yield performance and/or tolerance to different biotic/abiotic stresses, of the released stress-tolerant rice varieties as well as the popular farmer varieties.
Rice is the most important crop in the world feeding more people than any other crop and plays a vital role in the Asian economy. In 1966, the release of IR8, the first semi-dwarf high-yielding modern rice variety from a cross between Peta, a tall Indonesian variety, and Deegeo- woo-gen, a short-statured variety from Taiwan, marked the initiation of the Green Revolution in rice production that over time has transformed the food deficient Asia to a food self-sufficient region.
The Green Revolution had a remarkable impact on rice production, following which rice farming underwent a significant transformation. The improved short-statured varieties (such as Jaya, IR20, IR36, IR42, IR50) developed since then for the irrigated ecosystem had high yield potential, short growth duration, were input responsive, and were disease and insect resistant, however, they lacked improved grain quality.
IR64, released in the Philippines in 1985 had superior grain quality as well as higher yield compared with previously released IR varieties, and its long persistence in farmers’ fields after its release was attributed to the excellent eating quality.
The Green Revolution was based upon the philosophy that selection can be done under optimal environments assuming that an increased yield potential will have a carryover effect under water shortage conditions. However, in the regions chronically affected by drought, that approach failed to improve yield under drought conditions. Thus, by focusing on technologies for very favorable, usually irrigated environments, millions of hectares of land in rainfed areas were not able to fully benefit from yield gains made in irrigated areas.
Rice production systems can be classified into lowland and upland. Rainfed rice fields that are not irrigated but in which the soil is flooded for at least part of the crop cycle are commonly known as rainfed lowlands. Water availability is unpredictable, as the crop is rainfed. Asia has about 46 million hectares of rainfed lowland rice or almost 30 % of the total world rice area.
One-third of South and South East Asian rice lands lie within this ecosystem, which dominates rice areas of Bangladesh, Cambodia, Myanmar, Nepal, and Thailand, and is important in India, Indonesia, Laos, and Vietnam. More than 75 % of the region’s poor rice farmers depend on rainfed agriculture. The uncertain water supply in rainfed lowland areas, together with the infertile soil that can be acidic or saline and the varying crop management methodologies, provide a highly heterogeneous set of breeding targets with a range of environmental conditions that influence the phenotypic response of the genotypes.
This complex and diverse situation very often lies within small geographical regions. Genetic improvement under such complex situations would be a key challenge for any plant breeding program.
In rainfed environments characterized by a lack of water control, drought and flooding are regular problems. Drought is the most significant constraint affecting rice production in rainfed lowland rice. The intensity, duration, and timing of drought may vary from location to location and in a given location from year to year. At the reproductive stage, drought causes a reduction in the number of grains per panicle, increases grain sterility, and reduces grain weight. Drought during the reproductive stage leads to major yield reduction. Even moderate drought stress at the reproductive stage can result in a substantial reduction in grain yield.
Moreover, drought seldom occurs in isolation; it often interacts with other abiotic and biotic stresses such as soil texture, pH, soil fertility, diseases, and insects. With the effect of climate change, the recurrence and intensity of drought are likely to change and possibly become more frequent.
Recurrent drought linked with climate change would have an adverse effect on crop productivity and on millions of poor farmers’ livelihoods. Therefore, drought-prone rice systems require stress-tolerant rice varieties and improved management strategies.
However, breeding for drought resistance has been slow. In addition to drought tolerance being a complex trait, it is reported to be controlled by several genes each with small effects, and significant genotype × environment interactions (G × E) under stress lead to the variable performance of genotypes across locations. Consequently, yield under drought has been reported to have low heritability (H) which complicates the selection of superior, drought-tolerant, stable genotypes. In turn, this limits genetic gain for grain yield under drought.
Genetic gain, although a less commonly used measure of breeding efficiency, is defined as the amount of increase in performance that is achieved annually through artificial selection. Genetic gain estimation is vital for any crop breeding program to analyze its strengths and weaknesses and to plan future breeding activities. In stress environments, improving selection efficiency and increasing genetic gain requires a proper understanding of the target environment, a selection environment that is representative of the target environment, a population with large genetic variance, proper trial management, and appropriate screening procedures.
Genetic gain is usually estimated using multi-environment trials (METs) that are routinely conducted as a part of breeding programs. Traditionally, popular check cultivars are grown in all trials in METs to minimize the influences of location and year. Genetic progress has been estimated from the difference between checks and top-yielding cultivars or by subtracting the trend line fitted to a set of checks common across many years from the trend line fitted to the year-wise means.
However, the choice of a representative check cultivar becomes complicated and with the introduction of new crop genotypes, the check cultivar may become unrepresentative and a new check cultivar has to be chosen. In previous analyses, the genotype yields were often expressed as a percentage of the long-term check yield, assuming that this would minimize G × E, which may not be realistic.
Moreover, data generated from METs aimed at the screening of new cultivars are highly unbalanced due to variations in the genotypes tested from year to year, variations in the number of replicates at each site and variations in the sites that are included from year to year.
In addition, stress-susceptible varieties may contribute to missing values when exposed to severe stress. Therefore, the analytical methods for METs need to be adaptable to unbalanced data. Mixed model analysis is an alternative to the traditional MET analysis based on checks. Missing values, parameter estimation, and prediction of genotype performances are effectively handled by mixed models.
Treating genotypes as random effects in a mixed model allows their genetic value to be predicted using the best linear unbiased predictors (BLUPs). Recently, mixed models have been used to dissect genetic and non-genetic trends in multi-environment trial data ). The contribution of the cultivars (genetic trend) and of the environments (non-genetic trends) to yield improvement over time is quantified by regression coefficients. The genotypes screened in this study are breeding lines developed from crosses involving a high-yielding, but drought-susceptible recipient parent that possesses good grain quality, and a low-yielding but highly drought-tolerant donor parent.
The lines were advanced through direct selection for grain yield under both favorable conditions as well as variable levels of reproductive-stage drought stress in different generations in a pedigree generation advancement breeding program. The selected lines were evaluated for their performance for grain yield under favorable irrigated conditions as well as moderate and severe levels of reproductive stage drought stresses in the years 2005–2014.
The experiments were conducted in the Indian sites in collaboration with the International Rice Research Institute (IRRI) under the Drought Breeding Network of the Stress-Tolerant Rice for Africa and South Asia (STRASA) project.
The STRASA project, supported by the Bill & Melinda Gates Foundation, has helped millions of farmers who produce their crops under predominantly rainfed conditions to achieve significantly higher yields despite abiotic stresses such as drought, flood, cold, salinity, and iron toxicity.
The objective of the present study under STRASA was to assess and monitor yield trends and to estimate the annual rate of increase in yield for breeding lines in the highly variable drought-prone rainfed lowland environments of eastern and southern India. The trends were dissected to quantify the contributions of varietal selection and crop management to the improvement in grain yield. Comparisons draw on yield performance and/or tolerance to different biotic/abiotic stresses, of the released stress-tolerant rice varieties (STRVs) as well as the popular farmer varieties.
This study documents the significant genetic gain for grain yield of a breeding program targeting rainfed lowland rice in India that was based on direct selection for grain yield under both irrigated control and drought conditions. The study utilized extensive multi-season evaluation in target environments under irrigated control, moderate drought stress, and severe drought stress between 2004–2014 with a number of popular varieties as checks to enable accurate estimation of the genetic gain.
The yield improvement of the newly developed STRVs over the best currently grown varieties was also demonstrated on farmers’ fields. The developed STRVs have the potential to protect farmers from crop losses against the increasing impact of extreme droughts under climate change. The results of this study shall assist governments and policymakers to replace the currently grown decades-old varieties from the seed chain and emphasize larger efforts for seed multiplication and dissemination of the newly developed stress-tolerant varieties in coordination with various stakeholders.
The findings of the study shall also encourage researchers to plan for effective evaluation of the breeding=programs products with the aim to assess the success of the breeding programs in terms of genetic gains achieved.
Read the study:
Kumar A, Raman A, Yadav S, et al. (2021) Genetic gain for rice yield in rainfed environments in India. Field Crops Research, Volume 260.