Interaction For Grain Yield Research Articles

Effect of sowing time on grain yield, oil content, and fatty acids in rapeseed (Brassica napus subsp. oleifera)

Sowing time is an important factor affecting crop yield and quality, especially in arid and semi-arid regions. The aim of this research was to investigate the effects of sowing time on the growth, yield, and quality of rapeseed genotypes and to determine genotype × sowing time interactions for grain yield and its related traits. The study was carried out using 8 winter rapeseed genotypes (H604049, H604038, H604041, Viking, Elan, Titan, Lorenz, and Trabant) and 4 sowing times (10 October, 20 October, 30 October and 10 November) during the 2005-2006 and 2006-2007 growing seasons. An NIRS system was used to determine oil, protein, glucosinolate and sinapic acid esters. Fatty acid analyses were done by the capillary gas chromatography method. Significant differences were found between sowing times for most of the traits measured. The lowest average seed yield (1027.40 kg ha^{-1}) was obtained from the latest sowing time, whereas the highest average seed yield (2437.50 kg ha^{-1}) was obtained from the earliest sowing time. The genotype H604038 produced the highest seed yield (1988.4 kg ha^{-1}), and it was followed by Trabant (1980.8 kg ha^{-1}) and Titan (1963.8 kg ha^{-1}). The highest oil content, at 42.0%, was obtained from genotype Lorenz at the first sowing time and from genotype Trabant at the second sowing time. No interaction was found between genotype and sowing time for oil content, but significant interactions were found for seed yield and glucosinolates. Seed yield significantly decreased as sowing time was delayed. The effects of sowing time on fatty acid composition were also significant. As a result, it was found that sowing time is an important factor for seed yield and quality in rapeseed.

Analysis of genotype-by-environment interaction for grain yield of rainfed durum wheat genotypes in warm winter areas of Iran

This study was performed for pattern analysis of genotype-by-environment (GE) interaction on 20 durum wheat genotypes grown in 15 testing environments during 2004–06 in Iran. Combined analysis of variance showed significant genotypes (G), environments (E), and GE interactions (P < 0.01), with environmental main effects being the predominant source of variation, followed by GE interaction. The results showed various patterns of genotype responses to different environment groups and assisted in structuring the durum wheat testing locations with identification of two major-environment groups with high genotype discrimination ability. The locations (Gachsaran and Ilam) corresponding to warm and semi-arid aresa were similar in genotype discrimination and showed no association with the other testing locations (Gonbad, Moghan, and Khoramabad) representing the Mediterranean area, indicating they differ in rankings of genotypes. The top-yielding genotypes, G13, G14 and G9, were highly adapted to warm and semi-arid environments, but those corresponding to the Mediterranean area had a high ability to discriminate the genotypes G16, G11, and Saimareh. The stability and adaptability of specific genotypes were assessed by plotting their nominal grain yields at specific environments in an ordination biplot, which aided in the identification of environment groups. Appropriate check genotypes for all environments or for specific environments were also identified. Pattern analysis allowed a sensible and useful summarization of GE interaction data set and helped to facilitate selecting superior genotypes for target-growing sites.

Genotype × Environment Interactions for Grain Yield in Rainfed Winter Wheat Multi‐Environment Trials in Iran

Genotype × environment (G × E) interaction analysis was investigated on grain yield of 20 winter wheat genotypes grown in 24 environments in cold winter areas of Iran during 3 yr. Cluster and biplot analyses were applied to separate testing environments into groups with the same top‐yielding genotypes. Environment followed by G × E interaction effects accounted for the greatest proportion of the variability of grain yield of winter wheat genotypes. Cluster analysis divided both genotypes and environments into three groups, which accounted for 64.4% of the G × E sum of squares (SS). Biplot analysis confirmed the classification analysis and showed that different environment groups tend to discriminate genotype groups in dissimilar fashions. The genotype group G‐I with the highest yielding performance had the best adaptation to environment groups with the highest (E‐I) and the lowest (E‐III) yield potential. Genotype group G‐II was adapted to environment group E‐II with average yield potential, while the genotype group G‐III appeared to have the poorest adaptation to environment groups. The environmental PC1 leads to noncrossover G × E interactions, while PC2 represents a disproportional genotype response across locations, explaining crossover G × E interactions. The testing environments involved in E‐I and E‐III with large PC1 scores and low PC2 scores were the best for genotype discrimination. Results show that the highest yielding genotypes G4, G10, and G17 with the lowest changes in grain yields across environments were the most stable and are consequently good candidates for commercial release in cold winter areas of Iran.

Genotypic adaptation of rice to lowland hydrology in West Africa

In West Africa, the lowlands comprise a wide range of hydrological environments—from permanently flooded to permanently non-flooded conditions. Rice breeding programs must develop genotypes that are expected to perform well either across all hydrological environments or for a specific target population of environments. We evaluated 14 rice ( Oryza spp.) genotypes in seven experiments during 2 years (2007 and 2008) in Benin to investigate genotype × environment (G × E) interaction for grain yield, and to identify high-yielding genotypes and plant characteristics associated with high yield. The genotypes consisted of O. sativa indica genotypes, including ‘aerobic rice genotypes’ and interspecific genotypes, developed from crossing O. sativa and O. glaberrima for upland (‘NERICA’ genotypes) and lowland conditions (‘NERICA-L’). The mean grain yield ranged from 231 to 588 g m −2 across experiments, with the higher grain yields from flooded lowland conditions. The G × E interaction accounted for 22% of the total sum of squares, with environment and genotype responsible for 71 and 8%, respectively. Three environment groups were identified from a pattern analysis on grain yield. Grouping was related to water availability, distinguishing (i) an aerobic environment, with rice grown under aerobic conditions with supplemental irrigation, (ii) a hydromorphic environment, with rice grown under rainfed conditions with drought spells at the vegetative stage, and (iii) a permanently flooded environment. An interspecific genotype WAB1159-4-10-15-1-3 produced high yields in both flooded and hydromorphic environments, while two interspecific genotypes, NERICA-L-6 and NERICA-L-54 performed well only in a flooded environment. An aerobic rice genotype B 6144F-MR-6-0-0 outyielded these three interspecific genotypes in the aerobic environment. High grain yields in the flooded and hydromorphic environments resulted from biomass accumulation rather than harvest index, whereas the higher yield in the aerobic environment was the result of harvest index rather than biomass accumulation. Genotypes with high growth vigor at 42 and 63 days after sowing tended to have higher yields in the flooded and aerobic environments. Grain yield in the hydromorphic environment was positively correlated with growth duration. We conclude that while interspecific breeding appears to offer an effective approach to improving lowland rice productivity, a systematic effort is needed to screen a wide range of O. sativa and interspecific genotypes across hydrology gradients in West Africa to identify genotypes that perform well across or within a specific target population of environments.

Dissection of the genetic basis of heterosis in an elite maize hybrid by QTL mapping in an immortalized F2 population

The genetic basis of heterosis for grain yield and its components was investigated at the single- and two-locus levels using molecular markers with an immortalized F(2) (IF(2)) population, which was developed by pair crosses among recombinant inbred lines (RILs) derived from the elite maize hybrid Yuyu22. Mid-parent heterosis of each cross in the IF(2) population was used to map heterotic quantitative trait loci. A total of 13 heterotic loci (HL) were detected. These included three HL for grain yield, seven for ear length, one for ear row number and two for 100-kernel weight. A total of 143 digenic interactions contributing to mid-parent heterosis were detected at the two-locus level involving all three types of interactions (additive x additive = AA, additive x dominance = AD or DA, dominance x dominance = DD). There were 25 digenic interactions for grain yield, 36 for ear length, 31 for ear row number and 51 for 100-kernel weight. Altogether, dominance effects of HL at the single-locus level as well as AA interactions played an important role in the genetic basis of heterosis for grain yield and its components in Yuyu22.

Effects of reduced height (Rht) and photoperiod insensitivity (Ppd) alleles on yield of wheat in contrasting production systems

Near isogenic lines (NILs) varying for reduced height (Rht) and photoperiod insensitivity (Ppd-D1) alleles in a cv. Mercia background (rht (tall), Rht-B1b, Rht-D1b, Rht-B1c, Rht8c+Ppd-D1a, Rht-D1c, Rht12) were compared for interception of photosynthetically active radiation (PAR), radiation use efficiency (RUE), above-ground biomass (AGB), harvest index (HI), height, weed prevalence, lodging and grain yield, at one field site but within contrasting (‘organic’ vs. ‘conventional’) rotational and agronomic contexts, in each of 3 years. In the final year, further NILs (rht (tall), Rht-B1b, Rht-D1b, Rht-B1c, Rht-B1b+Rht-D1b, Rht-D1b+Rht-B1c) in Maris Huntsman and Maris Widgeon backgrounds were added together with 64 lines of a doubled haploid (DH) population [Savannah (Rht-D1b) × Renesansa (Rht-8c+Ppd-D1a)]. There were highly significant genotype × system interactions for grain yield, mostly because differences were greater in the conventional system than in the organic system. Quadratic fits of NIL grain yield against height were appropriate for both systems when all NILs and years were included. Extreme dwarfing was associated with reduced PAR, RUE, AGB, HI, and increased weed prevalence. Intermediate dwarfing was often associated with improved HI in the conventional system, but not in the organic system. Heights in excess of the optimum for yield were associated particularly with reduced HI and, in the conventional system, lodging. There was no statistical evidence that optimum height for grain yield varied with system although fits peaked at 85 and 96 cm in the conventional and organic systems, respectively. Amongst the DH lines, the marker for Ppd-D1a was associated with earlier flowering, and just in the conventional system also with reduced PAR, AGB and grain yield. The marker for Rht-D1b was associated with reduced height, and again just in the conventional system, with increased HI and grain yield. The marker for Rht8c reduced height, and in the conventional system only, increased HI. When using the System × DH line means as observations grain yield was associated with height and early vegetative growth in the organic system, but not in the conventional system. In the conventional system, PAR interception after anthesis correlated with yield. Savannah was the highest yielding line in the conventional system, producing significantly more grain than several lines that out yielded it in the organic system.

Analysis of Genotype × Environment Interactions for Grain Yield in Durum Wheat

Genotype × environment (GE) interactions are important sources of variation in crop breeding programs. The objectives of this study were (i) to analyze GE interactions on grain yield of one bread wheat (Triticum aestivum L.) and 19 durum wheat [T. turgidum L. subsp. durum (Desf.) Husn.] genotypes by the additive main effects and multiplicative interaction (AMMI) model, and (ii) to evaluate genotype (G), environment (E), and GE interactions with the statistical parameters of AMMI statistic coefficient (D), AMMI stability value (ASV), regression coefficient (b) and ecovalence (W2). Main effects due to E, G, and GE interaction as well as first three interaction principal component axes (IPCA 1 to 3) were found to be significant (P &lt; 0.01). The AMMI biplots distinguished genotypes with wide and specific adaptation and environments with high and low genotype discrimination ability. Ranking the statistical parameters indicated that they are similar in general, especially for the genotypes with similar responses and also for the environments with similar discrimination ability. The results show that the statistical parameters as well as the AMMI model are useful for analyzing GE interaction. Based on the AMMI model, the recommended genotypes had more grain yield improvement in favorable than unfavorable environments. The genotypes selected based on stability of grain yield combined high stability and high‐yielding performance.

Pattern analysis of genotype-by-environment interaction for grain yield in durum wheat

SUMMARYPattern analysis, cluster and ordination techniques, was applied to grain yield data of 20 durum wheat genotypes grown in 19 diversified environments during 2005–07 to identify patterns of genotype (G), environment (E) and genotype-by-environment (G×E) interaction in durum multi-environment trials (METs). Main effects due to E, G and G×E interaction were highly significant, and 0·85 of the total sum of squares (SS) was accounted for by E. Of the remaining SS, the G×E interaction was almost 12 times the contribution of G alone. The knowledge of environmental and genotype classification helped to reveal several patterns of G×E interaction. This was verified by ordination analysis of the G×E interaction matrix. Grouping of environments, based on genotype performance, resulted in the separation of different types of environments. Pattern analysis confirmed the cold and warm mega-environments, and allowed the discrimination and characterization of adaptation of genotypes. However, several patterns of G×E interaction in Iran's regional durum yield trials were further discerned within these mega-environments. The warm environments tended to be closer to one another, suggesting that they discriminate among durum genotypes similarly, whereas cold environments tended to diverge more. The dwarf and early maturing breeding lines from ICARDA with low to medium yields and high contribution to G×E interaction were highly adapted to warm environments, whereas the tall and later maturing genotypes with low to high yields were highly adapted to the cold environments of Iran.

Capacidade combinatória entre híbridos simples de milho em dialelo circulante

Onze híbridos comerciais de milho (AS 1550, DKB 214, DKB 215, DKB 330, Dow 2B150, Dow 8460, P 30F33, P 30F53, P 30P70, Penta e Premium Flex) foram intercruzados, obtendo-se 44 híbridos duplos, segundo um dialelo circulante com p=11 (onze genitores) e s=8 (intercruzados 8 a 8), avaliado em delineamento em blocos casualizados com três repetições. Foram conduzidos dois experimentos na Região Centro-sul do Paraná, nos municípios de Guarapuava e Goioxim. Os dados referentes à produção de grãos, altura média de planta e altura média de inserção de espiga foram submetidos à análise dialélica. Houve diferenças significativas entre os locais e entre os híbridos do dialelo para as três características avaliadas. Também houve efeito significativo da interação locais x híbridos do dialelo para a produção de grãos. Houve diferenças significativas para a capacidade geral de combinação (CGC) em todas as características e para a capacidade específica de combinação (CEC) apenas na produção de grãos. Destacaram-se por sua CGC os híbridos Penta, P 30F53 e Dow 8460 na produção de grãos, o híbrido AS 1550 quanto á menor altura de planta e de inserção da espiga. A melhor combinação híbrida para a produtividade foi Penta x P 30F53, por apresentar a maior média, elevada CEC e ambos os genitores terem as maiores estimativas de CGC. O genitor P 30F53 participou de três dos quatro híbridos duplos mais produtivos. É possível a obtenção de novas populações a partir de cultivares comerciais que se destacaram por sua CGC, e que, nos cruzamentos em que participaram, houve, também, estimativas favoráveis da CEC.

Yield response of indica and tropical japonica genotypes to soil fertility conditions under rainfed uplands in northern Laos

Traditional tropical japonica genotypes ( Oryza sativa) are typically grown without fertilizer for subsistence in slash-and-burn systems by resource-poor farmers in northern Laos. Recent short fallows have reduced soil fertility and rice productivity in these systems. Genotypes adapted to a range of soil fertility conditions are needed for their food security. Rainfed experiments were conducted to identify genotypes with high yield and to examine the nature of genotype by environment (G × E) interaction for grain yield. Two improved indica genotypes and seven traditional genotypes including indica and tropical japonica groups were grown without fertilizer in ten environments over the course of 2 years. The effects of G, E and G × E interaction were significant for grain yield, yield components, plant height and days to flowering. Two improved indica genotypes (B6144F-MR-6-0-0 and IR 55423-01) and a traditional indica genotype Chao Mat out-yielded the other genotypes across the environments (2.1 t ha −1 vs. 1.6 t ha −1). Pattern analysis showed that 40.2% of the G × E sum of squares was captured by a two genotype group by two environment group summary. One environment group had higher contents of soil organic carbon and extractable P and K, and mean grain yield than another environment group. The performance of one genotype group including consisting of improved and traditional indica genotypes was more stable across two environment groups than that of another group consisting of tropical japonica genotypes, which performed well only in the high soil fertility environment group. The low grain yield of tropical japonica genotypes in the low soil fertility environment group was the result of low sink size and grain-filling percentage. In conclusion, two improved genotypes are identified for high yield in both low and high soil fertility conditions and soil organic carbon appears to be a major factor contributing the G × E interaction for grain yield as well as overall productivity.

Assessment of Genotype X Enviroment Interactions for Grain Yield in Maize Hybrids Using Ammi and GGE Biplot Analyses

Seventeen hybrid maize genotypes were evaluated at four locations (Yeniehir- Marmora Region; Bornova-Aegean Region; Ceyhan-Mediterranean Region; Seyhan- Mediterranean Region) in 2005 and 2006 cropping seasons under irrigated conditions in Turkey. The analysis of variance for grain yield of the 17 hybrid genotypes tested in eight environments showed mean squares of environments, genotypes and GEI (genotype x environment interaction) were highly significant and accounted for 62.1%, 12.5% and 25.4% of treatment combination sum of squares, respectively. To determine the effects of GEI on yields, the data were subjected to additive main effects and multiplicative interaction (AMMI) and the GGE biplot analysis. Although mean grain yield of the check cultivar G12 was higher than those of experimental hybrids, difference between G12 and G16, which is the most stable genotype according to AMMI and GGE biplot, was insignificant. It is understood that the experimental hybrid maize G16 can be proposed in reliably for growing by the farmers. Also, it was detected that only the test environment E3 (Ceyhan location) may be sufficient for deciding about which experimental hybrids can be recommended, instead of four test locations (Ceyhan, Seyhan, Bornova and Yeniehir) in this study. In addition, it is concluded that there is no difference between the AMMI and GGE biplot analysis in evaluation of experimental maize hybrids and test environments in this research and that both methods can be used successfully in determining suitable locations for maize hybrids in the environments under Mediterranean climate conditions.

Genotype × environment interactions for grain yield of upland rice backcross lines in diverse hydrological environments

Genotype by environment (G × E) interactions were investigated in Vandana and a subset of 13 BC2 and BC3 lines of an improved indica upland rice cultivar, Vandana, backcrossed with a drought-tolerant traditional japonica cultivar, Moroberekan, which has a thick and extensive root system, in response to eight hydrological field environments conducted at Los Baños, in the Philippines, between 2001 and 2003. The G × E interaction accounted for 13% of the total sum of squares with environment and genotype responsible for 84 and 3%, respectively. Cluster analysis identified four environment and six genotype groups, which accounted for 70% of the G × E sums of squares. Of this, AX1, AX2 and AX3 accounted for 27, 22 and 21% of the G × E-SS, respectively. AX1 represented yield potential; AX2 was related to soil conditions, aerobic status and possibly VPD; and AX3 to change in phenology (days to flowering) with stress. The four environment groups were considered as broadly representative of contrasting rice production environments, including lowland-type, upland-wet season and upland-aerobic environments that experienced vegetative- or anthesis-stage drought stress. Genotype groups differed in adaptation to these diverse environments. For genotype groups G1–G6, G3 (VM150) had stable yields across environments; G1 (VM134) had the greatest grain yield in lowland-type environments (E2); G5 (VM135) in wet season environments (E3); G6 (VM168) in anthesis-stage drought (E4); G2 (Vandana and VM26) in vegetative- and anthesis-stage drought (E1 and E4); G4 had average yields across environments. Implications for breeding of rice adapted to contrasting hydrological environments are discussed, with the caution that adaptation to more than one environment type is desirable, because, as is demonstrated in this paper, an untimely climatic event can transform one environment type into another. Our results suggest that selection in one environment type may not give benefit in other environment types, so testing in more than one environment type is essential.

The Response of Leaf Photosynthesis and Dry Matter Accumulation to Nitrogen Supply in an Older and a Newer Maize Hybrid

Nitrogen use efficiency is higher in newer than in older maize (Zea mays L.) hybrids, but the physiological mechanisms underlying differences in N‐use efficiency are unknown. The objective of this study was to quantify differences between an older and a newer maize hybrid in their response to N availability throughout the life cycle at both the leaf and the whole‐plant level. An older and a newer maize hybrid were grown in a field hydroponic system located near Guelph, ON, in 2005 at a high and a low N level. Leaf carbon exchange rate (CER), chlorophyll index, and the thylakoid electron transport rate (ETR) were measured weekly from 2 wk presilking to 8 wk postsilking. Plant‐component dry matter and N content were determined from 1 wk presilking to maturity. At the leaf level, leaf CER declined during the grain‐filling period, and the decline was greater under low than high N availability. The decline in leaf CER during the grain‐filling period was less in the newer than in the older hybrid under both high and low N availability, and differences in leaf CER were associated most strongly with a reduction in leaf CER per unit absorbed photosynthetic photon flux density. At the whole‐plant level, reduction in grain yield in low vs. high N was greater in the older than in the newer hybrid. The hybrid × N interaction for grain yield was attributable predominantly to a greater decline in the proportion of dry matter allocated to the grain in the older hybrid.

Genotype × environment interaction and yield stability in a cowpea-based cropping system

Twenty-four cowpea genotypes were evaluated under sole cropping or additive series intercropping with sorghum from 2004 to 2005 at four sites representative of the Guinea and Sudan savannah ecologies in Ghana. The aim was to determine whether cowpea breeding programs that emphasize selection under sole-crop conditions have the potential to produce cultivars that are effective under additive series intercropping. Genotype × cropping systems interaction was significant for days to 50% flowering but not for grain yield, biomass and other studied traits. Genotypic yield reaction to cropping systems indicated that bridging the yield gap between sole cropping and intercropping systems is best addressed by agronomic interventions that reduce stress on intercrop cowpea rather than by selecting for specifically adapted genotypes for intercropping. Significant genotype × environment interactions were observed for all traits when data was pooled over cropping systems. Partitioning of the genotype × environment interaction variance indicated that days to 50% flowering was dominated by heterogeneity of genotypic variance, whereas genotype × environment interactions for grain yield and biomass was mainly due to imperfect correlations. Large differences in genotypic yield stability were observed as estimated by the among-environment variance, regression of yield on the environmental index, Kataoka’s index, and by partitioning of genotype × environment interaction sum of squares into components attributable to each genotype. The results suggest that in regions where genotype × environment interaction for yield frequently causes re-ranking across environments, genotypes with the least contribution to the interaction sum of squares are likely to be most productive. On the whole, the results support the contention that breeding under sole-crop conditions has the potential to produce cultivars effective under intercropping conditions.

Yield and grain protein concentration in bread wheat: how to use the negative relationship between the two characters to identify favourable genotypes?

A study of the relationship between grain yield (GY) and grain protein concentration (GPC) in bread wheat was carried out on a 11-year series of trials conducted by the Groupement d’Etude des Varietes Et Semences (GEVES) for the registration of new cultivars on the French National List. Values for GY and GPC came from 458 individual trials, grouped together in 21 series of bi-annual and multi-site data. The correlations between the two characters, calculated environment by environment, appeared highly variable due to high “genotype × environment” interactions for GY and GPC. The use of mean values, calculated on the 21 series of GEVES trials, enabled a better assessment of the relationship between the two characters, and an algorithm was proposed to avoid bias due to potential outliers. Using the well-assessed relationship obtained, grain protein deviations (GPDs) were defined as the standardized residuals of the regression of GPC on GY. These deviations appeared to have a partly genetic basis, as the lines with high deviations were about the same in the two independent datasets constituted by the two consecutive years of GEVES experiments. Some lines used as standards in GEVES trials obtained significant GPD for different series of bi-annual and multi-site trials, confirming the genetic origin of high-GPD. Simulations made to determine the minimum experimental design, showed that at least five sites per year for two consecutive years, were necessary to have a good assessment of the GY–GPC relationship, and hence reliable estimates of GPD.

QTL-based analysis of genotype-by-environment interaction for grain yield of rice in stress and non-stress environments

Use of DNA-based markers can accelerate cultivar development in variable cultivation environments since, in contrast to phenotype, DNA markers are environment-independent. In an effort to elucidate the genetic basis of genotype-by-environment interaction (G × E) for yield of rice (Oryza sativa L.), the associations between 139 AFLP markers and grain yield were determined for rice grown in fresh water (EC of 0.65 dS m−1) and saline conditions (EC of 4–8 dS m−1) with 0 kg ha−1 or 100 kg ha−1 nitrogen fertilizer in the years 2000 and 2001. A population of recombinant inbred lines of rice, developed from an IR29 × Pokkali cross, was used in the study. Both genotype × salinity and genotype × nitrogen level interactions were significant, with the genotype × salinity interaction being stronger. Through multiple regression analysis using a stepwise procedure for selecting markers, 36 markers were detected for grain yield in the four test conditions and of these 28 were detected in only one test condition implying strong environmental specificity for yield QTL expression. However, the fact that eight QTLs were detected in more than one test condition points to the existence of wide-adaptability genes in this cross. Markers with significant associations with yield explained between 37% and 48% of the yield variation in each test condition. Superior genotypes of rice were identified in all four test conditions based on their marker signatures. Furthermore, across N fertilizer regimes, yield predicted from summed additive effects of QTLs were significantly correlated with observed yield in the same year and across years. Thus marker-assisted selection can help breeders overcome the problem of low selection efficiency encountered during phenotypic selection for yield in stress environments.

Genotype-by-environment interactions for grain yield associated with water availability at flowering in rainfed lowland rice

While a large genotype-by-environment (G × E) interaction component of variance for grain yield (GY) has been widely reported for rainfed lowland rice, the reasons for such large interactions are not well known. A random reference population of 34 genotypes taken from the Cambodian rice improvement program was used to examine the magnitude and nature of G × E interactions for GY in Cambodia. These genotypes were evaluated in a multi-environment trial (MET) conducted across 3 years (2000–2002) and eight locations in the rainfed lowlands. The G × E interaction was partitioned into components attributed to genotype-by-location (G × L), genotype-by-year (G × Y) and genotype-by-location-by-year (G × L × Y) interactions. The G × L × Y interaction was the largest component of variance for GY. The G × L interaction was also significant and comparable in size to the genotypic component (G). The significant G component was partly explained by a group of four genotypes that were broadly adapted to different environmental conditions represented by three environmental groups. The three environmental groups were identified from a pattern analysis, and the grouping was partly related to the time of sowing, and hence water availability at flowering. A major factor contributing to the large G × L × Y interactions for GY was late maturing genotypes being affected greatly when soil water availability at flowering was reduced greatly, compared to earlier maturing genotype groups. While the differential genotypic responses to the water availability environment explained part of a large G × E interaction for GY, other non-water related environmental conditions also appeared to have contributed to the interaction. Three target environments were identified for focusing efforts of the breeding programs in Cambodia, and four putative genotypes were selected for their high yield and wide adaptation in the rainfed lowlands.

Crossover Interactions for Grain Yield in Multienvironmental Trials of Winter Wheat

ABSTRACTCrossover interactions (COIs) are changes in ranks among cultivars across environments. Breeders are concerned about COIs because their frequency affects how well rankings from one environment predict rankings in another environment. This research was undertaken to determine the frequency and distribution of COIs for grain yield within years in two regional trials of winter wheat (Triticum aestivum L.). The trials were in Nebraska and in the south‐central USA (SCUS). Each trial had four environments per year, and results from 1998, 1999, and 2000 were considered. Significance of COI for each pair of lines in each pair of environments within years was determined by a t test with an interaction‐wise Type 1 error rate. Grain yield varied significantly across environments in both trials in all years, and in the within‐year analyses the line × environment interaction was always highly significant. In the Nebraska trial, the frequency of COIs was less than expected by chance only in one pair of environments in 1 yr. Nonetheless, because estimates suggested the line × environment × year variance was substantially greater than the line × environment variance, this significant occurrence of COIs did not support breeding for local adaptation. In the SCUS trial, a lower frequency of COIs occurred than in the Nebraska trial. In both trials, frequency of COIs in a pair of environments was not closely related to the difference in mean yield between those environments, which raised the usefulness of categorizing environments as low‐stress or as high‐stress for the purpose of selection.

Pattern analysis on grain yield performance of Chinese and CIMMYT spring wheat cultivars sown in China and CIMMYT

Understanding the relationships among testing environments is essential for better targeting cultivars to production environments. To identify patterns of cultivar, environment, cultivar-by-environment interactions, and opportunities for indirect selection for grain yield, a set of 25 spring wheat cultivars from China and the International Maize and Wheat Improvement Center (CIMMYT) was evaluated in nine environments in China and four management environments at CIMMYT in Cd. Obregon, Mexico, during two wheat seasons. Genetic background and original environment were the main factors influencing grain yield performance of the cultivars. Baviacora M 92, Xinchun 2 and Xinchun 6 showed relatively more stable and higher grain yields, whereas highly photoperiod sensitive cultivars Xinkehan 9, Kefeng 6 and Longmai 19 proved consistently inferior across environments, except in Harbin and Keshan, the two high latitude environments. Longmai 26, also from high latitude environments in the north-eastern Heilongjiang province, was however probably not as photoperiodicly sensitive as other cultivars from that region, and produced much higher grain yield and expressed a broader adaptation. None of the environments reported major diseases. Pattern analyses revealed that photoperiod response and planting option on beds were the two main factors underlying the observed interactions for grain yield. The production environment of planting on the flat in Mexico grouped together with Huhhot and Urumqi in both wheat seasons, indicating an indirect response to selection for grain yield in this CIMMYT managed environment could benefit the two Chinese environments. Both the environment of planting on the flat with Chinese Hejin and Yongning, and the three CIMMYT environments planting on raised beds with Chinese Yongning grouped together only in one season, showing that repeatability may not be stable in this case.

Growth and Yield Response of Facultative Wheat to Winter Sowing, Freezing Sowing and Spring Sowing at Different Seeding Rates

AbstractGrowth and yield of wheat are affected by environmental conditions and can be regulated by sowing time and seeding rate. In this study, three sowing times [winter sowing (first week of September), freezing sowing (last week of October) and spring sowing (last week of April)] at seven seeding rates (325, 375, 425, 475, 525, 575 and 625 seeds m−2) were investigated during the 2002–03 and 2003–04 seasons, in Erzurum (Turkey) dryland conditions, using Kirik facultative wheat. A split‐plot design was used, with sowing times as main plots and seeding rates randomized as subplots. There was a significant year × sowing time interaction for grain yield and kernels per spike. Winter‐sown wheat produced a significantly higher leaf area index, leaf area duration, spikes per square metre, kernel weight and grain yield than freezing‐ and spring‐sown wheat. The optimum time of sowing was winter for the facultative cv. Kirik. Grain yields at freezing and spring sowing were low, which was largely the result of hastened crop development and high temperatures during and after anthesis. Increasing seeding rate up to 525 seeds m−2 increased the spikes per square metre at harvest, resulting in increased grain yield. Seeding rate, however, was not as important as sowing time in maximizing grain yield. Changes in spikes per square metre were the major contributors to the grain‐yield differences observed among sowing times and seeding rates. Yield increases from higher seeding rates were greater at freezing and spring sowing. We recommended that a seeding rate of 525 seeds m−2 be chosen for winter sowing, and 575 seeds m−2 for freezing and spring sowing.