Yield Trial Data Research Articles

SCREENING OF DIFFERENT WHEAT VARIETIES UNDER RAINFED CONDITIONS

Triticum Aestivum L. is a major cereal crop grown worldwide. Wheat is staple diet for billions of people and animals feed in all over the world. This study used yield trial data from various sources to evaluate wheat cultivars under rainfed conditions. wheat has sown 1st October to 1St December. The study examined 10 wheat cultivars from three locations in one season. Some cultivars yielded consistently high across locations and seasons, while others yielded differently. The analysis also showed that soil type, sowing time, and cropping season weather affected cultivar yield performance. The study emphasizes the importance of choosing wheat cultivars that suit rainfed conditions and sowing time. RCBD design and three replications were used in this experiment. Different parameters were collected such as days to heading, days to maturity, number of tillers, no. of spikelet per spike, plant height, germination percentage, yield g/plot, and disease rust data in rainfed conditions. Randomized Complete Block design and LSD test were used for this experiment. Total 25 wheat genotypes were sown under rainfed conditions. 10 varieties perform better production under rainfed conditions. These varieties like Barani-17, Ehsan-16, Fakhar e Bhakkar, Bhakkar Star and Zincol, Akbar-19, Ujala-16, Inqlab-91, Anaj-17, Attock-19, and Ghazi-19 show better perform under rainfed condition. The findings can help wheat growers, seed companies, and policymakers choose the best rainfed wheat cultivars to boost wheat yields and food security.

Genetic progress in cowpea [Vigna unguiculata (L.) Walp.] stemming from breeding modernization efforts at the International Institute of Tropical Agriculture.

Genetic gain has been proposed as a quantifiable key performance indicator that can be used to monitor breeding programs' effectiveness. The cowpea breeding program at the International Institute of Tropical Agriculture (IITA) has developed and released improved varieties in 70 countries globally. To quantify the genetic changes to grain yield and related traits, we exploited IITA cowpea historical multi-environment trials (METs) advanced yield trial (AYT) data from 2010 to 2022. The genetic gain assessment targeted short duration (SD), medium duration (MD), and late duration (LD) breeding pipelines. A linear mixed model was used to calculate the best linear unbiased estimates (BLUE). Regressed BLUE of grain yield by year of genotype origin depicted realized genetic gain of 22.75 kg/ha/year (2.65%), 7.91 kg/ha/year (0.85%), and 22.82 kg/ha/year (2.51%) for SD, MD, and LD, respectively. No significant gain was realized in 100-seed weight (Hsdwt). We predicted, based on 2022 MET data, that recycling the best genotypes at AYT stage would result in grain yield gain of 37.28 kg/ha/year (SD), 28.00 kg/ha/year (MD), and 34.85 kg/ha/year (LD), and Hsdwt gain of 0.48g/year (SD), 0.68g/year (MD), and 0.55g/year (LD). These results demonstrated a positive genetic gain trend for cowpea, indicating that a yield plateau has not yet been reached and that accelerated gain is expected with the recent integration of genomics in the breeding program. Advances in genomics include the development of the reference genome, genotyping platforms, quantitative trait loci mapping of key traits, and active implementation of molecular breeding.

Predictive power of YREMs and BLUPs for selecting superior genotypes in perennial crops: A black pepper case study

Black pepper (Piper nigrum L.), a highly sought-after spice crop with medicinal properties, requires careful evaluation and selection due to its perennial nature and associated resource requirements. Being a perennial, yield trials across years are feasible and practical in this crop rather than that across locations. However, parameters to assess the yield trial data and/or derive criteria to select superior cultivars with stable performance are lacking in black pepper. In this study, we examined the genotype-by-year interaction (GYI) pattern and its impact on black pepper yield, as well as the selection parameters for identifying stable and high-yielding cultivars. Average Yield Relative Environment Maximum (YREM) and Best Linear Unbiased Predictor (BLUP) emerged as the most effective measures for evaluating cultivar performance, as they accounted for relative yield and stability. Among the evaluated cultivars, OPKM displayed better stability estimates. However, considering high mean yield along with all the stability estimates, the HP 2173 ranked first. Notably, even with single-location multi-year trial data, the single-year YREM and BLUP values showed significant predictive power for future performance which is most preferred in perennial crops. Additionally, the use of multi-year average performance (YREM and BLUP) as quantitative criteria for selecting or rejecting genotypes in future breeding programs proved to be effective.

Significance of yield sustainability to develop climate smart wheat (Triticum aestivum) in India

To ensure food security in India, not only the wheat (Triticum aestivum L.) productivity but yield sustainability is also crucial especially when the production environments are quite diverse. An experiment was conducted to examined multi-environment wheat yield trial data of popular wheat cultivars in two agro-climatically diverse regions i.e. north- western plains zone (NWPZ) and central zone (CZ) under timely-sown (TS) and late-sown (LS) conditions with an aim to differentiate yield and yield sustainability at the level of 4 production environment, 11 prominent locations and 7 crop years. Wheat productivity matched in both zones; yet the level of yield-sustainability was much less in warmer climate of CZ. Yield sustainability was poorest in late-sown wheat of CZ and the drop in sustainability index (SI) was realized in locations, years and genotypes. For grain yield; locations and location-year interaction mattered most in each production environments but the years were crucial only in NWPZ-TS. Results showed that yield sustainability of test sites can not be adjudged by its productivity alone. Location status was associated with the yield sustainability only in NWPZ. Substantial drop in sustainability could be noticed in some crop seasons of CZ but deviations in NWPZ were smaller. In test sites, the causative factors associated with yield-sustainability varied under different production environments. Genotype-year interaction was effective in each situation except CZ-LS where only the crop year variations were supreme. Variations in locations and genotypes mattered most in NWPZ-TS whereas the crop year deviations were impactful in NWPZ-LS and CZ-TS environments. Based on the results; prospect of improvisation and breeding strategy have been suggested to select suitable production sites and the genotypes.

Upcycling rice yield trial data using a weather-driven crop growth model

Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Yp) calculated by a weather-driven crop growth model, called as the “YpCGM method”. We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world’s changing climate.

Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data.

Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G×E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.

Registration of ‘Lightning’ barley

Abstract‘Lightning’ (Reg. no. CV‐374, PI 698654), experimental designation DH130910, is a two‐row facultative barley (Hordeum vulgare L.) released by Oregon Agricultural Experiment Station in 2020. It was bred for fall planting and is well adapted to the U.S. Pacific Northwest and New York State. Because it does not require vernalization for a timely vegetative to reproductive transition, Lightning can be planted in the spring and will flower and mature in a similar time frame as spring growth habit cultivars. Lightning is a doubled haploid produced via anther culture and was advanced through fall‐planted trials from 2013 to 2019. Facultative growth habit was validated in unreplicated trials in multiple years; replicated yield trial data are available for 2020. Lightning was developed with the goal of commercial production as a malting cultivar. It was tested in the American Malting Barley Association (AMBA) Pilot Program, but it is not on the AMBA recommended list, due to unsatisfactory quality when malted using standard protocols. Because Lightning will produce excellent malt with adjustments to malting protocol, it was released as a cultivar based on its potential for malting and based on its yield and test weight advantage over check cultivars ‘Endeavor’ and ‘Wintmalt’. Furthermore, it has broad‐spectrum disease resistance (moderate to high resistance to scald, stripe rust, powdery mildew, net blotch, and Fusarium head blight).

Use of Genotype by Yield*Trait (GYT) Analysis to Select Hard Red Spring Wheat with Elevated Performance for Agronomic and Disease Resistance Traits

Elevating resistance levels to bacterial leaf streak (BLS), caused by Xanthomonas campestris pv. translucens (Jones, Johnson, & Reddy) and Fusarium head blight (FHB) caused by Fusarium graminearum Schwabe (telomorph Gibberella zeae (Schwein.) Petch) are major objectives of several hard red spring wheat (Triticum aestivum L.; HRSW) breeding programs situated in the northern Great Plains of the USA. High grain yield significantly contributes to the success of HRSW cultivars. Other traits, such as disease resistance, are most useful when accompanied by high grain yield in a single genotype. The genotype by yield-trait (GYT) analysis method aids in selecting genotypes with elevated performance levels for traits of consideration in the presence of high grain yield. Replicated yield trial data was subjected to GYT analysis to demonstrate its utility for selecting HRSW genotypes for use as parents and further consideration toward cultivar release. Genotype selections were identified based on overall performance while strong and weak characteristics were also investigated. Several genotypes were selected for potential use as parents while three were chosen for further cultivar release consideration. Performance levels associated with two of the three candidate cultivars were greater than average for all traits considered.

Identification of Virginia‐Type Peanut Genotypes for Water‐Deficit Conditions Based on Early Decrease in Transpiration Rate with Soil Drying

Early decrease in transpiration rate as soil progressively dries allows soil water conservation for sustained crop physiological activity as water deficit continues to increase. This trait is likely to be particularly useful for peanut (Arachis hypogaea L.), which is often grown on sandy soil where water deficit can develop quickly. This study was undertaken to identify peanut genotypes that express this water conservation trait and to determine if it confers a yield advantage. Three approaches were taken. (i) Two populations of peanut were tested in controlled environments during progressive soil drying. Especially high thresholds of soil water content for initiation of decrease in transpiration rate were identified in three lines in each of the breeding populations. (ii) Nine genotypes were identified for field observation of leaf wilting under rain shelters and in the open field. There was a correspondence between the early decrease in transpiration rate with soil drying and delayed wilting. (iii) Yield trial data for three genotypes were examined to identify those lines that consistently had higher yields than the commercial check cultivar ‘Bailey’, particularly at low yield levels commonly associated with drier conditions. When expressing total seed yield in monetary return, these breeding lines had yield values that were consistently superior to Bailey below the US$2000 ha−1 threshold. Based on the three experimental approaches, N12006ol consistently expressed a desired response of early decrease in transpiration rate with soil drying, delayed wilting in the field when soil water deficit developed, and greater yield value than Bailey in low‐yield environments.

Exploring multi-year soybean yield trial data in South Dakota environments

Crop performance test (CPT) is a common practice to evaluate yield performance and adaptability of each cultivar. In this study, we combined 16 years of soybean CPT data, which included six representative locations, three major maturity groups, and over 1000 cultivars, to determine some patterns associated with yield production. As expected, the repeatability for these cultivars in trial over years was very low. Thus, the data processing in this study was focused on descriptive statistics regarding time, location, and seed supplier and several linear model analyses. The results will be presented during the conference.

Heterogeneous Variances in Multi‐Environment Yield Trials for Corn Hybrids

ABSTRACTRecent developments in statistics and computing have enabled much greater levels of complexity in statistical models of multi‐environment yield trial data. One particular feature of interest to breeders is simultaneously modeling heterogeneity of variances among environments and hybrids. Our objective was to estimate the level of heterogeneity of genotype by environment interaction variance and error variance in the Iowa Crop Performance Test for Corn. A Bayesian approach was used to estimate variance components in a hierarchical model that allows for heterogeneous error and genotype‐by‐environment interaction (GEI) variances applied to corn yield data from the Iowa Crop Performance Test performed between 1995 and 2005. An average of 508 hybrids were tested per year with very little overlap between locations and years, which resulted in a very unbalanced data set. We divided the data into 16 subsets to study the effect of variability across locations and years. We found GEI and error variances to be heterogeneous among both environments and genotypes. Our results for corn contrasted previous work on oat (Avena sativa L.) in which very little heterogeneity was found for error variance among cultivars suggesting that different corn (Zea mays L.) hybrids can have different genotype by environment interaction variances and different error variances.

Dissecting genetic and non-genetic sources of long-term yield trend in German official variety trials

Long-term yield trends have genetic and non-genetic components which may be dissected by a linear mixed model with regression terms. Disease-resistance breakdown must be accounted for in the interpretation. Long-term yield trends of crop varieties may be studied to identify a genetic trend component due to breeding efforts and a non-genetic trend component due to advances in agronomic practices. Many such studies have been undertaken, and most of these inspect trends either by plotting means against years and/or by some kind of regression analysis based on such plots. Dissection of genetic and non-genetic trend components is a key challenge in such analyses. In the present paper, we consider mixed models with regression components for identifying different sources of trend. We pay particular attention to the effect of disease breakdown, which is shown to be confounded with long-term genetic and non-genetic trends, causing an over-estimation of genetic trends based on long-term yield trial data. The models are illustrated using German multi-environment trial data on yield, mildew and Septoria leaf blotch susceptibility for winter wheat and yield, mildew and net blotch susceptibility for spring barley.

A Public Program to Evaluate Commercial Soybean Cultivars for Pathogen and Pest Resistance.

The soybean crop is one of the most important crops worldwide, as the seeds are used for both protein meal and vegetable oil. Soybean acreage covers an estimated 6% of the arable land in the world, and since the 1970s, soybean has had the highest percent increase of hectares in production compared to any other major crop. As demand for soybean continues to rise, the production area and worldwide trade are likely to increase. Biotic constraints, such as pathogens, pests, and weeds, can be detrimental to soybean production, causing significant negative impacts to yield. To successfully reduce losses caused by pathogens and pests, various practices such as cultural and seed sanitation techniques, pesticide applications, and deployment of resistance are used. For many years, public institutions have conducted regional yield trials on both private and public sector soybean cultivars. In Illinois, the University of Illinois Variety Testing Program created a public database for growers. Prompted in part by disease reports on cultivars entered into the Variety Testing Program, the Illinois Soybean Association began providing funds in 1998 to obtain additional information from regional trials to benefit growers in the state. The researchers in the Soybean Variety Testing Program conduct replicated field trials and evaluate these plots for agronomic characteristics such as height, lodging, maturity, and yield. In addition to standard yield trial data, protein and oil content are analyzed.

Gaps between farmer and attainable yields across rainfed sunflower growing regions of Argentina

We computed three estimators of attainable yield for each of between 5 and 8 rainfed sunflower-growing regions of Argentina using between 5 and 9 years of data over the 2000–2007 interval. The estimators were based on comparative yield trial (CYT) data for commercial hybrids, on individual commercial field (ICF) data, and on reporting district (RD) yield information. Contrasts between these estimators led us to prefer the attainable (CYT) yield estimator over the other two. Attainable (CYT) yields ranged from 2.21 to 2.83tha−1 across regions. Yield gaps between mean farmer (RD data) and attainable (CYT) yields were computed using best linear unbiased estimator (BLUE) values for both variables obtained using mixed linear models. These gaps were statistically significant (p≤0.05) for all 8 regions and ranged from 0.37 to 1.18tha−1 across regions, for a country average of 0.75tha−1, equivalent to 41% of the mean country yield of 1.85tha−1. We also used CYT data to examine the issue of recurrent, albeit infrequent, reports of unusually high yields. Mean yields for the top decile of comparative yield trial data ranged from 3.2 to 4.2tha−1 across regions, and the highest yields for this decile in any of the years of record ranged from 3.9 to 4.8tha−1 across regions. Individual commercial field yields were available for 5 regions. Gaps between BLUEs for this variable and attainable (CYT) yields were smaller than those between reporting district and attainable (CYT) yields, but were nevertheless significant in all 5 regions. A notable feature of reporting district, individual field, and yield trial data was their variability. At reporting district level within regions, contributions of spatial and temporal variability were roughly similar. The mean relative contribution of the trial effect to non-error variance of the CYT data exceeded 85% across regions, dominating the contributions of genotype and of genotype by trial effects. We conclude that the magnitude of mean farmer/attainable (CYT) yield gaps for this crop in Argentina justifies further research aimed at reducing regional gaps; and that CYT data can be used to generate an appropriate benchmark for attainable yields.

Comparison of shifted multiplicative model, rank correlation, and biplot analysis for clustering winter wheat production environments

Categorization of locations with similar environments helps breeders to efficiently utilize resources and effectively target germplasm. This study was conducted to determine the relationship among winter wheat (Triticum aestivum L.) yield testing locations in South Dakota. Yield trial data containing 14 locations and 38 genotypes from 8 year were analyzed for crossover genotype (G) × environment (E) interactions according to the Azzalini-Cox test. G × E was significant (P < 0.05) and contributed a small proportion of variation over the total phenotypic variation. This suggested that for efficient resource utilization, locations should be clustered. The data were further analyzed using the Shifted Multiplicative Model (SHMM), Spearman’s rank correlation and GGE biplot to group testing locations based on yield. SHMM analysis revealed four major cluster groups in which the first and third had three locations, with four locations in each of the second and fourth groups. Spearman rank correlations between locations within groups were significant and positive. GGE biplot analysis revealed two major mega-environments of winter wheat testing locations in South Dakota. Oelrichs was the best testing location and XH1888 was the highest yielding genotype. SHMM, rank correlation and GGE biplot analyses showed that the locations of Martin and Winner in the second group and Highmore, Oelrichs and Wall in the third group were similar. This indicated that the number of testing locations could be reduced without much loss of grain yield information. GGE biplot provided additional information on the performance of entries and locations. SHMM clustered locations with reduced cross-over interaction of genotype × location. The combined methods used in this study provided valuable information on categorization of locations with similar environments for efficient resource allocation. This information should facilitate efficient targeting of breeding and testing efforts, especially in large breeding programs.

CIMMYT's use of synthetic hexaploid wheat in breeding for adaptation to rainfed environments globally.

The International Maize and Wheat Improvement Center (CIMMYT) has had significant impact on wheat production in rainfed regions of the developing world. During the last decade, yield potential has increased in drought-prone areas partly due to the use of synthetic hexaploid wheat (SHW), produced through interspecific hybridisation of Triticum turgidum spp. and Aegilops tauschii, followed by chromosome doubling. The objectives of this study were to document the use of SHW in wheat breeding at CIMMYT and quantify its potential effect on global wheat adaptation. The first SHW-derived lines targetted at rainfed conditions appeared in the 5th Semi-Arid Wheat Yield Trial (SAWYT) representing 8% of the lines, increasing to 46% by the 15th SAWYT. During the same period the average coefficient of parentage of SHW in all synthetically derived crosses decreased from 75 to 19%. Average yield rank of genotypes across locations and years was used as a performance indicator of the SHW-derived lines in SAWYT 5–12. In the 5th SAWYT the average rank of the SHW-derived lines was 30 (out of 50) increasing to 25 by the 12th SAWYT. SAWYT 11 was the first trial to include SHW-derived lines bred exclusively for rainfed environments, using directed selection for drought tolerance. International trial data from SAWYT 11 and 12 showed that the SHW-derived line Vorobey was a top-performing line. Vorobey performed well across all environments compared with the best locally adapted check cultivar at each location; trial means ranged from 1 to 8 t/ha. To further exploit genetic diversity for adaptation to drought, SHW has been produced using emmer wheat (T. turgidum L. subsp. dicoccon) as the tetraploid parent. Yield trial data from Mexico show that SHW derivatives based on emmer wheat improved yield performance under drought compared with their drought-tolerant recurrent parents. The use of SHW in wheat breeding for rainfed environments at CIMMYT has increased significantly over the past 10–15 years and the performance and effect of the derived lines have improved with time.

Environmental classification of maize-testing sites in the SADC region and its implication for collaborative maize breeding strategies in the subcontinent

When evaluating genotypes, it is efficient and resourceful to identify similar testing sites and group them according to similarity. Grouping sites ensures that breeders choose as many variable sites as possible to capture the effects of genotype-by-environment (GE) interactions. In order to exploit these interactions and increase testing efficiency and variety selection, it is necessary to group similar environments or mega-environments. The present mega-environments in the Southern African Development Community (SADC) countries are confounded within each country, which limits the exchange of germplasm among them. The objective of this study was to revise and group similar maize-testing sites across the SADC countries that are not confounded within each country. The study was based on 3 years (1999–2001) of regional maize yield trial data and geographical information systems (GIS) parameters from 94 sites. Sequential retrospective (Seqret) pattern analysis methodology was used to stratify testing sites and group them according to their similarity and dissimilarity based on mean grain yield. The methodology used historical data, taking into account imbalances of data caused by changes over locations and years, such as additions and omission of genotypes and locations. Cluster analysis grouped regional trial sites into seven mega-environments, mainly distinguished by GIS parameters related to rainfall, temperature, soil pH, and soil nitrogen with an overall R2 = 0.70. This analysis provides a challenge and an opportunity to develop and deploy maize germplasm in the SADC region faster and more effectively.

Is research on marginal lands catching up? The case of unfavourable wheat growing environments⋆

AbstractIt is widely believed that the Green Revolution had very little effect in unfavourable or marginal environments. Many researchers have been concerned about the slow progress of technical change in marginal environments and the level of research resources allocated to these areas. This paper provides empirical evidence that there has been significant growth in wheat yield potential in marginal environments, especially during the post‐Green Revolution. International yield trial data, covering all major wheat growing environments from 1964 to 1999, were used in estimating the growth in wheat yield potential and changes in yield variability. The global database on wheat varietal adoption and yield gains based on estimated wheat yield growth rates were used to determine production increases due to wheat breeding research. Results show greater progress in shifting the wheat yield frontier in marginal areas, particularly in drought and high temperature environments. Furthermore, yield variability in marginal environments has notably declined, while it has increased slightly in favourable environments. While initial gains came from crossover of varieties from favourable environments, targeted breeding efforts have contributed significantly to more recent productivity growth in marginal environments. Increased production from marginal environments accounted for around 25% of the total wheat production increase in 1997. These findings show greater progress in wheat research and the huge potential of improving wheat productivity in unfavourable environments.

Is research on marginal lands catching up?: The case of unfavourable wheat growing environments

It is widely believed that the Green Revolution had very little effect in unfavourable or marginal environments. Many researchers have been concerned about the slow progress of technical change in marginal environments and the level of research resources allocated to these areas. This paper provides empirical evidence that there has been significant growth in wheat yield potential in marginal environments, especially during the post-Green Revolution. International yield trial data, covering all major wheat growing environments from 1964 to 1999, were used in estimating the growth in wheat yield potential and changes in yield variability. The global database on wheat varietal adoption and yield gains based on estimated wheat yield growth rates were used to determine production increases due to wheat breeding research. Results show greater progress in shifting the wheat yield frontier in marginal areas, particularly in drought and high temperature environments. Furthermore, yield variability in marginal environments has notably declined, while it has increased slightly in favourable environments. While initial gains came from crossover of varieties from favourable environments, targeted breeding efforts have contributed significantly to more recent productivity growth in marginal environments. Increased production from marginal environments accounted for around 25% of the total wheat production increase in 1997. These findings show greater progress in wheat research and the huge potential of improving wheat productivity in unfavourable environments.

Regression models for lentil seed and straw yields in Near East

Lentil ( Lens culinaris Medikus subsp. culinaris) is traditionally grown as a rainfed crop globally, except in Sudan and Egypt, where it is grown under supplemental irrigation. Its seed is a rich source of protein for human consumption, and the straw is a valued animal feed in Near East. In the Mediterranean environment, lentil productivity is limited primarily by the amount and distribution of rainfall and temperature extremes. This study aimed to develop simple models to predict lentil seed and straw yields as a function of seasonal weather data (rainfall and temperature). Historical weather and yield trial data over 39 environments from three contrasting locations in Syria and Lebanon were used. Best-fit models for seed and straw yields in terms of climatic variables based on stepwise regression were developed. These accounted for over 77% variance in yield for both models. The overall responses to total seasonal rainfall were 5.16 kg ha −1 mm −1 seed yield and 10.7 kg ha −1 mm −1 for straw yield. Although May (period of pod-filling) rain contributed substantially to seed yield, high May temperatures reduced yield drastically. For straw yield, absolute minimum temperature and February temperature are important factors, in addition to total seasonal rainfall. The simple regression models developed from historical data not only describe the variation in lentil yields but can also be used for yield prediction purposes in the Near East.