Identification and selection of superior dryland Lentil (Lens culinaris Medik) genotypes using boundary lines in Iran cold areas
Bibliographic record
Abstract
IntroductionPulse crops play important role in supplying human food needs. Lentil (Lens culinaris Medik.) is probably the oldest grain legume to be domesticated and one of the most important pulse crops worldwide due its nutritional characteristics. Lentil is an integral part of dryland agriculture, mainly because of its ability to thrive comparatively well under water-limiting environments. As a result, the crop which provides protein-rich food and animal feed is largely grown under rainfed conditions. The major lentil producing countries are India, Canada, Turkey, USA, Nepal, Australia, Syria, China, Bangladesh, Iran, etc. Iran ranked fourth in area after India, Turkey and Canada and ninth in the production. The aim of any breeding program working for unpredictable and rainfed environments is to develop varieties with high and stable yields. Breeders take advantage of the selection for several traits to achieve maximum economic yield. The selection of genotypes based on indices using yield components was used by breeders for a long time. Breeders believe that obtaining a linear function for measurable traits could lead the selection of genotypes with better genetic values, but a need to include economic value and weights in this function express by many researchers. Simultaneous selection using characteristics with important and heritable economic values is more effective. Crop yield is a function of multiple variables inter-related to each other and can not be defined only by a single-variable equation. One of the most effective of these methods is boundary lines. Feiziasl et al (2003 and 2010) used this method for the first time to determine the optimum levels of plant (dryland wheat) characteristics and yield stability analysis. In this paper, “Boundary Lines” and averaging methods and are used to determine optimum levels for some traits of dryland lentil in cold areas. Materials and Methods More than 8300 data for each trait were collected from national and international trials conducted under rainfed conditions in Maragheh, Zanjan, Ardabil and Shirvan (North Khorasan Province) DARI experiment stations 20 years (1996-2016). The traits considered in the analysis were: days to heading, days to physiological maturity, grain filling period, plant height, 100 seed weight and grain yield. The Excel software was used to develop a scatter diagram showing the relationship between each trait with grain yield in each location. Two methods were used to determine the optimum value for a given characteristic. One is based on the boundary lines method where the maximum grain yield and the optimum value for the trait considered coincide with the crossing point of the two boundary lines. “Boundary Lines” method was used to determine the maximum limits of each crop characteristic. The scatter diagram is surrounded by two regression lines, one on the left and the other on the right called boundary lines. Then Maximum yield is obtained at the intersection of both boundary lines. The other approach, called averaging method, is based on subdividing the data into two groups: high and low yielding groups. Results and DiscussionThe boundary lines method allowed the determination of optimum levels for days to flowering, days to physiological maturity, duration of grain filling, plant height and 100 seed weight which were 55.4 days, 92.6 days, 33.8 days, 27.8 centimeters and 5.4 grams, respectively. By averaging of high yielding group method optimums for days to flowering, days to physiological maturity, duration of grain filling, plant height and 100 seed weight characteristics were 62.0 days, 92.6 days, 30.7 days, 23.7 centimeters, 4.9 grams, respectively. These optimums were closely equivalent to those determined using the averaging method for one days to physiological maturity characteristic. But for other plant characteristics there were a lot of difference and boundary line estimated its amplitude on average 3 times wider than the averaging method. Principle component analysis (PCA) show that, the most important traits for selection of lentil varieties were days to flowering and number of days to physiological maturity. These methods could help breeders to determine the optimum and range for characteristics determining the adaptation of genotypes to given environments. Boundary lines method is more suitable for determining of characteristic’s optimum levels in comparison with the averaging method. ConclusionIt can be concluded that, determining the optimum levels of some dryland lentil characteristics were closely equivalent in two boundary lines and averaging methods. However, in some other characteristics, the optimum levels were a lot of difference in estimating of boundary lines and averaging of high yielding groups. Because boundary lines method is considered the data distribution process and gap data in databank, therefore, its estimates in the optimum levels of the characteristics are more accurate and more realistic than the averaging of the high yielding group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".