Identification of Salt Tolerant Chickpea Genotypes Based on Yield and Salinity Indices

Background: Legumes are under explored crops in comparison to staple cereal crops and decreasing agricultural lands along with waste lands and poor water resources are the main constraints for sustainable agricultural production. Chickpea is the third most important food legume, known for its high nutritive values, generally considered as relatively salt sensitive crop. Existence of large genetic variation provides opportunity to explore variations and exploit the available salinity tolerance in chickpea. Methods: A Randomised block design experiment was conducted to explore the salinity tolerance in 10 chickpea genotypes including CSG-8962 (Karnal Chana-1), as salt tolerant check during 2018-19 and 2019-20 under control and salinity ECiw 6 dS/m and ECiw 9 dS/m. The leachate was collected from time to time to monitor the buildup of the desired salinity. At harvesting stage, yield and yield attributing traits were recorded and yield indices were calculated to identify the potential of chickpea genotypes against salinity stress.Result: Saline irrigation water significantly decreased the number of pods/plant by 21.29% under ECiw 6 dS/m and 53.29% under ECiw 9 dS/m. Genotypes ICCV 10, CSG 8962 and DCP 92-3 retained maximum number of filled pods at ECiw 6 dS/m, while under higher salinity of ECiw 9 dS/m, CSG 8962, ICCV 10 and KWR108 had the highest filled pods. Saline water of 6 dS/m caused reduction of 36.1% - 65.0% in grain yield, which further increased to 81.0% - 98.5% with saline water of 9 dS/m. Genotypes S7 and ICCV - 10 had percent grain yield reduction of 36.13% and 41.24% respectively whereas the salt tolerant check had a percent reduction of 46.94% at ECiw 6 dS/m. Based on studied yield indices, genotypes S7, KWR108 and CSG 8962 showed relatively higher tolerance than other studied genotypes, whereas BG 256 and ICC 4463 were the most salt sensitive chickpea genotypes.

Evaluation of the Potential Effects of Drought on Summer Maize Yield in the Western Guanzhong Plain, China

Drought and uneven distribution of precipitation during stages of crop growth exert a severe reduction on crop yield. It is therefore necessary to evaluate the impact of drought on crop yields. In this study, data from a two-year (2016 and 2017) field experiment were used to calibrate and evaluate the parameters of the Decision Support System for the Agrotechnology Transfer (DSSAT) model. The evaluation model was then employed to analyze the impact of potential drought on the yield of summer maize (Zea mays L.) over different growth stages for 46 years (1970–2015). The simulated summer maize flowering and harvest date differed by three and one days of the observed in 2017. The d-index value and the normalized root-mean-square error (nRMSE) of the simulated and measured values were 0.90 and 3.72%, 0.95 and 10.21%, and 0.92 and 13.12%, for summer maize yield, soil water content, and leaf area index, respectively. This indicates that the parameters of the DSSAT model were extremely reliable and that the simulation results were better. The yield reduction of summer maize was concentrated within the range of 0–40% from 1970 to 2015, and the two-stage yield reduction was higher than the one-stage yield reduction. The highest probability of yield reduction occurs if drought occurs during jointing and heading stages. Irrigation is therefore recommended during jointing stage or heading stage. If local irrigation conditions permit, irrigation can be carried out both at the jointing and heading stages. This study provides a theoretical basis for drought resistance management and scientific irrigation of summer maize in the western Guanzhong plain.

Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing

Key message Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. Abstract With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year’s data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

Cotton Stage of Growth Determines Sensitivity to 2,4-D

The anticipated release of EnlistTM cotton, corn, and soybean cultivars likely will increase the use of 2,4-D, raising concerns over potential injury to susceptible cotton. An experiment was conducted at 12 locations over 2013 and 2014 to determine the impact of 2,4-D at rates simulating drift (2 g ae ha−1) and tank contamination (40 g ae ha−1) on cotton during six different growth stages. Growth stages at application included four leaf (4-lf), nine leaf (9-lf), first bloom (FB), FB + 2 wk, FB + 4 wk, and FB + 6 wk. Locations were grouped according to percent yield loss compared to the nontreated check (NTC), with group I having the least yield loss and group III having the most. Epinasty from 2,4-D was more pronounced with applications during vegetative growth stages. Importantly, yield loss did not correlate with visual symptomology, but more closely followed effects on boll number. The contamination rate at 9-lf, FB, or FB + 2 wk had the greatest effect across locations, reducing the number of bolls per plant when compared to the NTC, with no effect when applied at FB + 4 wk or later. A reduction of boll number was not detectable with the drift rate except in group III when applied at the FB stage. Yield was influenced by 2,4-D rate and stage of cotton growth. Over all locations, loss in yield of greater than 20% occurred at 5 of 12 locations when the drift rate was applied between 4-lf and FB + 2 wk (highest impact at FB). For the contamination rate, yield loss was observed at all 12 locations; averaged over these locations yield loss ranged from 7 to 66% across all growth stages. Results suggest the greatest yield impact from 2,4-D occurs between 9-lf and FB + 2 wk, and the level of impact is influenced by 2,4-D rate, crop growth stage, and environmental conditions.