A Draft Transcriptome Announcement of Anguina tritici.

Producers wishing to diversify crop production systems from the traditional winter wheat (Triticum aestivum L.)-fallow system of the central Great Plains need information regarding the impact of sunflower (Helianthus annuus L.) on subsequent winter wheat and proso millet (Panicum miliaceum L.) yields. This study was conducted to quantify winter wheat and proso millet yield reductions due to the lower available soil water that exists when sunflower is the prior crop in rotation. Eight crop rotations-including combinations of winter wheat (W), proso millet (M), corn (Zea mays L.) (C), sunflower (Sun), and fallow (F)-were established in 1990 and evaluated for yield, available soil water at planting, and crop water use in 1995, 1996, and 1997. The experiment was conducted at Akron, CO, on a Weld silt loam (fine, smectitic, mesic Aridic Paleustoll). Available soil water at wheat and millet planting was lower where sunflower had been the previous crop than where sunflower was not the previous crop. In dry years, rotations with sunflower as the previous crop had lower wheat and millet water use than other rotations, but averaged over 3 yr, there was no effect of sunflower on wheat or millet water use. Average wheat yield in a W-Sun-F rotation was about 30% lower than wheat yield from W-C-Sun-F, W-M-Sun-F, W-C-F, and W-F. Average millet yield in a M-Sun rotation was 43% lower than millet yield from M-W-C. Wheat yield declined by 178.5 lb/acre (3 bu/acre) for each inch decline in available soil water at planting. Millet yield declined by 295.6 Iblacre for each inch decline in available soil water at planting. In making the decision to include sunflower in crop rotations, producers will have to consider impact on subsequent crop yields, as well as costs of production, market value of crop, impact on pest problems, and total productivity of all crops in the rotation.

Wheat (Triticum aestivum L.) yield in Indo-Gangetic plain of eastern India is much less than its actual potential. Apart from several yield deterministic factors, late sowing of wheat is one of the major reasons for sub-optimal wheat yield. The persistent yield gap poses a threat to future food security of this region with a vast population that is growing rapidly. In the present research, an attempt was made to quantify and classify the yield losses in wheat due to late sowing which is prevalent in this part of India. On-farm participatory agronomic trial was conducted at 1073 plots in 3 districts of eastern India, 2 from Uttar Pradesh and 1 from the state of Bihar. The trial was conducted during four consecutive winter season from 2016–17 to 2019–20. Following a split-plot design, main plots were categorized based on wheat sowing time and sub-plots were classified depending on the wheat varietal class. A sample survey of randomly selected 629 wheat farmers was conducted in 2017–18 wheat season in these 3 districts. Results from the agronomic trial showed that wheat yield decreased by 58 kg/ha for every one-day delay in sowing. Moreover, the yield of long-duration improved wheat variety (HD-2967) was statistically same (P=0.479) compared to the most preferred short-duration variety (PBW-373) in a very late-sown scenario (late December). Farmers’ survey data reconfirmed that the wheat yield has a very strong negative correlation with the sowing dates, but the yield decline was statistically insignificant until mid-November. Wheat yield in this part of India can be adequately boosted if sowing time of wheat advances and adoption of long-duration improved wheat varieties improves.

Growth and Yield Performance of Selected Wheat Genotypes at Variable Irrigation Management

To assess correlation and to find out the direct and indirect effect of yield attributing traits on grain yield, thirty wheat (Triticum aestivum L.) genotypes were experimented at Kamalamai-04, Phant, of Sindhuli district, Nepal. The experiment was laid out in alpha-lattice design with three replications. Thirteen quantitative traits including grain yield of wheat were studied during this study. The grain yield of wheat has significant (P≤0.01) and positive genotypic and phenotypic correlation with number of spikes per meter (0.6**, 0.47**), grains per spike (0.69**, 0.65**), weight of grains per spike (0.69**, 0.61**), thousand kernel weight (0.87**, 0.74**), maturity days (0.5*, 0.47**), above ground mass yield (0.96**, 0.83**) and harvest index (0.93**, 0.64**) of wheat. The genotypic correlation is higher in magnitude than the phenotypic correlation for almost all the studied traits. Path analysis of genotypic correlation showed a high positive direct effect of plant height (0.75), above ground biomass (0.6), spike length (0.43), and harvest index (0.29) on grain yield of wheat. Hence, for increasing yield of wheat in the breeding program, selection and hybridization can be made more effective and accurate by using those a significant positive correlation coefficient and direct effect on the grain yield of wheat.

The growth and yield of spring wheat (Triticum aestivum L.) were examined to determine the actual and potential yields of wheat at a site in the high rainfall zone (HRZ) of south-western Australia. Spring wheat achieved yields of 5.5-5.9 t/ha in 2001 and 2003 when subsurface waterlogging was absent or minimal. These yields were close to the estimated potential, indicating that a high yield potential is achievable. In 2002 when subsurface waterlogging occurred early in the growing season, the yield of spring wheat was 40% lower than the estimated potential. The yield of wheat was significantly correlated with the number of ears per m2 (r2 = 0.81) and dry matter at anthesis (r2 = 0.73). To achieve 5–6 t/ha of yield of wheat in the HRZ, 450–550 ears per m2 and 10–11 t/ha dry matter at anthesis should be targetted. Attaining such a level of dry matter at anthesis did not have a negative effect on dry-matter accumulation during the post-anthesis period. The harvest index (0.36-0.38) of spring wheat was comparable with that in drier parts of south-western Australia, but relatively low given the high rainfall and the long growing season. This relatively low harvest index indicates that the selected cultivar bred for the low- and medium-rainfall zone in this study, when grown in the HRZ, may have genetic limitations in sink capacity arising from the low grain number per ear. We suggest that the yield of wheat in the HRZ may be increased further by increasing the sink capacity by increasing the number of grains per ear.

Core Ideas Importance of factors on wheat yield was tested by partial least squares regression.Nitrogen fertilizer was the most important factor on wheat yield in all four groups.Climate factors, precipitation, and soil nutrients were also major control factors.Partial least squares regression is a useful tool to reveal the control factors on wheat yield. Wheat (Triticum aestivum L.) yield is influenced by many independent factors including precipitation, fertilization, soil nutrients, and crop variety. Due to high correlations of these factors, it is difficult to analyze their relative importance on wheat yield. This study quantified the effects of independent factors on wheat yield and identified the most important control factors through a long‐term experiment on the Loess Plateau, China. The experiment consisted of 17 treatments, including five different levels of N and P fertilizer. Partial least squares regression (PLSR) was used to evaluate the factors on wheat yield in four variety groups‐ Qinmai4 (1985–1986), Changwu131 (1987–1996), Changwu134 (1997–2015), and 31‐yr planting across the three varieties (1985–2015). Variable importance in projection (VIP) value revealed that N fertilizer had the greatest effect on wheat yield in all four groups (VIP = 1.266–2.313). The second most important factors were climate factors for Qinmai4 (VIP = 1.060), precipitation (February, annual, and fallow season) for Changwu131 (W1 = 0.335–0.351, VIP = 1.381–1.474), and soil nutrients (total nitrogen [TN], soil organic matter [SOM], and available potassium [AK]) for Changwu134 (W1 = –0.231–0.514, VIP = 1.084–2.317). When tested across varieties, TN and SOM were the second most important factors for 31‐yr planting (W2 = 0.455 and 0.313; VIP = 1.908 and 1.370, respectively). These results indicate that PLSR can reveal the control factors on wheat yield in the study area and provide a reference tool for analyses in other crops or areas.

Core Ideas Based on yield gap analysis, wheat yield can be improved significantly in China.Both actual and potential wheat yields showed considerable spatial variability.Winter wheat yields increased in 53% of the counties in China.Nearly half of the counties experienced impaired wheat yields.Declining solar radiation and increasing temperature reduce wheat yield. Wheat (Triticum aestivum L.) yield stagnation has been reported in some regions of the world. China is the largest producer of wheat across the globe, but the pattern of its wheat yield stagnation remains poorly addressed. Here, our goal is to examine the temporal trends and spatial patterns of wheat yields along with possible causes based on a comprehensive assessment of winter wheat yield throughout China over the 31‐yr period from 1980 to 2010. Combined with the Agricultural Production Systems Simulator (APSIM) wheat model, we assessed the winter wheat yield gaps and patterns in 1414 counties and at five physiogeographic regional scales across China to ascertain the driving factors of yield variations. Wheat yields increased in 53% of the 1414 counties, but the remaining counties experienced yields that never improved, stagnated, or collapsed from 1980 to 2010. The yield gap analysis showed that actual yields represented only 59% of the national average yield potential, indicating a substantial opportunity to improve winter wheat yields. Relatively larger yield gaps were observed in the northern China Plain (NC, 47%) and in southwestern China (SW, 45%). Although the yield gaps in these regions were accompanied by significantly progressive uptrends of actual yields, our results suggest that agronomic management could be further improved. Moreover, underperforming regions could potentially benefit from new investments and strategies to reliably increase actual yields and reverse trends in stagnation in winter wheat performance.

The effect of previous crops (oilseed, legume, and cereal) on the incidence and severity of crown rot ( Fusarium pseudograminearum , Fp ) and yield of wheat was investigated in 3 field studies in northern New South Wales. The experiments were designed to compare the effectiveness of the Brassica break crops canola ( Brassica napus L.) and mustard ( B. juncea L.) with chickpea ( Cicer arietinum L.) on reduction of Fp in subsequent wheat crops. Responses to previous broadleaf and cereal crops were investigated in Fp -tolerant bread wheat ( Triticum aestivum L.) and Fp -susceptible durum wheat [ Triticum turgidum L. ssp. durum (Dest.)]. In all experiments, broadleaf break crops increased the yield of Fp -susceptible durum wheat compared with durum after cereals (by 0.24–0.89 t/ha). The same response was observed for the Fp -tolerant wheat at 2 of the 3 sites (0.71 and 0.78 t/ha), with a lower yield (0.13 t/ha) after break crops than after cereals at one site during a drought. The yield of the Fp -susceptible durum wheat was generally higher after brassicas than after chickpea (yield advantage 0.27–0.58 t/ha), whereas there was no such difference in the tolerant wheat variety. In most cases, these yield responses to the previous crops were closely related to the severity of Fp infection. Overall yield of susceptible durum wheat was reduced by 1% for each 1% increase in Fp severity at harvest. Residual water and nitrogen (N) did not explain responses to previous crops, although common root rot ( Bipolaris sorokiniana ) may have contributed to some of the responses at the sites. There was little evidence that the lower disease and higher yield following brassicas compared with chickpea was related to suppression of Fp by biofumigation. More plausible explanations are that residual cereal residues decomposed more rapidly under dense Brassica canopies thus reducing Fp inoculum, that Fp severity was increased following chickpea due to higher soil N status, or that brassicas resulted in soil/residue biology that was less conducive to Fp inoculum survival. Evidence for the latter was provided by consistently higher levels of Trichoderma spp. isolated from wheat following brassicas compared with chickpea or cereals. Irrespective of the mechanisms involved, the results demonstrate that Brassica oilseeds provide an effective break crop for crown rot in northern NSW. Furthermore, brassicas may provide an excellent alternative rotation crop to chickpea for high value durum wheat due to an apparent capacity to more effectively reduce the severity of crown rot infection in subsequent crops.

IMPACT OF SOIL STERILIZATION FROM DIFFERENT LOCATIONS, WHEAT SEEDS CLEANING AND THEIR INTERACTIONS ON WEED CONTROL, AND YIELD AND YIELD COMPONENTS

A field experiment was conducted during the winter (rabi) seasons of 2014–2015 and 2015–16 at Kanpur, Uttar Pradesh, to study the effect of weed-management options and nitrogen scheduling on weed dynamics and yield of wheat (Triticum aestivum L.). Common infesting weeds which appeared in wheat field were Phalaris minor Retz. and Cynodon dactylon (L.) Pers. among grasses, and Chenopodium album L., Anagallis arvensis L., Melilotus alba Medik. and Convolvulus arvensis L. as broad-leaf weeds. Moreover, among sedges, only 1 species, Cyperus rotundus L. was observed. Among the weed-management options, mesosulfuron-methyl + Iodosulfuron-methyl sodium (400 g/ha) with higher dose of nitrogen (150 kg N/ha) applied in 3 spilt applications [50% as basal + 25% at crown-root initation (CRI) + 25% at flowering] proved significantly superior in minimizing the density (33%) and biomass of weeds (34%) at 60 days after sowing (DAS). The former treatment increased the leaf-area index (LAI) and SPAD values that elevated the production of wheat grain yield. Among time of nitrogen application, 3 splits (50% as basal + 25% at CRI + 25% at flowering) enhanced the nutrient-uptake efficiency (27.4%) and total nutri ent uptake (40.2%) by crop than 3 equal splits at sowing (basal), CRI and flowering. Higher dose of nitrogen (150 kg N/ha) resulted in higher yield (15.7%) and nutrient uptake (13.4%) than its lower dose. Thus, application of ready-mixed post-emergence herbicides, i.e. mesosulfuron + iodosulfuron (400 g/ha) with higher dose of nitrogen (150 kg N/ha) applied 50% as basal and 25% top-dressed at CRI and 25% at flowering was most effective with re spect to weed-suppression, yield and economics of wheat.

The combined long‐term effects of tillage method and crop rotation on crop yield have not been studied in rainfed systems under Mediterranean climates. A field study was conducted from 1988 to 1994 to determine the effects of tillage (TILL), crop rotation and (ROT) N fertilizer on wheat (Triticum aestivum L.) yield in a rainfed Mediterranean region. Tillage treatments include no tillage (NT) and conventional tillage (CT). Crop rotations were wheat‐sunflower (Helianthus annuus L.) (WS), wheat‐chickpea (Cicer arietinum L.) (WCP), wheatfababean (Vicia faba L.) (WFB), wheat‐fallow (WF), and continuous wheat (CW), with N fertilizer rates of 50, 100, and 150 kg N ha−1. A split‐split plot design with four replications was used. Differences in rainfall during the growing season had a marked effect on wheat yield. Amount of rainfall during the vegetative period for wheat (November‐February) was highly correlated with yield because of the high water‐retention capacity of Vertisols (Typic Haploxerert). In dry years, wheat yield was greater under NT than under CT; the opposite was true in wet years. The TILL ✕ ROT interaction was also significant in the drought years; the wheat yield under NT was greater for CW and the WFB and WF rotations than under CT. Wheat yields ranked by crop rotation were: WFB > WF >> WCP > WS >> CW. Wheat did not respond to N fertilizer when rainfall was below 450 mm during the growing season. Using these results strategies can be developed for establishing the N fertilizer rate applied to wheat as a function of rainfall, the preceding crop, and residual N in soil in order to optimize wheat yield and reduce nitrate pollution to groundwater.

To further increase wheat (Triticum spp.) yield, we need to understand whether it is source or sink limited. Earlier papers suggested that wheat yield is source limited in modern cultivars, including during the grain‐filling stage. Many recent papers support this interpretation, showing that yield is strongly related to environmental conditions that affect source capacity. In contrast to this, other authors working on source–sink manipulations have concluded that wheat yield is only or predominantly sink limited during grain filling. The objective of this forum paper was to examine this contrasting literature and revisit the assumptions and the interpretation of the results. We found that the arguments for a major sink limitation to wheat yield during grain filling arose from a common approach to quantitatively assess the degree of source–sink limitations, based on relativizing the change in grain weight (in response to source–sink manipulations) to the estimated change in source availability per grain. We show that the estimated changes in source availability with source manipulations are often overestimated in the literature, thus underestimating source limitations. Most importantly, we discuss why relativizing the change in grain weight to the estimated change in source availability biases the interpretation of source versus sink limitations. We conclude that wheat yield is mostly source limited during grain filling, and thus strongly dependent on environmental (including agronomic) conditions. A new model to interpret wheat yield limitations is proposed, describing yield as source limited during the whole crop cycle, including during grain filling.

An experiment was conducted during the winter season (rabq 1994-95 and 1995-96 at New Delhi, on integrated nitrogen management in wheat (Triticum aestivum L. emend. Fiori & Paol.) Application of farmyard manure (FYM) increased the growth, yield and water-use efficiency of wheat. Application of IV up to 90 kglha also boosted the growth and yield of wheat. An application of FYM recorded the saving of 45 kg Nlha. Both biofertilizers, azospirillum and azotobacter proved effective in enhancing the growth and yield of wheat.

Although rotational benefits of non-cereal crops have been observed in small plot research trials few quantitative data are available on a field scale. In this study, field data of farmers from the Manitoba Crop Insurance Corporation were analysed to compare yield of wheat (Triticum aestivum L.) following different crops. The yield of wheat following wheat was used as a basis of comparison among crop sequences. The yield data were collected between 1982 and 1993 from fields 64 ha in size, located throughout the province of Manitoba. During this period, the yield of wheat following flax (Linum usitatissimum L.), peas (Pisum sativum L.), and canola (Brassica napus L.), on average was 16%, 11%, and 8% higher, respectively, than wheat following wheat. In one year, the yield of wheat was increased by as much as 41% following a field pea crop. Key words: Crop rotation, barley, canola, flax, field pea, wheat

The study was carried out during the winter (rabi) season of 2021–22 at Punjab Agricultural University, Regional Research Station, Bathinda, Punjab to evaluate the predictability of wheat (Triticum aestivum L.) production using forecast scenarios that were gathered from the India Meteorological Department (IMD). CERES-wheat model was used to estimate crop phenology and wheat yield. The experiment was laid out in a split-plot design (SPD) with three replications. The main plot treatments included five sowing dates, viz. October 25, November 4, November 14, November 24 and December 4 with four sub-plot treatments of variety which were HD 3086, PBW 725, HD 2967 and PBW 658. The model’s output with R2/RMSE for emergence, anthesis and maturity by using actual and forecasted data having 0.38/3.37 and 0.48/2.63, 0.56/4.28 and 0.70/8.48 and 0.71/8.30 and 0.49/2.63, respectively. Moreover, the wheat yield with R2/RMSE values of 0.85/148.31 kg/ha for the actual data and 0.86/140.50 kg/ha for the simulated data showed good agreement between simulated and observed data. Model validation showed that simulated emergence, anthesis and maturity were deviated over observed by 1–3 days, 1–8 days, and 1–20 days, respectively, whereas anthesis, maturity and yield were overestimated. Additionally, the simulated wheat yield differed from the observed yield by 0.5–12%. Phenology and yield were found to have greater RMSE values, wider deviations between simulated and actual values and less connection with delayed sowing. For the wheat growing season (2013–21), rainfall, Tmax and Tmin weather forecast were employed, which were to assess the likelihood of wheat production at various sowing periods. The medium-range weather forecast and the actual weather data closely matched each other for wheat phenology and yield. The annual fluctuation in observed wheat yields as well as treatment-wise variations was more or less effectively reflected by the daily medium-range weather forecast data. The findings of the study are extremely valuable for directing decisions in the study area, figuring out the best time to sow wheat crop, choosing appropriate wheat varieties based on predicted conditions, scheduling irrigation at critical growth stages and applying fertiliser optimally to increase crop productivity and resource efficiency.