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Inflorescences of the tribe Triticeae, which includes wheat (Triticum sp. L.) and barley (Hordeum vulgare L.) are characterized by sessile spikelets directly borne on the main axis, thus forming a branchless spike. ‘Compositum-Barley’ and tetraploid ‘Miracle-Wheat’ (T. turgidum convar. compositum (L.f.) Filat.) display noncanonical spike-branching in which spikelets are replaced by lateral branch-like structures resembling small-sized secondary spikes. As a result of this branch formation ‘Miracle-Wheat’ produces significantly more grains per spike, leading to higher spike yield. In this study, we first isolated the gene underlying spike-branching in ‘Compositum-Barley,’ i.e., compositum 2 (com2). Moreover, we found that COM2 is orthologous to the branched headt (bht) locus regulating spike branching in tetraploid ‘Miracle-Wheat.’ Both genes possess orthologs with similar functions in maize BRANCHED SILKLESS 1 (BD1) and rice FRIZZY PANICLE/BRANCHED FLORETLESS 1 (FZP/BFL1) encoding AP2/ERF transcription factors. Sequence analysis of the bht locus in a collection of mutant and wild-type tetraploid wheat accessions revealed that a single amino acid substitution in the DNA-binding domain gave rise to the domestication of ‘Miracle-Wheat.’ mRNA in situ hybridization, microarray experiments, and independent qRT-PCR validation analyses revealed that the branch repression pathway in barley is governed through the spike architecture gene Six-rowed spike 4 regulating COM2 expression, while HvIDS1 (barley ortholog of maize INDETERMINATE SPIKELET 1) is a putative downstream target of COM2. These findings presented here provide new insights into the genetic basis of spike architecture in Triticeae, and have disclosed new targets for genetic manipulations aiming at boosting wheat’s yield potential.

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

The study used the DSSAT model to assess potential soybean yields in different regions of India and validated it under diverse agroecological conditions. The average simulated yield under irrigated conditions was 3794 kg ha−1 relative to the simulated average rainfed yield of 2446 kg ha−1, showing a 35.52% reduction in grain yield due to adverse moisture conditions under rainfed conditions. Relative to simulated yield, the average observed (actual) rainfed yield across 43 districts of India was 1025 kg ha−1, which was 2769 and 1421 kg ha−1 lower than irrigated and rainfed potential yield, respectively. A significant positive correlation was observed between simulated water non-limited yield and solar radiation (R2 = 0.55, p ≤ 0.05). The simulated rainfed grain yield (R2 = 0.66, p ≤ 0.05) had a significant, positive, and curvilinear relationship with growing season rainfall. On the other hand, the actual yield (R2 = 0.008) showed a non-significant relationship with mean crop seasonal rainfall across locations. The gap between simulated yield under irrigated and rainfed conditions is huge at locations with low seasonal rainfall and narrows with increasing rainfall. In addition, the gap between actual yield and simulated yield under rainfed conditions was larger, even in high seasonal rainfall areas. The yield gap under rainfed conditions is due to the non-adoption of improved crop management practices and could be reduced with proper interventions. This includes adapting drought-resistant varieties, conserving rainwater, changing land configuration, and adopting waterlogging-tolerant varieties using improved technology to reduce the soybean yield gap.

Newly released sugarcane varieties need to be adapted to various environments. This research was aimed at examining the growth and yield potential of newly released varieties of sugarcane in the first year as plant cane (PC) and the second year as first ratoon cane (RC1) on dry land. The research was carried out at Wedarijaksa station, Trangkil Sugar Mill area, Pati, Central Java in 2019–2021. Four sugarcane varieties were grown using a double rows system, AAS Agribun, ASA Agribun, AMS Agribun, and CMG Agribun and one commercial variety, PSJK 922. Measurements of crop growth were made periodically: yield components at harvest in PC-RC1, and physiological characteristics 5 months after planting. The results indicate that mean tonnes of cane and sugar per hectare between PC and RC1 decreased by 22.7% and 21.0%, respectively, for AAS Agribun, ASA Agribun, and CMG Agribun due to decreased stem weights. AMS Agribun showed the smallest decrease in tonnes of cane (4%) and increase in tonnes of sugar per hectare (2%) from PC to RC1. The highest number of tonnes of sugar in PC was achieved by ASA Agribun (12.8 t ha−1), slightly above PSJK 922 (12.69 t ha−1). The decline in tonnes of cane and sugar needs to be reduced by the continuously improving cultivation techniques. The mean photosynthetic water use efficiency of tested new varieties was 7.46 µmol CO2 mol H2O−1. These research findings provide information on crop performance and can be used as a basis for selecting varieties to be developed in the region. Further studies will be required to test these new sugarcane varieties in a wide range of agroecological zones in Indonesia.

Optical sensor-based N management strategies are promising approaches to improve N-use efficiency (NUE) and reduce environmental pollution risk. The objective of this study was to evaluate an active optical sensor-based in-season N management strategy for winter wheat (Triticum aestivum L.) in the North China Plain (NCP). Initially, 10 field experiments were conducted at four villages in NCP in the 2004/05, 2005/06, and 2006/07 growing seasons to evaluate the in-season N requirement prediction developed by Oklahoma State University. Then the N application rates, winter wheat grain yield, NUE, economic returns, residual N content after harvest and apparent N loss were compared among three different management systems on a total of 16 farmer fields in 2005/2006 and 14 farmer fields in 2006/2007. The systems included a sensor-based system, a soil test-based approach crediting soil residual mineral N (Nmin) to different depth at different growth stages, and common farmer practices. Averaged across site-years, the sensor-based, soil Nmin-based N management strategies, and farmer practices produced similar grain yields but used 67, 88, and 372 kg N ha-1, respectively. Nitrogen-use efficiencies were 61.3, 51.0, and 13.1% for the three methods of N recommendations, correspondingly. Their residual N content in the soil and apparent N loss were 115, 122, and 208 kg N ha-1, and 4, 15, and 205 kg N ha-1, respectively. The optical sensor-based N management strategy is relatively easy to use, has better potential to improve NUE and economic returns, and reduces residual soil N content and apparent N loss than other methods currently used in the NCP.

DSSAT-CANEGRO model have been used to determine crop potential yield over eight districts (viz; Muzaffarnagar, Shahjahanpur, Agra, Lucknow, Basti, Faizabad, Allahabad and Jhansi) representing different agroclimatic conditions & environmentof Uttar Pradesh state in India. The thirty six years (1980-2016) daily weather data of above districts were used to simulate seasonal yield potentials under the various management conditions and compared with the respective district reported yield. The simulated mean potential yield by the CANEGRO model over different district of the state varied between 77.8 t ha-1 in Muzaffarnagar and 97.8 t ha-1 in Agra, while mean reported yield (fresh stalk mass) varied between 40.1 t ha-1 in Jhansi and 62.8 t ha-1 in Muzaffarnagar within the state. Similarly, the attainable yield by the model was simulated lowest of 65.1 t ha-1 in Shahjahanpur and the highest of 73.6 t ha-1 in Faizabad district. The management yield gap was between 9.0 to 30.0 t ha-1 while sowing yield gap was between 7.0 to 26.0 t ha-1 in different districts under study. Further it is not only interesting & surprising but also encouraging to growers that the trends in total yield gap at all the above districts in various agro-climatic zones were found decreasing (narrowed down) at the rate of 138.8 – 801.2 kg ha–1 year–1. Delayed planting by about 30 days in some of the districts resulted into a decrease in sugarcane yield to the tune of 106.7 to 146.7, 103.3 to 143.3 and 80.0 to 133.0 kg ha–1 day–1, respectively. Findings reveal that DSSAT crop simulation model can be an effective tool to aid in decision support system. Yield gap estimates using the past crop data and subsequent adjustment in planting window may help to achieve close to the potential yields.

Nitrogen (N) fertilizer management in the Australian sugar industry is guided by the ‘SIX EASY STEPS’ (6ES) advisory program, for which the potential yield and amount of N that is potentially mineralizable from the soil are key input parameters; the latter is estimated from soil carbon (C) content. Whilst 6ES is not prescriptive about the scale at which it is used to deliver advice to sugarcane growers, common practice is to use the ‘district yield potential’ (DYP) to guide N fertilizer recommendations at the farm and block scales. Analysis of yield variation at the block scale, using sugar mill records over 7 seasons (2009–2015) from the Herbert River district, showed yield to be markedly spatially variable, with the patterns of this variation stable across seasons and crop class. Accordingly, DYP is sub-optimal as an input to 6ES. A block yield potential (BYP), derived from a map of the estimated maximum block-scale yield of first ratoon cane achieved over the 7 seasons, is suggested as a better alternative which can be readily updated as more data become available. Further refinement of the application of 6ES is possible with access to soil C data, derived from either regional soil survey or local soil testing. The present study suggests that use of BYP rather than DYP could lead to a total annual reduction in N applied of approximately 1700 t N over the Herbert River district without negatively impacting yield. Whilst the value of this to growers (A$23/ha) is a minimal proportion of the costs of production, a reduction in the risk of N loss to receiving waters of this magnitude could be of significant benefit to the protection of the Great Barrier Reef. Since data similar to those used here are collected by all sugar mills, similar analyses could be conducted in other sugarcane growing areas. The approach may also be of value in other cropping systems which use central points of delivery (e.g., grain silos).

It is not clear whether the changing climate in Europe will be favourable for crop yield in the future. In this study, we quantified the yield gap for the year 2000 and analyzed the sensitivity of the rain-fed potential yield of winter wheat to changes in temperature, precipitation, and CO2 across Europe. The ecosystem model ANTHRO-BGC was used to simulate potential yields; actual winter wheat yield data together with modelled potential yields were used to calculate yield gap. Artificial climate scenarios for the main climate factors used in sensitivity studies were generated according to climate scenarios from the IPCC 4th Assessment Report (AR4). We found that there is currently a large yield gap in Eastern Europe (around 6 t ha(-1)), whereas in a few developed countries in Western Europe the harvested yield approaches potential yield (around 2 t ha(-1)). Sensitivity analysis indicates that the rain-fed potential yield could increase by about 14% in Europe, under the assumption that the changes in temperature and precipitation will be the same as those projected for 2050 from AR4, and that CO2 will increase from 380 to 550 ppm. This increase in projected potential yield is mainly due to fertilization effects caused by increasing atmospheric CO2 concentrations (15% yield increase), whereas the projected changes in temperature and precipitation will negatively (-1%) affect the rain-fed potential yield in Europe.

Quantitative understanding of factors driving yield increases of major food crops is essential for effective prioritization of research and development. Yet previous estimates had limitations in distinguishing among contributing factors such as changing climate and new agronomic and genetic technologies. Here, we distinguished the separate contribution of these factors to yield advance using an extensive database collected from the largest irrigated maize-production domain in the world located in Nebraska (United States) during the 2005-to-2018 period. We found that 48% of the yield gain was associated with a decadal climate trend, 39% with agronomic improvements, and, by difference, only 13% with improvement in genetic yield potential. The fact that these findings were so different from most previous studies, which gave much-greater weight to genetic yield potential improvement, gives urgency to the need to reevaluate contributions to yield advances for all major food crops to help guide future investments in research and development to achieve sustainable global food security. If genetic progress in yield potential is also slowing in other environments and crops, future crop-yield gains will increasingly rely on improved agronomic practices.

Droughts caused by a lack of precipitation are one of the major factors limiting agricultural crop production. It is thus important to assess the risk of such droughts in order to reduce their effect on agriculture. In the present study, the drought risk for crop production was assessed through an integrated approach that analyzed the relationship between crop yield and drought on the Henan Plain, China. We used the calibrated CERES-Wheat model to simulate 2 levels of wheat yield, the yield potential and the water-limited yield potential, at 66 weather stations. The yield gap between the yield potential and the water-limited yield potential was used as an indicator of the effects of a precipitation deficit on crop production under rain-fed conditions. A strong linear relationship between the yield gap and the amount of precipitation in the growing season was observed for each station during the period 1962−2009. A uniform criterion for drought severity thresholds for the entire Henan Plain was constructed based on the yield gap. For each station, the growing-season precipitation thresholds associated with different drought severities were then calculated based on the linear relationship between the yield gap and the amount of precipitation in the growing season. Drought frequencies derived from changes in the amount of precipitation during the growing season were also examined for all stations and spatially interpolated over the plain. The results showed diverse spatial patterns of frequency with respect to different drought types. Light droughts often occurred in the southern region, and moderate droughts occurred more frequently in the western and eastern regions. Severe drought displayed a generally decreasing trend from north to south.