Spring top-dressings of ‘Nitro-Chalk’ and late sprays of a liquid N-fertilizer and a broad spectrum fungicide for consecutive crops of winter wheat at Saxmundham, Suffolk

1978 ◽  
Vol 90 (3) ◽  
pp. 509-516 ◽  
Author(s):  
A. Penny ◽  
F. V. Widdowson ◽  
J. F. Jenkyn
Keyword(s):  
Calcium Carbonate ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Ammonium Nitrate ◽  
Leaf Area ◽  
Broad Spectrum ◽  
N Fertilizer ◽  
N Content ◽  
Area Index ◽  
Liquid Fertilizer

SummaryAn experiment at Saxmundham, Suffolk, during 1974–6, tested late sprays of a liquid N-fertilizer (ammonium nitrate/urea) supplying 50 kg N/ha, and a broad spectrum fungicide (benomyl and maneb with mancozeb) on winter wheat given, 0, 50, 100 or 150 kg N/ha as ‘Nitro-Chalk’ (ammonium nitrate/calcium carbonate) in springMildew (Erysiphe graminisf. sp. tritici) was most severe in 1974. It was increased by N and decreased by the fungicide in both 1974 and 1975, but was negligible in 1976. Septoria (S. nodorum) was very slight in 1974 and none was observed in 1976. It was much more severe in 1975, but was unaffected by the fungicide perhaps because this was applied too late.Yield and N content, number of ears and leaf area index were determined during summer on samples taken from all plots given 100 or 150 kg N/ha in spring; each was larger with 150 than with 100 kg N/ha. The effects of the liquid N-fertilizer on yield and N content varied, but leaf area index was consistently increased. None was affected consistently by the fungicide.Yields, percentages of N in, and amounts of N removed by grain and straw were greatly and consistently increased by each increment of ‘Nitro-Chalk’. Yields of grain were increased (average, 9%) by the liquid fertilizer in 1974 and 1975, and most where most ‘Nitro-Chalk’ had been given, but not in 1976 when the wheat ripened in July; however, both the percentage of N in and the amount of N removed by the grain were increased by the liquid fertilizer each year. The fungicide increased the response to the liquid N-fertilizer in 1974, but not in 1975 when Septoria was not controlled, nor in 1976 when leaf diseases were virtually absent.The weight of 1000 grains was increased by each increment of ‘Nitro-Chalk’ in 1975 but only by the first one (50 kg N/ha) in 1974 and 1976; it was very slightly increased by the liquid fertilizer and by fungicide each year.

scholarly journals Representing winter wheat in the Community Land Model (version 4.5)

2017 ◽  
Vol 10 (5) ◽  
pp. 1873-1888 ◽  
Author(s):  
Yaqiong Lu ◽  
Ian N. Williams ◽  
Justin E. Bagley ◽  
Margaret S. Torn ◽  
Lara M. Kueppers
Keyword(s):  
Heat Flux ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Latent Heat Flux ◽  
Carbon Allocation ◽  
Net Ecosystem Exchange ◽  
Area Index ◽  
Community Land Model ◽  
The Us

Abstract. Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

scholarly journals An optimized non-linear vegetation index for estimating leaf area index in winter wheat

2019 ◽  
Vol 20 (6) ◽  
pp. 1157-1176 ◽  
Author(s):  
Wei Feng ◽  
Yapeng Wu ◽  
Li He ◽  
Xingxu Ren ◽  
Yangyang Wang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Area Index ◽  
Non Linear

scholarly journals Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat

2017 ◽  
Vol 16 (2) ◽  
pp. 266-285 ◽  
Author(s):  
He LI ◽  
Zhong-xin CHEN ◽  
Zhi-wei JIANG ◽  
Wen-bin WU ◽  
Jian-qiang REN ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Area Index

Validate the DSSAT Model on Winter Wheat under Conservation Tillage Treatments in the West Henan, China

2014 ◽  
Vol 522-524 ◽  
pp. 699-708 ◽  
Author(s):  
Xiang Hui Lu ◽  
Hua Bai ◽  
Hui Ying Liu
Keyword(s):  
Water Balance ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Soil Water ◽  
Leaf Area ◽  
Conservation Agriculture ◽  
Soil Water Balance ◽  
The West ◽  
Area Index ◽  
Straw Mulching

Crop growth simulation models can be useful in evaluating the impacts of different tillage and residue management operations on the changes in land productivity and soil-water balance components. They offer a potentially valuable set of tools for examining questions related to performance of conservation agriculture. This can be both to improve our understanding or conceptualization of processes and to improve quantitative predictions for use by agronomists, growers, policy makers or others. We applied the new Decision Support System for Agro-technology Transfer (DSSAT) version 4.5, an improved crop growth simulation model, to three conservation agriculture treatments and one conventional tillage treatment data from a field-scale study in west Henan region of China to predict winter-wheat yield, leaf area index and soil-water balance. The sites average annual precipitation is 632mm and it had a winter wheat-fallow-winter wheat rotation. There winter wheat planting in October and harvesting in next year June. The model was calibrated using 2005-2006 winter-wheat crop data from field experiments of the four treatments. The treatments were: (1) decreased tillage (DT): mulching of 10-15cm height straw and one ploughing operation to 25cm depth on July 1st; (2) zero tillage (ZT): zero tillage with 35-40cm height straw mulching; (3) subsoiling (SS): 35-40cm height straw mulching and subsoil to 40cm depth on July 1st; (4) conventional tillage (CT): 10-15cm height straw mulching and two ploughing operations 20cm deep on July 1st and October 1st. The DSSAT satisfactorily simulated the four treatments variations in winter-wheat yield, leaf area index and soil-water balance. There was better agreement between observed and predicted yields (the error absolute values were less than 3.95% and the error mean absolute values were less than 2.78%). The mean value of root mean square errors (RMSE) for simulated leaf area index (LAI) and soil water storage were 0.41cm2·cm-2 and 0.08cm3·cm-3 for DT, ZT, SS and CT, treatment respectively. The predicted water use efficiency for the four treatments were 15.85, 15.40, 16.58 and 15.81kg·mm-1·ha-1, respectively. These values were close to the values calculated from field measured data (16.82, 14.44, 16.86 and 15.66kg·mm-1·ha-1, respectively). Although the analysis results show us that the DSSAT V4.5 is well suited for simulating winter-wheat growth in the West Henan region of China, these results are preliminary and based on only one year of experimental data and four treatments and further long-term analyses need to be carried out for improving the understanding of the conservation agriculture cropping systems in the west Henan region of China.

Impact of spectral saturation on leaf area index and aboveground biomass estimation of winter wheat

2016 ◽  
Vol 49 (4) ◽  
pp. 241-248 ◽  
Author(s):  
Chao Wang ◽  
Mei-Chen Feng ◽  
Wu-De Yang ◽  
Guang-Wei Ding ◽  
Hui Sun ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Aboveground Biomass ◽  
Biomass Estimation ◽  
Area Index

scholarly journals Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat

2020 ◽  
Vol 12 (15) ◽  
pp. 2378
Author(s):  
Yang Song ◽  
Jinfei Wang ◽  
Jiali Shang ◽  
Chunhua Liao
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Low Cost ◽  
Simple Algorithm ◽  
Yield Potential ◽  
Yield Estimation ◽  
Area Index ◽  
Cloud Data

Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale.

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