Size-Dependent Economic Thresholds for Three Broadleaf Weed Species in Soybeans

1991 ◽  
Vol 5 (3) ◽  
pp. 674-679 ◽  
Author(s):  
Susan E. Weaver
Keyword(s):  
Leaf Area ◽  
Crop Yield ◽  
Soybean Seed ◽  
Yield Losses ◽  
Weed Species ◽  
Damage Functions ◽  
Area Index ◽  
Weed Density ◽  
Size Dependent ◽  
Economic Thresholds

Soybean seed yield losses due to interference from common cocklebur, velvetleaf, and jimsonweed, with and without a PPI application of 0.42 kg ai ha-1metribuzin, were determined in 1986, 1987, and 1988. Damage functions were calculated based on weed density, weed leaf density, and relative weed leaf area index, respectively. Functions relating crop yield losses to weed density varied significantly among treatments and years for each species. Weeds which escaped soil-applied metribuzin were shorter with fewer leaves at 3 wk after planting, and caused lower crop yield losses than control plants at equal densities. Yield loss estimates based upon relative weed leaf area at 3 wk after planting showed least variation between years and treatments.

Canopy Measurements as Predictors of Weed-Crop Competition

Weed Science ◽  
1996 ◽  
Vol 44 (3) ◽  
pp. 511-516 ◽  
Author(s):  
J. I. Vitta ◽  
C. Fernandez Quintanilla
Keyword(s):  
Leaf Area ◽  
Crop Yield ◽  
Weed Management ◽  
Yield Losses ◽  
Weed Density ◽  
Damage Coefficient ◽  
Relative Damage ◽  
Weed Cover ◽  
Better Than ◽  
Crop Competition

The development of weed management systems requires accurate prediction of weed-crop competition. In this paper, simple regression models of crop yield losses based on weed density and weed leaf area are compared. In weed leaf area models, variations in the relative damage coefficient (q) were also analyzed. Finally, three simple methods to assess weed cover were compared: visual, photographic, and optic device assessment. Leaf area models were at least as accurate as weed density models. However, the generality of the leaf area models was restricted by changes in q, according to the date of leaf area evaluation and the year. Although all methods to assess weed cover correlated adequately with weed leaf area, visual estimates were the best to predict crop yield losses perhaps because very low levels of weed leaf area could be distinguished visually better than by other methods.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

Crop Yield Modelling Applying Leaf Area Index Estimated from Sentinel-2 and Proba-V Data at JECAM site in Poland

2018 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Maciej Bartold ◽  
Radoslaw Gurdak ◽  
Martyna Gatkowska ◽  
Wojciech Kiryla ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Area Index ◽  
Sentinel 2

scholarly journals A Plant Process Economic Model for Weed Management Decisions in Irrigated Onion

1999 ◽  
Vol 124 (1) ◽  
pp. 99-105 ◽  
Author(s):  
Claudio M. Dunan ◽  
Philip Westra ◽  
Frank D. Moore
Keyword(s):  
Leaf Area ◽  
Plant Density ◽  
Light Extinction ◽  
Species Number ◽  
Yield Reduction ◽  
Weed Species ◽  
Early Application ◽  
Area Index ◽  
Variable Approach ◽  
Number Of Leaves

A simulation model was built as a decision aid for management of five weed species in direct seeded irrigated onion (Allium cepa L.). The model uses the state variable approach and simulations are driven by temperature and sunlight as photosynthetically active radiation (PAR). It predicts yield reduction caused by competition for PAR according to the ratio of crop leaf area index (LAI) to weed LAI and respective light extinction coefficients (k). Input variables are plant density by species and average number of leaves by species. Number of leaves per plant is used by the model to provide an estimate of initial leaf area per plant. The model calculates initial species LAIs by multiplying species density times average leaf area per plant. The model accurately describes competitive interactions, taking into account respective plant densities, time of emergence, and time of weed removal. It permits economic evaluation of management factors such as handweeding, chemical weed control, herbicide phytotoxicity due to early application, and control of weed flushes during the season. The model is also used to evaluate mechanisms of plant competition for sunlight. In a sensitivity analysis, onion yield loss was more sensitive to weed PAR interception than to PAR use efficiency, the latter a species-dependent constant in the model.

Simulation of Competition for Photosynthetically Active Radiation Between Common Ragweed (Ambrosia artemisiifolia) and Dry Bean (Phaseolus vulgaris)

Weed Science ◽  
1996 ◽  
Vol 44 (3) ◽  
pp. 545-554 ◽  
Author(s):  
David Chikoye ◽  
Leslie A. Hunt ◽  
Clarence J. Swanton
Keyword(s):  
Leaf Area ◽  
Crop Yield ◽  
Photosynthetically Active Radiation ◽  
Growth And Development ◽  
Field Studies ◽  
Weed Competition ◽  
Yield Losses ◽  
Dry Bean ◽  
Common Ragweed ◽  
Active Radiation

The influence of weeds on crop yield is not only dependent on weed-related factors such as density and time of emergence, but also on environmental and management factors that affect both the weed and crop through time. This study was undertaken to develop the first physiologically based dry bean model that would account for the influence of weed competition. The specific objective was to develop a model that would account for the influence of weed competition on crop yield, and to use this model to test the hypothesis that crop yield losses resulted from competition for photosynthetically active radiation (PAR). To this end, a model that simulated the growth and development of dry bean was developed. The model performed daily calculations and simulated the phenology, leaf area expansion, dry matter production and distribution, and grain yield of dry bean based on weather and management information, but assumed adequate water and nutrients. The model was calibrated without weed competition at two locations and yr, and for these situations, adequately described the growth and development of the crop. Simulations were then run for five common ragweed densities and two times of emergence. Common ragweed leaf area was read into the model from input files and used to simulate weed shading. Shading of the dry bean canopy by common ragweed accounted for about 50 to 70% of the yield losses observed in field studies when weeds emerged with the crop. Weed shading did not account for the yield reduction measured from weeds that emerged at the second trifoliate stage of crop growth. The agreement between model predictions and field studies was consistent with the hypothesis that competition for PAR was a principal factor in weed-crop interaction. The ability to account for differences in weed densities, management, and environmental conditions suggested that modeling was a useful tool for evaluating the interaction among weeds and crops.

scholarly journals Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

2020 ◽  
Author(s):  
Francesco Novelli ◽  
Heide Spiegel ◽  
Taru Sandén ◽  
Francesco Vuolo
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Field Data ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index ◽  
Sentinel 2

&lt;p&gt;The work is based on a previously published study with the aim to further analyse the results obtained. Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields.&lt;br&gt;LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. Better RMSE and RRMSE were obtained in 2017 compared to 2016 (RMSE = 0.44 vs. 0.46) (RRMSE = 17% vs. 19%). In 2016 year, a slightly lower R&lt;sup&gt;2&lt;/sup&gt; value was found compared to 2017 (R&lt;sup&gt;2&lt;/sup&gt; = 0.72 vs. 0.89). A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The work shows that the assimilation of remote sensing data into the crop growth model can help to overtake some structural problems of the model.&amp;#160; The assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.&lt;/p&gt;

scholarly journals Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 255 ◽  
Author(s):  
Francesco Novelli ◽  
Heide Spiegel ◽  
Taru Sandén ◽  
Francesco Vuolo
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Field Data ◽  
Crop Growth ◽  
Wheat Crop ◽  
Calibration Data ◽  
Yield Estimation ◽  
Area Index ◽  
Sentinel 2

Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.

Green Foxtail (Setaria viridis) and Pale Smartweed (Polygonum lapathifolium) Interference in Field Crops

1994 ◽  
Vol 8 (2) ◽  
pp. 311-316 ◽  
Author(s):  
John T. O'Donovan
Keyword(s):  
Crop Yield ◽  
Field Experiments ◽  
Dry Weight ◽  
Limiting Factor ◽  
Yield Losses ◽  
Weed Density ◽  
Green Foxtail ◽  
Polygonum Lapathifolium ◽  
And Control ◽  
The Relationship

Field experiments were conducted at Vegreville, Alberta in 1984, 1985, 1986, and 1988 to determine the effects of green foxtail and pale smartweed on yield of wheat, barley, and canola. There was considerable variation among years in the response of crop yield to both weeds and in the relationship between weed dry weight and weed density. Mostly relationships between crop yield and either weed density or dry weight were poor, suggesting that the weeds competed weakly with the crops. Thus density or dry weight may be poor predictors of crop yield losses due to green foxtail or pale smartweed. Where the crops emerged ahead of these weeds, and where soil moisture was not a limiting factor, crop yield losses were minimal and control with herbicides probably uneconomical. In some instances, growth and development of the weeds was suppressed by the crops to the extent that little or no weed dry matter was present at crop maturity. This was most evident with barley, and where the crops emerged ahead of the weeds.

scholarly journals Modelling weed competition and yield losses to study the effect of omission of herbicides in winter wheat.

1990 ◽  
Vol 38 (4) ◽  
pp. 711-718
Author(s):  
L.A.P. Lotz ◽  
M.J. Kropff ◽  
M.W. Groeneveld
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Sandy Soil ◽  
Morphological Characteristics ◽  
Weed Competition ◽  
Yield Losses ◽  
Emergence Time ◽  
Weed Species ◽  
Competition For Light ◽  
Tuber Crops

Omission of application of various herbicides to winter wheat cv. Arminda, cv. Citadel, cv. Okapi, cv. Granada, cv. Sarino and cv. Tombola on clay and sandy soil in 1982-86 caused a significantly lower crop yield only when the densities of certain weed species were extremely high in spring. A dynamic model simulating the competition for light and water between broadleaved weeds and winter wheat was used to assess the observed small effects of weeds on yield in terms of the relative emergence time, physiological and morphological characteristics of weeds. Like the experiments, the simulations indicated that in the Netherlands weeds emerging in spring hardly affect crop yield. Weeds emerging in autumn, however, reduced crop yield considerably if they grew as tall as winter wheat (20% loss at a density of 100 plants/msuperscript 2). The results are discussed in relation to characteristics of crop rotation systems that include root and tuber crops. (Abstract retrieved from CAB Abstracts by CABI’s permission)

scholarly journals Revisiting the concept of the critical period of weed control

2022 ◽  
pp. 1-7
Author(s):  
K. Ramesh ◽  
S. Vijaya Kumar ◽  
P. K. Upadhyay ◽  
B. S. Chauhan
Keyword(s):  
Weed Control ◽  
Crop Yield ◽  
Critical Period ◽  
Management Practice ◽  
Cumulative Effect ◽  
Farming Systems ◽  
Time Interval ◽  
Weed Species ◽  
Weed Density ◽  
Critical Timing

Abstract Weeds are a major biotic constraint to the production of crops. Studies on the critical period of weed control (CPWC) consider the yield loss due to the presence of all weeds present in the crop cycle. The CPWC is the time interval between the critical timing of weed removal (CTWR) and the critical weed-free period (CWFP), and the weed presence before and after the extremes of CTWR and CWFP may not significantly reduce crop yield. The crop yield is taken into consideration and weed density or biomass of individual weeds (annual or perennial) is not so important while calculating the CPWC. Only weed density or biomass is considered for calculating weed control efficiency of a particular management practice for which the weed seed bank is also a criterion. However, weed biomass is the outcome after competition experienced by each weed species with the fellow crop and the weeds. Consequently, the weed pressure in the subsequent season will be the cumulative effect of the preceding season too, which is unaccounted for in CPWC. It is argued that in organic farming or low-input farming systems, where herbicides are not used, the concept of CPWC can be misleading and should be avoided. It is concluded that CTWR is more meaningful than the CPWC.

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