Logistic model analysis of winter wheat growth on China's Loess Plateau

2014 ◽  
Vol 94 (8) ◽  
pp. 1471-1479 ◽  
Author(s):  
Wang Xiangxiang ◽  
Wang Quanjiu ◽  
Fan Jun ◽  
Su Lijun ◽  
Shen Xinlei
Keyword(s):  
Winter Wheat ◽  
Plant Height ◽  
Loess Plateau ◽  
Logistic Model ◽  
Model Analysis ◽  
Above Ground Biomass ◽  
Wheat Growth ◽  
Area Index ◽  
Ground Biomass ◽  
Irrigation Treatments

Xiangxiang, W., Quanjiu, W., Jun, F., Lijun, S. and Xinlei, S. 2014. Logistic model analysis of winter wheat growth on China's Loess Plateau. Can. J. Plant Sci. 94: 1471–1479. The leaf area index (LAI) and above-ground biomass are closely related to crop growth status and yields. Therefore, analysis of their variation and development of a mathematical model for their prediction can provide a theoretical basis for further research. This paper presents a new equation for logistic pattern that calculates above-ground biomass and LAI for different irrigation treatments independent of growing degree days (GDD) and plant height. The model root mean square of error (RMSE) for the LAI was from 0.25 to 1.36, and for above-ground biomass it was from 0.49 to 1.34. The r2 values for the model's output under the single irrigation, double irrigation, triple irrigation, and quadruple irrigation treatments were 0.98, 0.87, 0.96, 0.98 and 0.99, respectively. For above-ground biomass they were 0.96, 0.97, 0.99, 0.97, and 0.97, respectively. The relative error for LAI ranged from 0.026 to 15.2%. For above-ground biomass, the Re ranged from 5.78 to 8.79%. The results gave good agreement between the estimated values and the measured values. The Logistic model was good at estimating the LAI and the above-ground biomass from the plant height.

scholarly journals Correlating the Plant Height of Wheat with Above-Ground Biomass and Crop Yield Using Drone Imagery and Crop Surface Model, A Case Study from Nepal

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 28 ◽  
Author(s):  
Uma Shankar Panday ◽  
Nawaraj Shrestha ◽  
Shashish Maharjan ◽  
Arun Kumar Pratihast ◽  
Shahnawaz ◽  
...  
Keyword(s):  
Food Security ◽  
Plant Height ◽  
Crop Yield ◽  
Earth Observation ◽  
Wheat Crop ◽  
Surface Model ◽  
Observation Data ◽  
Land Resources ◽  
Above Ground Biomass ◽  
Ground Biomass

Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant part of these is already degraded due to overexploitation. In order to get optimum output from the available land resources, it is of prime importance that crops are monitored, analyzed, and mapped at various stages of growth so that the areas having underdeveloped/unhealthy plants can be treated appropriately as and when required. This type of monitoring can be performed using ultra-high-resolution earth observation data like the images captured through unmanned aerial vehicles (UAVs)/drones. The objective of this research is to estimate and analyze the above-ground biomass (AGB) of the wheat crop using a consumer-grade red-green-blue (RGB) camera mounted on a drone. AGB and yield of wheat were estimated from linear regression models involving plant height obtained from crop surface models (CSMs) derived from the images captured by the drone-mounted camera. This study estimated plant height in an integrated setting of UAV-derived images with a Mid-Western Terai topographic setting (67 to 300 m amsl) of Nepal. Plant height estimated from the drone images had an error of 5% to 11.9% with respect to direct field measurement. While R2 of 0.66 was found for AGB, that of 0.73 and 0.70 were found for spike and grain weights respectively. This statistical quality assurance contributes to crop yield estimation, and hence to develop efficient food security strategies using earth observation and geo-information.

scholarly journals Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

2020 ◽  
Vol 12 (19) ◽  
pp. 3121
Author(s):  
Roya Mourad ◽  
Hadi Jaafar ◽  
Martha Anderson ◽  
Feng Gao
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Above Ground Biomass ◽  
Area Index ◽  
Ground Biomass ◽  
Semi Arid ◽  
Sentinel 2

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

Growth and yield response of winter wheat to soil warming and rainfall patterns

2010 ◽  
Vol 148 (5) ◽  
pp. 553-566 ◽  
Author(s):  
R. H. PATIL ◽  
M. LAEGDSMAND ◽  
J. E. OLESEN ◽  
J. R. PORTER
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Rainfall Amount ◽  
Growth And Yield ◽  
Above Ground Biomass ◽  
Soil Warming ◽  
Ground Biomass ◽  
Rainfall Frequency ◽  
Crop Development

SUMMARYIt is predicted that climate change will increase not only seasonal air and soil temperatures in northern Europe but also the variability of rainfall patterns. This may influence temporal soil moisture regimes and the growth and yield of winter wheat. A lysimeter experiment was carried out in 2008/09 with three factors: rainfall amount, rainfall frequency and soil warming (two levels in each factor), on sandy loam soil in Denmark. The soil warming treatment included non-heated as the control and an increase in soil temperature by 5°C at 100 mm depth as heated. The rainfall treatment included the site mean for 1961–90 as the control and the projected monthly mean change for 2071–2100 under the International Panel on Climate Change (IPCC) A2 scenario for the climate change treatment. Projected monthly mean changes in rainfall compared to the reference period 1961–90 show, on average, 31% increase during winter (November–March) and 24% decrease during summer (July–September) with no changes during spring (April–June). The rainfall frequency treatment included mean monthly rainy days for 1961–90 as the control and a reduced frequency treatment with only half the number of rainy days of the control treatment, without altering the monthly mean rainfall amount. Mobile rain-out shelters, automated irrigation system and insulated heating cables were used to impose the treatments.Soil warming hastened crop development during early stages (until stem elongation) and shortened the total crop growing season by 12 days without reducing the period taken for later development stages. Soil warming increased green leaf area index (GLAI) and above-ground biomass during early growth, which was accompanied by an increased amount of nitrogen (N) in plants. However, the plant N concentration and its dilution pattern during later developmental stages followed the same pattern in both heated and control plots. Increased soil moisture deficit was observed only during the period when crop growth was significantly enhanced by soil warming. However, soil warming reduced N concentration in above-ground biomass during the entire growing period, except at harvest, by advancing crop development. Soil warming had no effect on the number of tillers, but reduced ear number and increased 1000 grain weight. This did not affect grain yield and total above-ground biomass compared with control. This suggests that genotypes with a longer vegetative period would probably be better adapted to future warmer conditions. The rainfall pattern treatments imposed in the present study did not influence either soil moisture regimes or performance of winter wheat, though the crop receiving future rainfall amount tended to retain more green leaf area. There was no significant interaction between the soil warming and rainfall treatments on crop growth.

Clover green manure productivity and weed suppression in an organic grain rotation

2016 ◽  
Vol 32 (5) ◽  
pp. 474-483 ◽  
Author(s):  
Katja Koehler-Cole ◽  
James R. Brandle ◽  
Charles A. Francis ◽  
Charles A. Shapiro ◽  
Erin E. Blankenship ◽  
...  
Keyword(s):  
Winter Wheat ◽  
White Clover ◽  
Green Manure ◽  
Red Clover ◽  
Late Summer ◽  
Weed Suppression ◽  
Grain Protein ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Sampling Times

AbstractGreen manure crops must produce high biomass to supply biological N, increase organic matter and control weeds. The objectives of our study were to assess above-ground biomass productivity and weed suppression of clover (Trifolium spp.) green manures in an organic soybean [Glycine max (L.) Merr.]-winter wheat (Triticum aestivum L.)-corn (Zea mays L.) rotation in eastern Nebraska in three cycles (2011–12, 2012–13, 2013–14). Treatments were green manure species [red clover (T. pratense L.) and white clover (T. repens L.)] undersown into winter wheat in March and green manure mowing regime (one late summer mowing or no mowing). We measured wheat productivity and grain protein at wheat harvest, and clover and weed above-ground biomass as dry matter (DM) at wheat harvest, 35 days after wheat harvest, in October and in April before clover termination. Winter wheat grain yields and grain protein were not affected by undersown clovers. DM was higher for red than for white clover at most sampling times. Red clover produced between 0.4 and 5.5 Mg ha−1 in the fall and 0.4–5.2 Mg ha−1 in the spring. White clover produced between 0.1 and 2.5 Mg ha−1 in the fall and 0.2–3.1 Mg ha−1 in the spring. Weed DM was lower under red clover than under white clover at most sampling times. In the spring, weed DM ranged from 0.0 to 0.6 Mg ha−1 under red clover and from 0.0 to 3.1 Mg ha−1 under white clover. Mowing did not consistently affect clover or weed DM. For organic growers in eastern Nebraska, red clover undersown into winter wheat can be a productive green manure with good weed suppression potential.

scholarly journals Effectiveness of Chlorophyll Meter Measurement in Winter Wheat at Field Scale Level

2014 ◽  
Vol 60 (2) ◽  
pp. 41-49 ◽  
Author(s):  
Vojtěch Lukas ◽  
Fernando Rodriguez-Moreno ◽  
Tamara Dryšlová ◽  
Lubomír Neudert
Keyword(s):  
Winter Wheat ◽  
Plant Height ◽  
Growth Parameters ◽  
Nitrogen Concentration ◽  
Chlorophyll Concentration ◽  
Nutrition Status ◽  
Sampling Point ◽  
Area Index ◽  
Chlorophyll Meter ◽  
Nutritional Deficit

Abstract This paper examines the relationship among chlorophyll meter Yara N-Tester readings, nutrition status and growth parameters (leaf area index (LAI), plant height) of the winter wheat plants. Data used in this study were collected in 2010 from two fields located in the Czech Republic (area 52 and 38 ha) from different farms, both with uniform and conventional crop management. The monitoring of crop stands was done at growth stage BBCH 30 in a regular sampling grid with 150 m distance between points (27 and 18 points). At each sampling point, the plant height, LAI (Delta-T SunScan) and the chlorophyll concentration (Yara N-Tester) were recorded. Plant samples were taken to analyse the content of main nutrients (N, P, K, Mg, Ca and S). The results of plant analysis showed that both fields were in different nutrition status: one in a correct status and another had a complex nutritional deficit (K, Ca and N). Linear regressions and ANOVA proved that under a multiple nutritional deficit, N-Tester readings responded to the growth of the crop, while in the adequate nutritional conditions the sensitivity of N-Tester to the variation in the nitrogen concentration is lower. The relationships between crop parameters and chlorophyll meter readings are not generalisable and thus the interpretation of N-Tester results has to be done separately for each field.

scholarly journals Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

2019 ◽  
Vol 11 (11) ◽  
pp. 1261 ◽  
Author(s):  
Yaxiao Niu ◽  
Liyuan Zhang ◽  
Huihui Zhang ◽  
Wenting Han ◽  
Xingshuo Peng
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Plant Height ◽  
Linear Regression Model ◽  
Vegetation Indices ◽  
Point Clouds ◽  
Above Ground Biomass ◽  
Sensing System ◽  
Ground Biomass ◽  
Application Potential

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

scholarly journals Dry Above Ground Biomass for a Soybean Crop Using an Empirical Model in Greece

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 201
Author(s):  
Christos Vamvakoulas ◽  
Stavros Alexandris ◽  
Ioannis Argyrokastritis
Keyword(s):  
Empirical Model ◽  
Above Ground Biomass ◽  
Crop Evapotranspiration ◽  
Area Index ◽  
Soybean Crop ◽  
Ground Biomass ◽  
Cultivation Period ◽  
Proposed Model ◽  
Glycine Max L ◽  
Using Data

A new empirical equation for the estimation of daily dry above ground biomass (D-AGB) for a hybrid of soybean (Glycine max L.) is proposed. This equation requires data for three crop dependent parameters; leaf area index, plant height, and cumulative crop evapotranspiration. Bilinear surface regression analysis was used in order to estimate the factors entering in the empirical model. For the calibration of the proposed model, data yielded from a well-watered soybean crop for the year 2015, in the experimental field (0.1 ha) of the agricultural University of Athens, were used as a reference. Verification of the validity of the model was obtained by using data from a 2014 cultivation period for well-watered soybean cultivation (100% of crop evapotranspiration water treatment), as well as data from three irrigation treatments (75%, 50%, 25% of crop evapotranspiration) for two cultivation periods (2014–2015). The proposed method for the estimation of D-AGB may be proven as a useful tool for estimations without using destructive sampling.

scholarly journals High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR

2018 ◽  
Vol 9 ◽  
Author(s):  
Jose A. Jimenez-Berni ◽  
David M. Deery ◽  
Pablo Rozas-Larraondo ◽  
Anthony (Tony) G. Condon ◽  
Greg J. Rebetzke ◽  
...  
Keyword(s):  
Plant Height ◽  
High Throughput ◽  
Ground Cover ◽  
Above Ground Biomass ◽  
Ground Biomass
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