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 Effects of drip irrigation on spring soybean (Glycine max (L.) Merr.) growth and soil physical indexes of Heilongjiang province of China

2019 ◽  
Vol 48 (1) ◽  
pp. 95-103
Author(s):  
Baoguo Zhu ◽  
Chunfeng Zheng ◽  
Huibin Jia ◽  
Qingying Meng ◽  
Nannan Wang ◽  
...  
Keyword(s):  
Glycine Max ◽  
Physical Properties ◽  
Leaf Area ◽  
Drip Irrigation ◽  
Growth Index ◽  
Soil Physical Properties ◽  
Above Ground Biomass ◽  
Area Index ◽  
Ground Biomass ◽  
Glycine Max L

Effects of different drip irrigation on the growth, development and yield of spring soybean was carried out by measuring the growth index and soil physical characteristics index of spring soybean (Glycine max (L.) Merr.). On the basis of natural precipitation in the same year, 4 drip irrigation levels, namely W1 (0 mm), W2 (200 mm), W3 (400 mm) and W4 (600 mm) were established. From flowering to the pod stage of soybean, a significant increase in the soil moisture and a reduction in the soil hardness and bulk density were observed. Though the difference between W3 and W4 was not significant, drip irrigation affected soil physical properties followed W4 > W3 > W2 > W1. Improved growth index including plant height, above-ground biomass and leaf area of soybean was also observed, but excessive drip irrigation triggered the decline of leaf area index and above-ground biomass. Changes caused in the soil physical properties due to drip irrigation affected soybean growth, which resulted a positive action on yield. Compared with W1 treatments, soybean yield in the different irrigation in the W3, W4 and W2 in the year 2015 and 2016 were increased by 83.68 and 46.99%, 61.58 and 39.47%, 23.51 and 20.21%, respectively. Based on the results of the present experiment it was observed that W3 treatment (irrigation rate 400 mm) was the best one for the improved crop yield of soybean.

scholarly journals Evaluation of the Sensitivity of SMOS L-VOD to Forest Above-Ground Biomass at Global Scale

2020 ◽  
Vol 12 (9) ◽  
pp. 1450
Author(s):  
Arnaud Mialon ◽  
Nemesio J. Rodríguez-Fernández ◽  
Maurizio Santoro ◽  
Sassan Saatchi ◽  
Stéphane Mermoz ◽  
...  
Keyword(s):  
Large Scale ◽  
Correlation Coefficients ◽  
Global Scale ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Northern Latitudes ◽  
Ocean Salinity ◽  
Good Proxy ◽  
Using Data ◽  
The Tropics

The present study evaluates the L band Vegetation Optical Depth (L-VOD) derived from the Soil Moisture and Ocean Salinity (SMOS) satellite to monitor Above Ground Biomass (AGB) at a global scale. Although SMOS L-VOD has been shown to be a good proxy for AGB in Africa and Tropics, little is known about this relationship at large scale. In this study, we further examine this relationship at a global scale using the latest AGB maps from Saatchi et al. and GlobBiomass computed using data acquired during the SMOS period. We show that at a global scale the L-VOD from SMOS is well-correlated with the AGB estimates from Saatchi et al. and GlobBiomass with the Pearson’s correlation coefficients (R) of 0.91 and 0.94 respectively. Although AGB estimates in Africa and the Tropics are well-captured by SMOS L-VOD (R > 0.9), the relationship is less straightforward for the dense forests over the northern latitudes (R = 0.32 and 0.69 with Saatchi et al. and GlobBiomass respectively). This paper gives strong evidence in support of the sensitivity of SMOS L-VOD to AGB estimates at a globale scale, providing an interesting alternative and complement to exisiting sensors for monitoring biomass evolution. These findings can further facilitate research on biomass now that SMOS is providing more than 10 years of data.

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.

scholarly journals Water productivity and yield analysis of groundnut using CropSyst simulation model in hyper arid partially irrigated zone of Rajasthan

2017 ◽  
Vol 40 (04) ◽  
Author(s):  
Sita Ram Jat ◽  
I. J. Gulati ◽  
M. L. Soni ◽  
Amit Kumawat ◽  
N. D. Yadava ◽  
...  
Keyword(s):  
Experimental Data ◽  
Seed Yield ◽  
N Uptake ◽  
Crop Growth ◽  
Growth And Yield ◽  
Weather Data ◽  
Above Ground Biomass ◽  
Area Index ◽  
Ground Biomass ◽  
Daily Weather Data

CropSyst is one of the most important process-oriented simulation models largely used for field crops all over the world to study the effect of climate, soil and management practices on crop productivity. In the present study, we have calibrated and validated the CropSyst model for groundnut crop grown at farmer’s field in IGNP Stage-II of Bikaner. CropSyst model was calibrated using the experimental data of crop parameters, soil profile data and observed daily weather data of experimental site for 2012 and validated the experimental data of crop growth and yield parameters for 2013. The results of the study showed that the CropSyst model simulated the crop growth parameter data viz. green area index, seed yield, above ground biomass and N-uptake of groundnut reasonably well. The seed yield, above ground biomass and N- uptake was validated well by the model with relative error of 3.3, 2.2 and 8.4 %, respectively. The total water applied in groundnut was 728.9 and 619.6 mm in 2012 and 2013, respectively out of this 664.9 and 530.5mm consumed in evapotranspiration.

scholarly journals Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands

2018 ◽  
Vol 10 (9) ◽  
pp. 1355 ◽  
Author(s):  
Luciana Pereira ◽  
Luiz Furtado ◽  
Evlyn Novo ◽  
Sidnei Sant’Anna ◽  
Veraldo Liesenberg ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Floodplain Forest ◽  
Single Frequency ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Area Index ◽  
Ground Biomass ◽  
L Band ◽  
Sar Data

The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.

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.

Leaf Area Index and Above-Ground Biomass of terra firme Rain Forest and Adjacent Clearings in Amazonia

1993 ◽  
Vol 7 (3) ◽  
pp. 310 ◽  
Author(s):  
A.-L. C. McWilliam ◽  
J. M. Roberts ◽  
O. M. R. Cabral ◽  
M. V. B. R. Leitao ◽  
A. C. L. de Costa ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Rain Forest ◽  
Above Ground Biomass ◽  
Terra Firme ◽  
Area Index ◽  
Ground Biomass

scholarly journals (12) Influence of Single and Biculture Cover Crops with or without Synthetic Nitrogen on Leaf Area Index and Above Ground Biomass Yields of Sweet Corn

HortScience ◽  
2006 ◽  
Vol 41 (4) ◽  
pp. 1063B-1063
Author(s):  
Wayne F. Whitehead ◽  
Bharat P. Singh
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Cover Crops ◽  
Cover Crop ◽  
Sweet Corn ◽  
Dry Weight ◽  
Above Ground Biomass ◽  
Hairy Vetch ◽  
Area Index ◽  
Ground Biomass

During the 2004–05 growing season, a study was conducted to determine effect of cover crop, their mixture and fertilizer N rates on above ground biomass (AGB) yields, and Leaf Area Index (LAI) of Bt sweet corn. The following cover crop nitrogen fertility treatments were applied using randomized complete-block design with three replications: 1) fall-0 N, fallow; spring-0 N, 2) fall-0 N, abruzzi rye; spring-0 N, 3) fall-0 N, hairy vetch; spring-0 N, 4) fall-0 N, abruzzi rye+hairy vetch; spring-0 N, 5) fall-0 N, fallow; spring-101 kg N/ha, 6) fall-0 N, abruzzi rye; spring-101 kg N/ha, 7) fall-0 N, hairy vetch; spring-101 kg N/ha, 8) fall-0 N, abruzzi rye+hairy vetch; spring-101 kg N/ha, 9) fall-0 N, fallow; spring-202 kg N/ha, 10) fall-0 N, abruzzi rye; spring-202 kg N/ha, 11) fall-0 N, hairy vetch; spring-202 kg N/ha, and 12) fall-0 N, abruzzi rye+hairy vetch; spring-202 kg N/ha. In Spring 2005, `Attribute BSS0977' bi-color (BC) supersweet (sh2) corn seeds were field planted. AGB yields were collected during harvest week while LAI was recorded at tasseling (6/27), silking (7/8) and one week after harvest (7/25). Hairy vetch; spring-101 kg N/ha produced highest LAI at tasseling (2.18), silking (2.73), and one week after harvest (2.57). Lowest LAI at tasseling (1.12) and silking(1.60) were produced by abruzzi rye; spring-0 N with fallow; spring-0 N producing lowest LAI (1.40) one week after harvest. Maximum AGB fresh (40.5 Mg/ha) and dry weight (12.1 Mg/ha) yields were produced by hairy vetch; spring-101kg N/ha, while minimum AGB fresh (9.6 Mg/ha) and dry weight (3.6 Mg/ha) yields were produced by abruzzi rye; spring-0 N. Results imply LAI at each growth stage and AGB yields of this BCsh2 corn variety are best supported by hairy vetch supplemented with N at 101 kg/ha.

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