scholarly journals Effect of row width on splash erosion and throughfall in silage maize crops

2017 ◽  
Vol 12 (No. 1) ◽  
pp. 39-50 ◽  
Author(s):  
V. Brant ◽  
P. Zábranský ◽  
M. Škeříková ◽  
J. Pivec ◽  
M. Kroulík ◽  
...  
Keyword(s):  
Stem Elongation ◽  
Field Measurements ◽  
Positive Influence ◽  
Row Spacing ◽  
Growth Stages ◽  
Area Index ◽  
Factors Affecting ◽  
Splash Erosion ◽  
Silage Maize ◽  
Plant Length

Line width is one of the major factors affecting arable soil erosion. The aim of the study was to assess the effects of different row spacing on splash erosion and throughfall in maize crops. Field measurements of the throughfall (P<sub>th</sub>, mm) and splash erosion (MSR, g/m<sup>2</sup>) were carried out in silage maize crops (row spacing 0.45 and 0.75 m) in 2012–2014. The BBCH growth stages for the crops, plant length (L, m), and leaf area index (LAI) were evaluated. Positive correlation was observed between the aerial precipitation (P, mm) and the P<sub>th</sub> values. With increasing P-values, higher levels of P<sub>th</sub> were identified in the 0.75 m compared to the 0.45 m row spacing. The value of this proportion was decreasing from the centre of the inter-row (0.75 m) to the row of the plants direction. Statistically significant lower values of splash erosion were observed in the 0.45 m compared with the 0.75 m wide rows, especially within the years 2012 and 2014. The experiments proved the positive influence of the length of plants and LAI on P/P<sub>th </sub>values. A decrease of P<sub>th </sub> in relation to precipitation values with height of plants and LAI values was observed. This dependency was then confirmed from the beginning of the stem elongation (BBCH 30) to the end of flowering (BBCH 70). Tighter dependency between the plant length (L) and the values of P/P<sub>th</sub> ratio in the 0.75 m wide crop rows was determined. Conversely, a more important influence of LAI on the values of P/P<sub>th</sub> ratio was estimated in the 0.45 m wide crop rows. The experiments proved the positive influence of the 0.45 m wide rows on the decrease of splash erosion as well as throughfall compared with the 0.75 m row spacing.  

scholarly journals Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

2019 ◽  
Vol 11 (15) ◽  
pp. 1763 ◽  
Author(s):  
Songyang Li ◽  
Fei Yuan ◽  
Syed Tahir Ata-UI-Karim ◽  
Hengbiao Zheng ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Digital Images ◽  
Oryza Sativa L ◽  
Accuracy Assessment ◽  
Stem Elongation ◽  
Growth Stages ◽  
Digital Imagery ◽  
Area Index ◽  
Practical Applications ◽  
Color Indices ◽  
Promising Solution

Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.

scholarly journals Machine Learning Approach for Prescriptive Plant Breeding

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kyle A. Parmley ◽  
Race H. Higgins ◽  
Baskar Ganapathysubramanian ◽  
Soumik Sarkar ◽  
Asheesh K. Singh
Keyword(s):  
Machine Learning ◽  
Plant Breeding ◽  
Seed Yield ◽  
Management Practices ◽  
Predictor Variables ◽  
Phenotypic Trait ◽  
Phenotypic Traits ◽  
Row Spacing ◽  
Growth Stages ◽  
Area Index

AbstractWe explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding.

Pitting Corrosion of Biomedical Titanium and Titanium Alloys: A Brief Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Yu-Wei Cui ◽  
Liang-Yu Chen ◽  
Xin-Xin Liu
Keyword(s):  
Corrosion Resistance ◽  
Pitting Corrosion ◽  
Potential Temperature ◽  
Growth Stages ◽  
Factors Affecting ◽  
Ti Alloys ◽  
Pitting Corrosion Resistance ◽  
Superior Mechanical Properties ◽  
Good Biocompatibility ◽  
Main Factors

Abstract:: Thanks to their excellent corrosion resistance, superior mechanical properties and good biocompatibility, titanium (Ti) and Ti alloys are extensively applied in biomedical fields. Pitting corrosion is a critical consideration for the reliability of Ti and Ti alloys used in the human body. Therefore, this article focuses on the pitting corrosion of Ti and Ti alloys, which introduces the growth stages of pitting corrosion and its main influencing factors. Three stages, i.e. (1) breakdown of passive film, (1) metastable pitting, and (3) propagation of pitting, are roughly divided to introduce the pitting corrosion. As reviewed, corrosive environment, applied potential, temperature and alloy compositions are the main factors affecting the pitting corrosion of Ti and Ti alloys. Moreover, the pitting corrosion of different types Ti alloys are also reviewed to correlate the types of Ti alloys and the main factors of pitting corrosion. Roughly speaking, β-type Ti alloys have the best pitting corrosion resistance among the three types of Ti alloys.

scholarly journals Individual and Combined Effects of Planting Date, Seeding Rate, Relative Maturity, and Row Spacing on Soybean Yield

Agronomy ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 605
Author(s):  
Peder K. Schmitz ◽  
Hans J. Kandel
Keyword(s):  
Seed Yield ◽  
Management Practices ◽  
Synergistic Effects ◽  
Canopy Cover ◽  
Planting Date ◽  
Row Spacing ◽  
Growth Stages ◽  
Seeding Rate ◽  
Yield Increase ◽  
Beginning Of Flowering

Planting date (PD), seeding rate (SR), relative maturity (RM) of cultivars, and row spacing (RS) are primary management factors affecting soybean (Glycine max (L.) Merr.) yield. The individual and synergistic effects of PD, SR, RM, and RS on seed yield and agronomic characteristics in North Dakota were herein investigated. Early and late PD, early and late RM cultivars, two SR (408,000 and 457,000 seed ha−1), and two RS (30.5 and 61 cm) were evaluated in four total environments in 2019 and 2020. Maximizing green canopy cover prior to the beginning of flowering improved seed yield. Individual factors of early PD and narrow RS resulted in yield increase of 311 and 266 kg ha−1, respectively. The combined factors of early PD, late RM, high SR, and narrow RS improved yield by 26% and provided a $350 ha−1 partial profit over conventional practices. Canopy cover and yield had relatively weak relationships with r2 of 0.36, 0.23, 0.14, and 0.21 at the two trifoliolate, four trifoliolate, beginning of flowering, and beginning of pod formation soybean growth stages, respectively. Producers in the most northern soybean region of the USA should combine early planting, optimum RM cultivars, 457,000 seed ha−1 SR, and 31 cm RS to improve yield and profit compared to current management practices.

scholarly journals Arctic riparian shrub expansion indicates a shift from streams gaining water to those that lose flow

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Anna K. Liljedahl ◽  
Ina Timling ◽  
Gerald V. Frost ◽  
Ronald P. Daanen
Keyword(s):  
Microbial Communities ◽  
Soil Microbial Communities ◽  
Late Summer ◽  
Field Measurements ◽  
The Arctic ◽  
Shrub Expansion ◽  
Riparian Ecosystems ◽  
Stream Length ◽  
Area Index ◽  
Soil Microbial

AbstractShrub expansion has been observed across the Arctic in recent decades along with warming air temperatures, but tundra shrub expansion has been most pronounced in protected landscape positions such as floodplains, streambanks, water tracks, and gullies. Here we show through field measurements and laboratory analyses how stream hydrology, permafrost, and soil microbial communities differed between streams in late summer with and without tall shrubs. Our goal was to assess the causes and consequences of tall shrub expansion in Arctic riparian ecosystems. Our results from Toolik Alaska, show greater canopy height and density, and distinctive plant and soil microbial communities along stream sections that lose water into unfrozen ground (talik) compared to gaining sections underlain by shallow permafrost. Leaf Area Index is linearly related to the change in streamflow per unit stream length, with the densest canopies coinciding with increasingly losing stream sections. Considering climate change and the circumpolar scale of riparian shrub expansion, we suggest that permafrost thaw and the resulting talik formation and shift in streamflow regime are occurring across the Low Arctic.

Green roof hydrologic performance and modeling: a review

2013 ◽  
Vol 69 (4) ◽  
pp. 727-738 ◽  
Author(s):  
Yanling Li ◽  
Roger W. Babcock
Keyword(s):  
Peak Flow ◽  
Green Roof ◽  
Field Measurements ◽  
Green Roofs ◽  
Economic Benefits ◽  
Design Parameters ◽  
Technical Literature ◽  
Factors Affecting ◽  
Model Soil ◽  
Runoff Volume

Green roofs reduce runoff from impervious surfaces in urban development. This paper reviews the technical literature on green roof hydrology. Laboratory experiments and field measurements have shown that green roofs can reduce stormwater runoff volume by 30 to 86%, reduce peak flow rate by 22 to 93% and delay the peak flow by 0 to 30 min and thereby decrease pollution, flooding and erosion during precipitation events. However, the effectiveness can vary substantially due to design characteristics making performance predictions difficult. Evaluation of the most recently published study findings indicates that the major factors affecting green roof hydrology are precipitation volume, precipitation dynamics, antecedent conditions, growth medium, plant species, and roof slope. This paper also evaluates the computer models commonly used to simulate hydrologic processes for green roofs, including stormwater management model, soil water atmosphere and plant, SWMS-2D, HYDRUS, and other models that are shown to be effective for predicting precipitation response and economic benefits. The review findings indicate that green roofs are effective for reduction of runoff volume and peak flow, and delay of peak flow, however, no tool or model is available to predict expected performance for any given anticipated system based on design parameters that directly affect green roof hydrology.

Dwarf sunflower response to row spacing, stand reduction, and defoliation at different growth stages

2003 ◽  
Vol 83 (2) ◽  
pp. 319-326 ◽  
Author(s):  
B. L. Johnson
Keyword(s):  
Helianthus Annuus ◽  
Oil Content ◽  
Growth Stage ◽  
Total Yield ◽  
Reciprocal Relationship ◽  
Yield Response ◽  
Yield Reduction ◽  
Row Spacing ◽  
Growth Stages ◽  
Helianthus Annuus L

Growth compensation of dwarf sunflower (Helianthus annuus L.) hybrids to low initial stands, later stand losses, or plant defoliation has not been reported regarding replanting decisions and crop insurance yield loss assessment. Three experiments were conducted to study the affect of stand reduction, defoliation, and row spacing on dwarf sunflower yield and quality when grown in eastern North Dakota. Experiment 1 evaluated stand reduction (0, 25, 50 and 75%) applied at growth stages (V4, R1 and R6) in 15, 45 and 76 cm spaced rows. Row spacing interactions with stand reduction and growth stage were not significant for yield indicating growth stage and stand reduction effects on yield response were independent of row spacing. In exp. 2, significant growth stage (V4, V8, R1, R2, R3, R5 and R6) by stand reduction (0, 12, 25, 37, 50, 62 and 75%) interaction showed stand reduction at vegetative growth stages not influencing yield, but as maturity progressed yield reductions became greater with increased stand reduction. Achene weight increased with increasing stand reduction at vegetative and early reproductive stages. A reciprocal relationship was noted between achene weight and achene oil content where oil content decreased as achene weight increased. Interaction of growth stage (R1 and R6) and defoliation (0, 25, 50, 75 and 100%) in exp. 3 indicated greater reduction in yield, test weight, 1000-achene weight, and achene oil conte nt as defoliatin increased at growth stage R6. Yield compensating ability of dwarf sunflower is dependent on type and level of damage and growth stage of occurrence, with total yield reduction considering all effects. Key words: Sunflower, Helianthus annuus L., row spacing, stand reduction, defoliation

scholarly journals Multiscale Effects of Multimodal Public Facilities Accessibility on Housing Prices Based on MGWR: A Case Study of Wuhan, China

2022 ◽  
Vol 11 (1) ◽  
pp. 57
Author(s):  
Lingbo Liu ◽  
Hanchen Yu ◽  
Jie Zhao ◽  
Hao Wu ◽  
Zhenghong Peng ◽  
...  
Keyword(s):  
Public Service ◽  
House Prices ◽  
Housing Prices ◽  
Positive Influence ◽  
Policy Formulation ◽  
Public Facility ◽  
Factors Affecting ◽  
Public Facilities ◽  
Geographically Weighted Regression Model ◽  
The Impact

The layout of public service facilities and their accessibility are important factors affecting spatial justice. Previous studies have verified the positive influence of public facilities accessibility on house prices; however, the spatial scale of the impact of various public facilities accessibility on house prices is not yet clear. This study takes transportation analysis zone of Wuhan city as the spatial unit, measure the public facilities accessibility of schools, hospitals, green space, and public transit stations with four kinds of accessibility models such as the nearest distance, real time travel cost, kernel density, and two step floating catchment area (2SFCA), and explores the multiscale effect of public services accessibility on house prices with multiscale geographically weighted regression model. The results show that the differentiated scale effect not only exists among different public facility accessibilities, but also exists in different accessibility models of the same sort of facility. The article also suggests that different facilities should adopt its appropriate accessibility model. This study provides insights into spatial heterogeneity of urban public service facilities accessibility, which will benefit decision making in equal accessibility planning and policy formulation for the layout of urban service facilities.

scholarly journals Evaluation of MEGAN-CLM parameter sensitivity to predictions of isoprene emissions from an Amazonian rainforest

2014 ◽  
Vol 14 (17) ◽  
pp. 23995-24041 ◽  
Author(s):  
J. A. Holm ◽  
K. Jardine ◽  
A. B. Guenther ◽  
J. Q. Chambers ◽  
E. Tribuzy
Keyword(s):  
Land Surface ◽  
Field Measurements ◽  
Leaf Temperature ◽  
Surface Model ◽  
Amazon Forest ◽  
Biophysical Parameters ◽  
Area Index ◽  
Central Amazon ◽  
Community Land Model ◽  
Environmental Controls

Abstract. Tropical trees are known to be large emitters of biogenic volatile organic compounds (BVOC), accounting for up to 75% of the global isoprene budget. Once in the atmosphere, these compounds influence multiple processes associated with air quality and climate. However, uncertainty in biogenic emissions is two-fold, (1) the environmental controls over isoprene emissions from tropical forests remain highly uncertain; and (2) our ability to accurately represent these environmental controls within models is lacking. This study evaluated the biophysical parameters that drive the global Model of Emissions of Gases and Aerosols from Nature (MEGAN) embedded in a biogeochemistry land surface model, the Community Land Model (CLM), with a focus on isoprene emissions from an Amazonian forest. Upon evaluating the sensitivity of 19 parameters in CLM that currently influence isoprene emissions by using a Monte Carlo analysis, up to 61% of the uncertainty in mean isoprene emissions was caused by the uncertainty in the parameters related to leaf temperature. The eight parameters associated with photosynthetic active radiation (PAR) contributed in total to only 15% of the uncertainty in mean isoprene emissions. Leaf temperature was strongly correlated with isoprene emission activity (R2 = 0.89). However, when compared to field measurements in the Central Amazon, CLM failed to capture the upper 10–14 °C of leaf temperatures throughout the year (i.e., failed to represent ~32 to 46 °C), and the spread observed in field measurements was not representative in CLM. This is an important parameter to accurately simulate due to the non-linear response of emissions to temperature. MEGAN-CLM 4.0 overestimated isoprene emissions by 60% for a Central Amazon forest (5.7 mg m−2 h−1 vs. 3.6 mg m−2 h−1), but due to reductions in leaf area index (LAI) by 28% in MEGAN-CLM 4.5 isoprene emissions were within 7% of observed data (3.8 mg m−2 h−1). When a slight adjustment to leaf temperature was made to match observations, isoprene emissions increased 24%, up to 4.8 mg m−2 h−1. Air temperatures are very likely to increase in tropical regions as a result of human induced climate change. Reducing the uncertainty of leaf temperature in BVOC algorithms, as well as improving the accuracy of replicating leaf temperature output in land surface models is warranted in order to improve estimations of tropical BVOC emissions.

Sign in / Sign up

Export Citation Format

Share Document