scholarly journals Relationship between Soil Health Assessment and the Growth of Lettuce

2013 ◽  
Vol 16 (1) ◽  
pp. 25-32
Author(s):  
. Riwandi ◽  
Merakati Handajaningsih
Keyword(s):  
Performance Indicators ◽  
Soil Health ◽  
Health Indicators ◽  
Total Carbon ◽  
Plant Performance ◽  
Laboratory Evaluation ◽  
Lettuce Plant ◽  
Peat Soils ◽  
Soil Variables ◽  
Lettuce Growth

Soil health is very important point for plant growth which is measured by several indicators. The purposes of the research were to assess and to classify soil health Padang Betuah area of Bengkulu, and to compare between soil health indicators and lettuce plant performance indicators. Soils, consist of mineral and peat soils, were sampled using a soil random sampling technique. Lettuce plants were grown in polybags using sample soils. Both lettuce performance and soil health were assessed by calculating the percentage of total scores of lettuce plant or soil performance indicators which derived from variables observed. Soil variables for field evaluation included color, moisture content, texture, structure, compaction, land slope, organic matter, pH, amount of earthworm, erosion level, LCC (Legume Cover Crop), and vegetation performance. Soil variables for laboratory evaluation were pH, electrical conductivity (EC), total Carbon and Nitrogen, available-Posphorus, cation exchangeable capacity, basesaturation, and aluminum saturation. While, the variables for lettuce growth performance included plant height, numbers of leaf, degree of leaf greenness, plant fresh weight, and relative percentage of shoot : root ratio. The results of field and laboratory evaluation showed that soil health were categoried as a healthy soil and moderate healthy soil both for mineral and peat soils, respectively. Furthermore, similar categories were also obtained for evaluation of plant performance categories. No correlation was found between the soil performance indicatorcategory and the lettuce performance category.Keywords: Field indicator, laboratory indicator, lettuce growth indicator, soil health

scholarly journals Interrelationships of Chemical, Physical and Biological Soil Health Indicators in Beef-Pastures of Southern Piedmont, Georgia

2021 ◽  
Vol 13 (9) ◽  
pp. 4844
Author(s):  
Subash Dahal ◽  
Dorcas H. Franklin ◽  
Anish Subedi ◽  
Miguel L. Cabrera ◽  
Laura Ney ◽  
...  
Keyword(s):  
Soil Health ◽  
Health Assessment ◽  
Health Indicators ◽  
Cost Effective ◽  
N Fertilizer ◽  
Broiler Litter ◽  
Soil Microbial ◽  
Fertilizer Recommendation ◽  
Soil Variables ◽  
System Sustainability

The study of interrelationships among soil health indicators is important for (i) achieving better understanding of nutrient cycling, (ii) making soil health assessment cost-effective by eliminating redundant indicators, and (iii) improving nitrogen (N) fertilizer recommendation models. The objectives of this study were to (i) decipher complex interrelationships of selected chemical, physical, and biological soil health indicators in pastures with history of inorganic or broiler litter fertilization, and (ii) establish associations among inorganic N, potentially mineralizable N (PMN), and soil microbial biomass (SMBC), and other soil health indicators. In situ soil respiration was measured and soil samples were collected from six beef farms in 2017 and 2018 to measure selected soil health indicators. We were able to establish associations between easy-to-measure active carbon (POXC) vs. PMN (R2 = 0.52), and N (R2 = 0.43). POXC had a noteworthy quadratic relationship with N and nitrate, where we found dramatic increase of N and nitrate beyond an inflection point of 500 mg kg−1 POXC. This point may serve as threshold for soil health assessment. The relationships of loss-on-ignition (LOI) carbon with other soil health indicators were discernable between inorganic- and broiler litter-fertilized pastures. We were able to establish association of SMBC with other soil variables (R2 = 0.76) and there was detectable difference in SMBC between inorganic-fertilized and broiler litter-fertilized pastures. These results could be useful for cost-effective soil health assessment and optimization of N fertilizer recommendation models to improve N use efficiency and grazing system sustainability.

scholarly journals Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation

2020 ◽  
Vol 6 (3) ◽  
pp. 261
Author(s):  
Ronnie Sabino Concepcion II ◽  
Jonnel Dorado Alejandrino ◽  
Sandy Cruz Lauguico ◽  
Rogelio Ruzcko Tobias ◽  
Edwin Sybingco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Stage ◽  
Growth Stages ◽  
Learning Models ◽  
Lettuce Plant ◽  
Watershed Transformation ◽  
Head Development ◽  
Area And Perimeter ◽  
Lettuce Growth ◽  
Machine Learning Models

Identifying the plant's developmental growth stages from seed leaf is crucial to understand plant science and cultivation management deeply. An efficient vision-based system for plant growth monitoring entails optimum segmentation and classification algorithms. This study presents coupled color-based superpixels and multifold watershed transformation in segmenting lettuce plant from complicated background taken from smart farm aquaponic system, and machine learning models used to classify lettuce plant growth as vegetative, head development and for harvest based on phytomorphological profile. Morphological computations were employed by feature extraction of the number of leaves, biomass area and perimeter, convex area, convex hull area and perimeter, major and minor axis lengths of the major axis length the dominant leaf, and length of plant skeleton. Phytomorphological variations of biomass compactness, convexity, solidity, plant skeleton, and perimeter ratio were included as inputs of the classification network. The extracted Lab color space information from the training image set undergoes superpixels overlaying with 1,000 superpixel regions employing K-means clustering on each pixel class. Six-level watershed transformation with distance transformation and minima imposition was employed to segment the lettuce plant from other pixel objects. The accuracy of correctly classifying the vegetative, head development, and harvest growth stages are 88.89%, 86.67%, and 79.63%, respectively. The experiment shows that the test accuracy rates of machine learning models were recorded as 60% for LDA, 85% for ANN, and 88.33% for QSVM. Comparative analysis showed that QSVM bested the performance of optimized LDA and ANN in classifying lettuce growth stages. This research developed a seamless model in segmenting vegetation pixels, and predicting lettuce growth stage is essential for plant computational phenotyping and agricultural practice optimization.

scholarly journals Relationship between soil oxidizable carbon and physical, chemical and mineralogical properties of umbric ferralsols

2011 ◽  
Vol 35 (1) ◽  
pp. 25-40 ◽  
Author(s):  
Flávio Adriano Marques ◽  
Márcia Regina Calegari ◽  
Pablo Vidal-Torrado ◽  
Peter Buurman
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Clay Content ◽  
Selective Extraction ◽  
Total Carbon ◽  
Soil Matrix ◽  
Soil Variables ◽  
Physical Chemical ◽  
Mineralogical Properties ◽  
Extractable Al

The occurrence of Umbric Ferralsols with thick umbric epipedons (> 100 cm thickness) in humid Tropical and Subtropical areas is a paradox since the processes of organic matter decomposition in these environments are very efficient. Nevertheless, this soil type has been reported in areas in the Southeast and South of Brazil, and at some places in the Northeast. Aspects of the genesis and paleoenvironmental significance of these Ferralsols still need a better understanding. The processes that made the umbric horizons so thick and dark and contributed to the preservation of organic carbon (OC) at considerable depths in these soils are of special interest. In this study, eight Ferralsols with a thick umbric horizon (UF) under different vegetation types were sampled (tropical rain forest, tropical seasonal forest and savanna woodland) and their macromorphological, physical, chemical and mineralogical properties studied to detect soil characteristics that could explain the preservation of high carbon amounts at considerable depths. The studied UF are clayey to very clayey, strongly acidic, dystrophic, and Al-saturated and charcoal fragments are often scattered in the soil matrix. Kaolinites are the main clay minerals in the A and B horizons, followed by abundant gibbsite and hydroxyl-interlayered vermiculite. The latter was only found in UFs derived from basalt rock in the South of the country. Total carbon (TC) ranged from 5 to 101 g kg-1 in the umbric epipedon. Dichromate-oxidizable organic carbon represented nearly 75 % of TC in the thick A horizons, while non-oxidizable C, which includes recalcitrant C (e.g., charcoal), contributed to the remaining 25 % of TC. Carbon contents were not related to most of the inorganic soil variables studied, except for oxalate-extractable Al, which individually explained 69 % (P < 0.001) of the variability of TC in the umbric epipedon. Clay content was not suited as predictor of TC or of the other studied C forms. Bulk density, exchangeable Al3+, Al saturation, ECEC and other parameters obtained by selective extraction were not suitable as predictors of TC and other C forms. Interactions between organic matter and poorly crystalline minerals, as indicated by oxalate-extractable Al, appear to be one of the possible organic matter protection mechanisms of these soils.

scholarly journals Plant-wide modelling for assessment and optimization of upgraded full-scale wastewater treatment plant performance

2018 ◽  
Vol 13 (3) ◽  
pp. 566-582 ◽  
Author(s):  
Nadja Hvala ◽  
Darko Vrečko ◽  
Cirila Bordon
Keyword(s):  
Wastewater Treatment ◽  
Simulation Model ◽  
Wastewater Treatment Plant ◽  
Performance Indicators ◽  
Treatment Plant ◽  
Full Scale ◽  
Plant Performance ◽  
Limit Values ◽  
Reject Water ◽  
Integrate State

Abstract This paper presents the design of a plant-wide CNP (carbon-nitrogen-phosphorus) simulation model of a full-scale wastewater treatment plant, which will be upgraded for tertiary treatment to achieve compliance with effluent total nitrogen (TN) and total phosphorus (TP) limit values. The plant-wide model of the existing plant was first designed and extensively validated under long-term dynamic operation. The most crucial step was a precise characterization of input wastewater that was performed by extending the plant performance indicators both to a water line and sludge line and systematically estimating identifiable wastewater characterization parameters from plant-wide performance indicators, i.e. effluent concentrations, biogas and sludge production, and sludge composition. The thus constructed simulation model with standard activated sludge model (ASM2d) and anaerobic digestion model (MantisAD) overpredicted ortho-P and ammonia-N on the sludge line, indicating a need to integrate state-of-the-art physico-chemical minerals precipitation models to simulate plant-wide interactions more precisely. The upgraded plant with multimode anaerobic/anoxic/oxic configuration shows limited denitrification potential. Therefore, additional reject water treatment was evaluated to improve effluent TN and TP performance.

Silvopastoral system for resilience of key soil health indicators in semi-arid environment

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Srinivasan Ramakrishnan ◽  
Sunil Kumar ◽  
Manoj Chaudhary ◽  
Prabhu Govindasamy ◽  
Maniksha Yadav ◽  
...  
Keyword(s):  
Soil Health ◽  
Health Indicators ◽  
Arid Environment ◽  
Silvopastoral System ◽  
Semi Arid
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