Soil nutrient content, above-ground biomass and litter in a semi-arid shrubland, South Africa

Geoderma ◽  
2011 ◽  
Vol 164 (3-4) ◽  
pp. 128-137 ◽  
Author(s):  
Peter Azah Abanda ◽  
John S. Compton ◽  
Robyn E. Hannigan
Keyword(s):  
South Africa ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Soil Nutrient Content ◽  
Semi Arid

FERTIGATION OF PRIMOCANE-FRUITING RASPBERRY – LEAF AND SOIL NUTRIENT CONTENT BETWEEN APPLICATIONS

2009 ◽  
pp. 341-348 ◽  
Author(s):  
K.S. Koumanov ◽  
I. Tsareva ◽  
K. Kolev ◽  
G. Kornov
Keyword(s):  
Soil Nutrient ◽  
Nutrient Content ◽  
Soil Nutrient Content

scholarly journals Estimation of Soil Nutrient Content Using Hyperspectral Data

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1129
Author(s):  
Yiping Peng ◽  
Lu Wang ◽  
Li Zhao ◽  
Zhenhua Liu ◽  
Chenjie Lin ◽  
...  
Keyword(s):  
Least Squares ◽  
Soil Nutrients ◽  
Cross Validation ◽  
Regional Scale ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
New Method ◽  
Accurate Estimation ◽  
Support Vector ◽  
Soil Nutrient Content

Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates proves to be difficult due to the weak spectral features of soil nutrients and the low accuracy of soil nutrient estimation models. This study proposed a new method to improve soil nutrient estimation. Firstly, for obtaining characteristic variables, we employed partial least squares regression (PLSR) fit degree to select an optimal screening algorithm from three algorithms (Pearson correlation coefficient, PCC; least absolute shrinkage and selection operator, LASSO; and gradient boosting decision tree, GBDT). Secondly, linear (multi-linear regression, MLR; ridge regression, RR) and nonlinear (support vector machine, SVM; and back propagation neural network with genetic algorithm optimization, GABP) algorithms with 10-fold cross-validation were implemented to determine the most accurate model for estimating soil total nitrogen (TN), total phosphorus (TP), and total potassium (TK) contents. Finally, the new method was used to map the soil TK content at a regional scale using the soil component spectral variables retrieved by the fully constrained least squares (FCLS) method based on an image from the HuanJing-1A Hyperspectral Imager (HJ-1A HSI) of the Conghua District of Guangzhou, China. The results identified the GBDT-GABP was observed as the most accurate estimation method of soil TN ( of 0.69, the root mean square error of cross-validation (RMSECV) of 0.35 g kg−1 and ratio of performance to interquartile range (RPIQ) of 2.03) and TP ( of 0.73, RMSECV of 0.30 g kg−1 and RPIQ = 2.10), and the LASSO-GABP proved to be optimal for soil TK estimations ( of 0.82, RMSECV of 3.39 g kg−1 and RPIQ = 3.57). Additionally, the highly accurate LASSO-GABP-estimated soil TK (R2 = 0.79) reveals the feasibility of the LASSO-GABP method to retrieve soil TK content at the regional scale.

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 Effects of Two Trichoderma Strains on Plant Growth, Rhizosphere Soil Nutrients, and Fungal Community of Pinus sylvestris var. mongolica Annual Seedlings

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 758 ◽  
Author(s):  
Saiyaremu Halifu ◽  
Xun Deng ◽  
Xiaoshuang Song ◽  
Ruiqing Song
Keyword(s):  
Enzyme Activity ◽  
Community Structure ◽  
Plant Growth ◽  
Soil Nutrients ◽  
Fungal Community ◽  
Rhizosphere Soil ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Root Structure ◽  
Soil Nutrient Content

Trichoderma spp. are proposed as major plant growth-promoting fungi that widely exist in the natural environment. These strains have the abilities of rapid growth and reproduction and efficient transformation of soil nutrients. Moreover, they can change the plant rhizosphere soil environment and promote plant growth. Pinus sylvestris var. mongolica has the characteristics of strong drought resistance and fast growth and plays an important role in ecological construction and environmental restoration. The effects on the growth of annual seedlings, root structure, rhizosphere soil nutrients, enzyme activity, and fungal community structure of P. sylvestris var. mongolica were studied after inoculation with Trichoderma harzianum E15 and Trichoderma virens ZT05, separately. The results showed that after inoculation with T. harzianum E15 and T. virens ZT05, seedling biomass, root structure index, soil nutrients, and soil enzyme activity were significantly increased compared with the control (p < 0.05). There were significant differences in the effects of T. harzianum E15 and T. virens ZT05 inoculation on the growth and rhizosphere soil nutrient of P. sylvestris var. mongolica (p < 0.05). For the E15 treatment, the seedling height, ground diameter, and total biomass of seedlings were higher than that those of the ZT05 treatment, and the rhizosphere soil nutrient content and enzyme activity of the ZT05 treatment were higher than that of the E15 treatment. The results of alpha and beta diversity analyses showed that the fungi community structure of rhizosphere soil was significantly different (p < 0.05) among the three treatments (inoculated with T. harzianum E15, T. virens ZT05, and not inoculated with Trichoderma). Overall, Trichoderma inoculation was correlated with the change of rhizosphere soil nutrient content.

scholarly journals Soil Nutrient Content Influences the Abundance of Soil Microbes but Not Plant Biomass at the Small-Scale

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e91998 ◽  
Author(s):  
Kadri Koorem ◽  
Antonio Gazol ◽  
Maarja Öpik ◽  
Mari Moora ◽  
Ülle Saks ◽  
...  
Keyword(s):  
Soil Microbes ◽  
Plant Biomass ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Small Scale ◽  
Soil Nutrient Content

AGRO SENSORS

2015 ◽  
Vol 3 (4) ◽  
pp. 325-327
Author(s):  
M.R Suja Barathi
Keyword(s):  
Soil Nutrient ◽  
Nutrient Content ◽  
Feedback Mechanism ◽  
Reference Voltage ◽  
Common Input ◽  
Current Output ◽  
Soil Nutrient Content ◽  
The Common ◽  
Effect Transistor ◽  
Resistance Value

Soil nutrient is detected by the use of ISFETs and the corresponding current is obtained from it according to the variation in concentration of the nutrient in soil and their pH. The use of metal oxide coated semiconductors for gas detection is high nowadays. The Ion sensitive field effect transistor has the electrode. This on reaction with the analyst changes its resistance value and corresponding signals are generated and the transducer unit in the sensor gives the varying current output. This could be then processed that is converted to voltage, amplified and then given to a window detector where in the voltage obtained from the sensor could be tested for it be in between two reference voltage levels and these reference voltage levels represent the level of nutrients in the soil either it is deficient, in surplus or in sufficient amounts. This could be used to make the LEDs glow. It proves to be an easy way of observing the soil nutrient content even by an illiterate. The checking of soil nutrient content could be automated and made periodic by the use of microcontroller, the feedback mechanism involving the soil’s nutrient content value as the common input and the ideal or required value as the reference input.

Soil nutrient content of old-field and agricultural ecosystems exposed to chronic gamma irradiation

1975 ◽  
Vol 15 (4) ◽  
pp. 329-336 ◽  
Author(s):  
T.V. Armentano ◽  
B.R. Holt ◽  
P.J. Bottino
Keyword(s):  
Gamma Irradiation ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Old Field ◽  
Agricultural Ecosystems ◽  
Soil Nutrient Content

scholarly journals Soil nutrient content in relation to women's agricultural knowledge in the urban gardens of Kisumu, Kenya

2018 ◽  
Author(s):  
Nicolette Tamara R. J. M. Jonkman ◽  
Esmee D. Kooijman ◽  
Boris Jansen ◽  
Nicky R. M. Pouw ◽  
Karsten Kalbitz
Keyword(s):  
Cultural Context ◽  
Management Practices ◽  
Soil Analysis ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Agricultural Management ◽  
Urban Gardens ◽  
Available N ◽  
Agricultural Knowledge ◽  
Soil Nutrient Content

Abstract. Agricultural production in Kenya has been declining since the 1980s, either because soils are mismanaged or because they lack nutrients. In Kisumu, just under 50 % of the workers in the urban gardens are female. On average, women spend more hours a day in the gardens than men. To increase yields, women’s knowledge has to be considered in agricultural management. However, women face greater obstacles in land ownership, investment, and farm inputs due to social and cultural constraints as consequence of their gender. This case study aimed to determine the nutrient content in soils of the urban gardens of Kisumu, the agricultural knowledge of the women farmers and how their knowledge influences soil nutrient content through their management. Soils were sampled in Nyalenda, one of Kisumu's informal settlements where urban gardening is practised, to determine soil nutrient content. To determine how agricultural management practices influences total C and N, available N and P, and exchangeable K, Mg and Ca in the soil, two prevailing practices were compared: (1) applying manure only, and: (2) applying manure while intercropping with cowpeas. Interviews and focus group discussions were organised to determine what knowledge the female vegetable farmers possess, and where they acquired their knowledge. Soil analysis showed that agricultural management had significant effects on nutrient presence and availability. Intercropping led to significantly lower total soil nutrient contents than when only manure was applied. However, due to socio-economic factors, such as poverty, intercropping was applied in a way that did not increase soil nutrients but diversified revenue. The knowledge of the female vegetable growers was found to be limited to practical and sensory knowledge. This shows that in addition to socio-economic and cultural context, gendered knowledge differentiation has to be acknowledged and used in agricultural training when aiming to improve soil nutrient status and agricultural yields.

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