Comparing the Ability of Multiple Soil Sensors to Predict Soil Properties in a Scottish Potato Production System

2010 ◽  
pp. 387-396 ◽  
Author(s):  
J. A. Taylor ◽  
M. Short ◽  
A.B. McBratney ◽  
J. Wilson
Keyword(s):  
Soil Properties ◽  
Production System ◽  
Potato Production ◽  
Soil Sensors

Soil Structural Disturbance Effects on Crop Yields and Soil Properties in a No-Till Production System

2004 ◽  
Vol 96 (6) ◽  
pp. 1651-1659 ◽  
Author(s):  
M. Díaz-Zorita ◽  
J. H. Grove ◽  
L. Murdock ◽  
J. Herbeck ◽  
E. Perfect
Keyword(s):  
Soil Properties ◽  
Production System ◽  
Crop Yields ◽  
No Till ◽  
Disturbance Effects ◽  
Structural Disturbance

Simulation model for the sequential in-field machinery operations in a potato production system

2015 ◽  
Vol 116 ◽  
pp. 173-186 ◽  
Author(s):  
K. Zhou ◽  
A. Leck Jensen ◽  
D.D. Bochtis ◽  
C.G. Sørensen
Keyword(s):  
Simulation Model ◽  
Production System ◽  
Potato Production

Profile Soil Property Estimation Using a VIS-NIR-EC-Force Probe

2017 ◽  
Vol 60 (3) ◽  
pp. 683-692 ◽  
Author(s):  
Yongjin Cho ◽  
Kenneth A. Sudduth ◽  
Scott T. Drummond
Keyword(s):  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Soil Property ◽  
Soil Strength ◽  
Reflectance Spectra ◽  
Land Resource ◽  
Strength Data ◽  
Soil Sensors ◽  
Combining Data

Abstract. Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa, and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R2 = 0.32, 0.67, and 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in WC estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.

scholarly journals Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Soil Systems ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 52
Author(s):  
Gustavo M. Vasques ◽  
Hugo M. Rodrigues ◽  
Maurício R. Coelho ◽  
Jesus F. M. Baca ◽  
Ricardo O. Dart ◽  
...  
Keyword(s):  
Soil Properties ◽  
Ordinary Kriging ◽  
Soil Property ◽  
Clay Content ◽  
Exchange Capacity ◽  
Organic C ◽  
Covariate Data ◽  
Independent Validation ◽  
Soil Sensors ◽  
Dense Grid

Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.

Development of an IoT system for sensing monitoring of soil properties

2020 ◽  
Author(s):  
Byeongchul Lee ◽  
Kyoung Jae Lim ◽  
Jae E Yang ◽  
Dong Seok Yang ◽  
Jiyoeng Hong
Keyword(s):  
Big Data ◽  
Ecosystem Services ◽  
Soil Properties ◽  
Real Time ◽  
System Analysis ◽  
Vital Role ◽  
Data System ◽  
Soil Sensors ◽  
Soil Ecosystem Services ◽  
Time Basis

<p>In the age of big data, constructing a database plays a vital role in various fields. Especially, in the agricultural and environmental fields, real-time databases are useful because the fields are easily affected by dynamic nature phenomena. To construct a real-time database in these fields, various sensors and an Internet of Things (IoT) system have been widely used. In this study, an IoT system was developed to construct soil properties database on a real-time basis and aim to a big data system analysis that can assess ecosystem services provided from soil resources. The IoT system consisted of three types of soil sensors, main devices, sensor connectors, and subsidiary devices. The IoT system can measure soil temperature, moisture, and electrical conductivity (EC) data on a five-minute interval. Also, the devices were applied to two test-beds near Chuncheon city in South Korea and have been testing for the stability and availability of the system. In a further study, we will add various soil sensors and functions into the developed IoT system to improve their availability. If the developed IoT system becomes to be stable and functional, it can contribute to constructing soil properties database on a real-time basis and a big data system that assesses soil ecosystem services.</p>

scholarly journals Growth-associated factors and pre-basic seed production of ten native potato varieties using aeroponic conditions in Kishuara -Peru

2017 ◽  
Vol 21 (2) ◽  
pp. 25-38
Author(s):  
S. Medina-Quispe, S. Quispe-Chipana, J. Veneros-Guevara, C.A. Chuquillanqui-Sotomayor, C.A. Bolaños-Carriel
Keyword(s):  
Seed Production ◽  
Production System ◽  
Associated Factors ◽  
Seed Tuber ◽  
Potato Production ◽  
Tuber Formation ◽  
Experimental Unit ◽  
Average Weight ◽  
Potato Seed ◽  
Basic Seed

This experiment was carried out in the greenhouse of the sub-region agrarian direction at Kishuara district in the province of Andahuaylas of the Apurimac region of Peru under the aeroponic production system. Growth-associated factors and pre-basic seed production were evaluated in ten varieties of native potatoes using aeroponic conditions in Kishuara - Peru. A complete randomized blocks design was used, and the experimental unit consisted of 12 plants spaced at 20 cm x 18 cm. The variables under study were: height of the plant (average of 12 sampling plants), days to the tuber formation, survival rate (%), days to senescence of the plant, diameter of the stem at senescence, days to the first harvest, yield, number of tubers per plant, and the average weight of tubers. Huayro variety reached the highest growth in plant height (133 cm), the highest yield (981 g / plant), and the highest average weight of mini tubers/plant (12.5 g / plant). The Q'ompis variety was the most precocious (39 days to tuber formation). The days to the first harvest were 96 days for the varieties: Duraznillo, Yana Suytu, Q'ompis and Camotillo, and 125 in Q’eq’orani. Huayro seems to be the best variety to be used for generation of new cultivars and exploitation as native variety for potato seed tuber under aeroponic production system. Our study open the possibility for production of best quality pre-basic seed for native potato production in Peru.

scholarly journals Poultry litter and fresh mulch of Elephant grass improve the organic potato production

2020 ◽  
Vol 50 ◽  
Author(s):  
Renato Yagi ◽  
Nilceu Ricetti Xavier de Nazareno ◽  
Jackson Kawakami
Keyword(s):  
Late Blight ◽  
Production System ◽  
Poultry Litter ◽  
Potato Production ◽  
Organic Production ◽  
Elephant Grass ◽  
Potato Tubers ◽  
Organic Fertilization ◽  
Organic System ◽  
Marketable Potato

ABSTRACT The organic production system for potato is usually limited by the occurrence of diseases and nutrient shortage. In these cases, fresh grass mulch and organic fertilization can interact in the foliar late blight infestation and increase the yield and quality of marketable potato tubers, in the organic production system. Aiming to validate this hypothesis, four poultry litter doses (0 Mg ha-1, 10 Mg ha-1, 20 Mg ha-1 and 30 Mg ha-1), which were incorporated into the soil at the pre-planting stage of organic grown potato combined with the presence and absence of fresh mulch (60 Mg ha-1) composed of chopped Elephant grass, were tested. The incorporation of poultry litter into the soil at the pre-planting of potato or the use of fresh grass mulch after the potato hilling inhibits the late blight infestation on leaves, in the organic system. The application of fresh grass mulch after the hilling operation enhances the effect of organic fertilization at the pre-planting of potatoes. The pre-planting application of poultry litter increases the yield and decreases the specific gravities of marketable potato tubers in association with fresh grass mulch. The use of poultry litter at pre-planting and fresh grass mulch improve the sustainable potato cropping in the organic production system.

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