scholarly journals Case studies for precision agriculture

2017 ◽  
Vol 38 (SI 2 - 6th Conf EFPP 2002) ◽  
pp. 704-710
Author(s):  
J. Šilha ◽  
P. Hamouz ◽  
V. Táborský ◽  
K. Štípek ◽  
J. Šnobl ◽  
...  
Keyword(s):  
Soil Ph ◽  
Precision Agriculture ◽  
Crop Production ◽  
Crop Protection ◽  
Decision Rules ◽  
Available P ◽  
Agrochemical Properties ◽  
P Content ◽  
Complex Decision ◽  
Soil Agrochemical Properties

The results of spatial variability of plant-available soil nutrients (P, K, Mg) and soil pH are described in this paper. Experiment was realized on the field of area 72 ha (orthic luvisol), located in the area of Český Brod. The use of coefficient of variation as a criterion of variability of soil agrochemical properties and yield on the field showed the following: the highest variability was observed in available P, the second highest variability was in available K, and the lowest variability of main non-mobile nutrients was in the available Mg. Soil pH was the lowest of all measured soil properties. Although the highest correlation coefficient between the soil available P content and soil pH was established, the process of spatial dependence was not detected. Detailed field scouting and others data can be important elements, as can complex decision rules, taking into account additional factors such as the characteristics of crop protection agents and preferences of the farm manager. This paper illustrates, how to plant nutritions, crop protection, crop production might be integrated to support these diseases and weeds management decisions.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

scholarly journals Economics of autonomous equipment for arable farms

2021 ◽  
Author(s):  
James Lowenberg-DeBoer ◽  
Kit Franklin ◽  
Karl Behrendt ◽  
Richard Godwin
Keyword(s):  
Precision Agriculture ◽  
Environmental Performance ◽  
Crop Production ◽  
Practical Experience ◽  
Production Costs ◽  
Farm Size ◽  
Medium Size ◽  
Economic Implications ◽  
The United Kingdom ◽  
Crop Agriculture

AbstractBy collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment for arable agriculture using a grain-oilseed farm in the United Kingdom as an example. The study is possible because the Hands Free Hectare (HFH) demonstration project at Harper Adams University has produced grain with autonomous equipment since 2017. That practical experience showed the technical feasibility of autonomous grain production and provides parameters for farm-level linear programming (LP) to estimate farm management opportunities when autonomous equipment is available. The study shows that arable crop production with autonomous equipment is technically and economically feasible, allowing medium size farms to approach minimum per unit production cost levels. The ability to achieve minimum production costs at relatively modest farm size means that the pressure to “get big or get out” will diminish. Costs of production that are internationally competitive will mean reduced need for government subsidies and greater independence for farmers. The ability of autonomous equipment to achieve minimum production costs even on small, irregularly shaped fields will improve environmental performance of crop agriculture by reducing pressure to remove hedges, fell infield trees and enlarge fields.

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

scholarly journals Feed Composition Differences Resulting from Organic and Conventional Farming Practices Affect Physiological Parameters in Wistar Rats—Results from a Factorial, Two-Generation Dietary Intervention Trial

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 377
Author(s):  
Marcin Barański ◽  
Dominika Średnicka-Tober ◽  
Leonidas Rempelos ◽  
Gultakin Hasanaliyeva ◽  
Joanna Gromadzka-Ostrowska ◽  
...  
Keyword(s):  
Crop Production ◽  
Wistar Rats ◽  
Dietary Intervention ◽  
Growth Parameters ◽  
Crop Protection ◽  
The Body ◽  
Interactive Effects ◽  
Supporting Evidence ◽  
Feed Composition ◽  
Npk Fertilizer

Recent human cohort studies reported positive associations between organic food consumption and a lower incidence of obesity, cancer, and several other diseases. However, there are very few animal and human dietary intervention studies that provide supporting evidence or a mechanistic understanding of these associations. Here we report results from a two-generation, dietary intervention study with male Wistar rats to identify the effects of feeds made from organic and conventional crops on growth, hormonal, and immune system parameters that are known to affect the risk of a number of chronic, non-communicable diseases in animals and humans. A 2 × 2 factorial design was used to separate the effects of contrasting crop protection methods (use or non-use of synthetic chemical pesticides) and fertilizers (mineral nitrogen, phosphorus and potassium (NPK) fertilizers vs. manure use) applied in conventional and organic crop production. Conventional, pesticide-based crop protection resulted in significantly lower fiber, polyphenol, flavonoid, and lutein, but higher lipid, aldicarb, and diquat concentrations in animal feeds. Conventional, mineral NPK-based fertilization resulted in significantly lower polyphenol, but higher cadmium and protein concentrations in feeds. Feed composition differences resulting from the use of pesticides and/or mineral NPK-fertilizer had a significant effect on feed intake, weight gain, plasma hormone, and immunoglobulin concentrations, and lymphocyte proliferation in both generations of rats and in the second generation also on the body weight at weaning. Results suggest that relatively small changes in dietary intakes of (a) protein, lipids, and fiber, (b) toxic and/or endocrine-disrupting pesticides and metals, and (c) polyphenols and other antioxidants (resulting from pesticide and/or mineral NPK-fertilizer use) had complex and often interactive effects on endocrine, immune systems and growth parameters in rats. However, the physiological responses to contrasting feed composition/intake profiles differed substantially between the first and second generations of rats. This may indicate epigenetic programming and/or the generation of “adaptive” phenotypes and should be investigated further.

Ash from the thermal gasification of pig manure—effects on ryegrass yield, element uptake, and soil properties

Soil Research ◽  
2012 ◽  
Vol 50 (5) ◽  
pp. 406 ◽  
Author(s):  
Ksawery Kuligowski ◽  
Robert John Gilkes ◽  
Tjalfe Gorm Poulsen ◽  
Baiq Emielda Yusiharni
Keyword(s):  
Soil Ph ◽  
Pig Manure ◽  
P Availability ◽  
Higher Plant ◽  
Regulatory Limits ◽  
P Content ◽  
Relative Agronomic Effectiveness ◽  
Elemental Uptake ◽  
Plant Yields ◽  
P Sources

Effects of thermally gasified pig manure ash (GA) and lime-free gasified ash (LF-GA) on properties of an acidic soil (pH 4.5) and the growth and elemental uptake of ryegrass (Lolium rigidum Gaudin) were investigated. The GA was an effective liming agent (2% addition raised soil pH from 4.5 to 7.9); both GA and LF-GA increased soil electrical conductivity and bicarbonate-extractable phosphorus (P). Soil fertilised with LF-GA supported slightly higher plant dry matter (DM) yield than GA (1.5–1.7 v. 1.2–1.5 g DM/kg soil) for the first harvest, due to greater initial P availability at pH <5 than at pH >6. However, plant yields for the subsequent two harvests were similar, as soil acidity dissolved lime in untreated ash (GA) over time. Maximum yields for ash-treated soil and soil treated with mono-calcium phosphate (MCP) were similar. Relative agronomic effectiveness of P sources for three harvests, based on plant P content compared with values for MCP, were 6, 11, and 12% for GA and 19, 10, and 33% for LF-GA. Internal efficiency of P utilisation was similar for all three P sources for each harvest, indicating that differences in yield were mostly a consequence of differences in P supply. Heavy metal concentrations in plants fertilised with ash were minor and within regulatory limits. In general, application of ash did not systematically affect the concentrations of elements (Al, B, Cd, Mg, Mn, Fe, Pb, S, Se) in plants.

scholarly journals Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

2021 ◽  
Vol 13 (8) ◽  
pp. 1519
Author(s):  
Kensuke Kawamura ◽  
Tomohiro Nishigaki ◽  
Andry Andriamananjara ◽  
Hobimiarantsoa Rakotonindrina ◽  
Yasuhiro Tsujimoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Soil Phosphorus ◽  
Predictive Ability ◽  
Available P ◽  
Accurate Estimation ◽  
One Dimensional ◽  
P Content ◽  
Soil Available P

As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.

CERTIFICATION SUBSIDIZING AND ORGANIC PRODUCTION DEVELOPMENT

2020 ◽  
Vol 10 (4) ◽  
pp. 580-593
Author(s):  
M A. Bryzgalina ◽  
Keyword(s):  
Organic Agriculture ◽  
Crop Production ◽  
Plant Protection ◽  
Crop Protection ◽  
Organic Production ◽  
Mineral Fertilizers ◽  
Selling Price ◽  
Saratov Region ◽  
Annual Volume ◽  
The Government

The demand for organic food is a prerequisite for the formation and development of organic agriculture, and the task of promoting it on domestic and foreign markets is among the priority ones. A serious problem in the sale of this category of goods to the domestic food markets of the country is the distrust of potential consumers. It is possible to solve this problem through certification and the use of a well-known brand. Certification of manufacturers of environmentally friendly products is a rather complicated and expensive procedure, therefore it is not available for most agricultural producers in the Saratov region. However, basing on the fact that today the task of developing the organic agriculture industry is set at the level of the government of the country, it is possible to solve this problem with the support of the state. The article examines the enterprises of the Saratov region of various legal forms, which do not use fertilizers and chemical means of crop protection in the production of crop production. Using the example of agricultural organizations and farms in the region, a mechanism for subsidizing certification of the most promising producers of organic wheat (winter and spring) is proposed, which includes the allocation of targeted subsidies for its implementation. As a criterion for the selection of applicants for this type of state support, as well as the distribution of budgetary resources between them, it is proposed to use the average indicator (potential) of the annual volume of organic production in the work. As a result, direct participants in certification subsidies were selected from the compiled sample of the studied enterprises that do not use chemical plant protection products and mineral fertilizers and the total annual volume of their marketable wheat was determined. The author determined the maximum cost of quality confirmation procedures for one enterprise, taking into account the increasing coefficients per one day of inspection, and also established the largest amount of budgetary resources that may be spent on the implementation of the proposed measure. In order to evaluate the effectiveness of the proposed certification subsidy mechanism, the author developed formulas for determining the selling price of products in the promising organic segment, taking into account its increase by the level of premium premiums.

scholarly journals Modifications of phosphorus in Latosol as a function of humic acids and acidity

2018 ◽  
Vol 22 (7) ◽  
pp. 488-492 ◽  
Author(s):  
Márcia H. Beck ◽  
Pedro A. V. Escosteguy ◽  
Deborah P. Dick
Keyword(s):  
Factorial Design ◽  
Humic Acids ◽  
Soil Ph ◽  
Crucial Role ◽  
Soil Acidity ◽  
Ph Value ◽  
P Availability ◽  
Soil P ◽  
P Content

ABSTRACT The effect of humic acids (HA) on phosphorus (P) availability is still contradictory; thus, it is necessary to identify the conditions that play a crucial role in this effect. The aim of this study was to investigate the effect of HA application, combined with doses of P, on the content of this nutrient in a Latosol with and without acidity correction. Two experiments were carried out, one with HA from peat and another with HA from mineral charcoal (leonardite). Doses of these acids (0; 1.12 and 5.62 mg C g-1 of soil) and P (26.2 and 104.7 mg P g-1 of soil, 1 and 4-fold higher than recommended, respectively) were tested at soil pH 4.5 and 7.0, in a three-factorial design. The soil was incubated for 20 days and the soil-P content was measured by Mehlich-1 and remaining-P tests. The effect of HAs on P availability varied with the P doses and soil acidity. Humic acids application increases P content in Latosol when P dose is higher than recommended and there is no acidity correction (pH 4.5). However, there is no effect of HAs application on soil-P content when applying the recommended amount of this nutrient, irrespective of the pH value.

scholarly journals Phosphorus forms in leaves and their relationships with must composition and yield in grapevines

2017 ◽  
Vol 52 (5) ◽  
pp. 319-327 ◽  
Author(s):  
Rogério Piccin ◽  
Rafael da Rosa Couto ◽  
Roque Júnior Sartori Bellinaso ◽  
Luciano Colpo Gatiboni ◽  
Lessandro De Conti ◽  
...  
Keyword(s):  
The Other ◽  
Available P ◽  
Anthocyanin Content ◽  
P Fractionation ◽  
Cabernet Franc ◽  
Phosphorus Forms ◽  
Yield Parameters ◽  
P Content ◽  
Total P

Abstract: The objective of this work was to evaluate phosphorus forms in grape leaves and their relationships with must composition and yield in grapevines grown in a Typic Hapludalf with different available P contents. Two experiments were carried out with Vitis viniferacultivars, one with 'Tannat' and the other with 'Cabernet Franc' grapes. Experiment 1 consisted of two vineyards of 'Tannat', with the following P content in the soil: V1, 11.8 mg kg-1 P; and V2, 34.6 mg kg-1 P. Experiment 2 consisted of two vineyards of 'Cabernet Franc', with the following P content in the soil: V1, 16.0 mg kg-1 P; and V2, 37.0 mg kg-1 P. Leaves were collected at flowering (FL) and veraison (V), and, after their preparation, P forms were evaluated. Yield and must composition were assessed. The highest yield was observed in V2 of experiment 1 and in V2 of experiment 2. Total P content and P forms in leaves at FL and V have no relationship with yield parameters; however, total P content in leaves has a relationship with anthocyanin content in the must of 'Tannat' grapevines. Therefore, P fractionation in leaves predicts neither grapevine yield nor must composition.

EFFECTS OF LIME ON EXTRACTABLE ALUMINUM AND OTHER SOIL PROPERTIES AND ON BARLEY AND ALFALFA GROWN IN POT TESTS

1972 ◽  
Vol 52 (3) ◽  
pp. 427-438 ◽  
Author(s):  
A. J. MacLEAN ◽  
R. L. HALSTEAD ◽  
B. J. FINN
Keyword(s):  
Plant Growth ◽  
Soil Properties ◽  
Soil Ph ◽  
Acid Soil ◽  
Soil Samples ◽  
Neutral Salt ◽  
Incubation Experiment ◽  
Pot Experiments ◽  
P Content ◽  
Mn Content

Liming of six acid soil samples in an incubation experiment with rates to raise the soil pH to 6.0 or above eliminated Al soluble in 0.01 M CaCl2, reduced soluble Mn and Zn, increased NO3-N markedly, and at the highest pH increased the amounts of NaHCO3-soluble P in some of the soils. In corresponding pot experiments, liming increased the yield of alfalfa and in three of the soils the yield of barley also. Liming reduced the concentrations of the metals in the plants and at the highest pH tended to increase the P content of the plants. Liming to a pH of about 5.3 eliminated or greatly reduced soluble Al and the soils were base saturated as measured by the replacement of Al, Ca, and Mg by a neutral salt. There was some evidence that liming to reduce soluble Al and possibly Mn was beneficial for plant growth. Gypsum increased the concentrations of Al, Mn, and Zn in 0.01 M CaCl2 extracts of the soils whereas phosphate reduced them. The changes in the Mn content of the plants following these treatments were in agreement with the amounts of Mn in the CaCl2 extracts.

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