scholarly journals Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence

2020 ◽  
Author(s):  
Alessandro da Costa Lima ◽  
Kassio Ferreira Mendes
Keyword(s):  
Environmental Impact ◽  
Precision Agriculture ◽  
Weed Management ◽  
Crop Productivity ◽  
Variable Rate ◽  
Chemical Management ◽  
Variable Rate Application ◽  
Skilled Workforce ◽  
Investment Capacity

With the advent of precision agriculture, it was possible to integrate several technologies to develop the variable rate application (VRA). The use of VRA allows savings in the use of herbicides, better weed control, lower environmental impact and, indirectly, increased crop productivity. There are VRA techniques based on maps and sensors for herbicide application in preemergence (PRE) and postemergence (POST). The adoption of the type of system will depend on the investment capacity of the producer, skilled workforce available, and the modality of application. Although it still has some limitations, VRA has been widespread and has been occupying more and more space in chemical management, the tendency in the medium- and long term is that there is a gradual replacement of the conventional method of application. Given the benefits provided by VRA along with the engagement of companies and researchers, there will be constant evolution and improvement of this technology, cheapening the costs of implementation and providing its adoption by an increasing number of producers. Thus, the objective of this chapter was to address an overview of the use of herbicides in VRA for weed management in PRE and POST.

Variable-rate application technologies in precision agriculture

2018 ◽  
pp. 171-193
Author(s):  
Kenneth A. Sudduth ◽  
◽  
Aaron J. Franzen ◽  
Heping Zhu ◽  
Scott T. Drummond ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Variable Rate ◽  
Variable Rate Application

scholarly journals Life Cycle Assessment of Variable Rate Fertilizer Application in a Pear Orchard

2020 ◽  
Vol 12 (17) ◽  
pp. 6893
Author(s):  
Anna Vatsanidou ◽  
Spyros Fountas ◽  
Vasileios Liakos ◽  
George Nanos ◽  
Nikolaos Katsoulas ◽  
...  
Keyword(s):  
Climate Change ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Impact ◽  
Precision Agriculture ◽  
Management System ◽  
Primary Data ◽  
Variable Rate ◽  
Impact Reduction ◽  
Pear Orchard

Precision Agriculture (PA) is a crop site-specific management system that aims for sustainability, adopting agricultural practices more friendly to the environment, like the variable rate application (VRA) technique. Many studies have dealt with the effectiveness of VRA to reduce nitrogen (N) fertilizer, while achieving increased profit and productivity. However, only limited attention was given to VRA’s environmental impact. In this study an International Organization for Standardization (ISO) based Life Cycle Assessment (LCA) performed to identify the environmental effects of N VRA on a small pear orchard, compared to the conventional uniform application. A Cradle to Gate system with a functional unit (FU) of 1 kg of pears was analyzed including high quality primary data of two productive years, including also the non-productive years, as well as all the emissions during pear growing and the supply chains of all inputs, projecting them to the lifespan of the orchard. A methodology was adopted, modelling individual years and averaging over the orchard’s lifetime. Results showed that Climate change, Water scarcity, Fossil fuels and Particulate formation were the most contributing impact categories to the overall environmental impact of the pear orchard lifespan, where climate change and particulates were largely determined by CO2, N2O, and NH3 emissions to the air from fertilizer production and application, and as CO2 from tractor use. Concerning fertilization practice, when VRA was combined with a high yield year, this resulted in significantly reduced environmental impact. LCA evaluating an alternative fertilizer management system in a Greek pear orchard revealed the environmental impact reduction potential of that system.

scholarly journals Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1104
Author(s):  
Mohammad Rokhafrouz ◽  
Hooman Latifi ◽  
Ali A. Abkar ◽  
Tomasz Wojciechowski ◽  
Mirosław Czechlowski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Statistical Tests ◽  
Unsupervised Clustering ◽  
Data Availability ◽  
Agricultural Drought ◽  
Rank Test ◽  
Variable Rate ◽  
Management Zones ◽  
Variable Rate Application

Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.

scholarly journals Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 645
Author(s):  
S. Hamed Javadi ◽  
Angela Guerrero ◽  
Abdul M. Mouazen
Keyword(s):  
Feature Selection ◽  
Precision Agriculture ◽  
Input Data ◽  
Clustering Algorithm ◽  
Near Infrared ◽  
Variable Rate ◽  
Yield Data ◽  
Data Format ◽  
Variable Rate Application ◽  
Cross Correlation Analysis

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

Weed Science Beyond the Weeds: The Role of Integrated Weed Management (IWM) in Agroecosystem Health

Weed Science ◽  
1996 ◽  
Vol 44 (2) ◽  
pp. 437-445 ◽  
Author(s):  
Clarence J. Swanton ◽  
Stephen D. Murphy
Keyword(s):  
Cover Crops ◽  
Weed Management ◽  
Crop Yields ◽  
Crop Productivity ◽  
Integrated Weed Management ◽  
Spatial And Temporal Scales ◽  
Weed Population Dynamics ◽  
Environmental Goals ◽  
Predictive Approaches

Integrated weed management (IWM) research has focused on how crop yields and weed interference are affected by changes in management, e.g., tillage, herbicide application timing and rates, cover crops, and planting patterns. Acceptance of IWM will depend on recommendation of specific strategies that manage weeds and maintain crop productivity; such research will and should continue. However, IWM needs to move from a descriptive to a predictive phase if long-term strategies are to be adopted. Linking management changes with crop-weed modeling that includes such components as weed population dynamics and the ecophysiological basis of competition will help predict future weed problems and solutions and the economic risks and benefits of intervention. Predictive approaches would help incorporate IWM into models of the processes that occur in agricultural systems at wider spatial and temporal scales, i.e., in agroecosystems comprised of the interactions among organisms (including humans) and the environment. It is at these larger scales that decisions about management are initiated and where questions about the long-term consequences and constraints of IWM and agriculture are often asked. These questions can be addressed by agroecosystem health, an approach that integrates biophysical, social, and economic concerns and recognizes that agriculture is part of a world with many complex subsystems and interactions. Indicators are used to examine the status of an agroecosystem, e.g., whether or not it contains all that is necessary to continue functioning. Indicators include soil quality, crop productivity, and water quality; all of these are related to the rationale of IWM, hence IWM can be linked to agroecosystem health. Ancillary effects of using IWM relate to other indicators such as diversity and energy efficiency. Linking IWM to agroecosystem health has at least two benefits: (1) predictive models within IWM can be incorporated into larger agroecosystem models to explore hitherto unforseen problems or benefits of IWM, and (2) the relevance and benefits of IWM should become clearer to the public and government agencies who otherwise might not examine how IWM promotes many of the larger social, economic and environmental goals being promulgated.

scholarly journals Variable Rate Irrigation (VRI)

2020 ◽  
pp. 22
Author(s):  
João Coimbra ◽  
José Rafael Marques da Silva ◽  
Manuela Correia
Keyword(s):  
Precision Agriculture ◽  
Variable Rate ◽  
Irrigation Technology ◽  
Variable Rate Application ◽  
Variable Rate Irrigation

Many types of technology are used in variable rate application for Precision Agriculture. In this case, we are talking about Variable Rate Irrigation technology. Materials for this topic include a presentation and a text, that are complementary.

scholarly journals Nitrogen replenishment using variable rate application technique in a small hand-harvested pear orchard

2018 ◽  
Vol 15 (4) ◽  
pp. e0209 ◽  
Author(s):  
Anna Vatsanidou ◽  
George D. Nanos ◽  
Spyros Fountas ◽  
John Baras ◽  
Anamaria Castrignano ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Organic Matter Content ◽  
Climatic Conditions ◽  
Matter Content ◽  
Management Approach ◽  
Variable Rate ◽  
Growing Seasons ◽  
Variable Rate Application ◽  
Small Hand ◽  
Pear Orchard

Precision agriculture is a management approach for sustainable agriculture. It can be applied even in small fields. It aims to optimize inputs, improve profits, and reduce adverse environmental impacts. In this study, a series of measurements were conducted over three growing seasons to assess variability in a 0.55 ha pear orchard located in central Greece. Soil ECa was measured using EM38 sensor, while soil samples were taken from a grid 17 × 8 m and analysed for texture, pH, P, K, Mg, CaCO3, and organic matter content. Data analysis indicated that most of the nutrients were at sufficient levels. Soil and yield maps showed considerable variability while fruit quality presented small variations across the orchard. Yield fluctuations were observed, possibly due to climatic conditions. Prescription maps were developed for nitrogen variable rate application (VRA) for two years based on the replacement of the nutrients removed by the crop. VRA application resulted in 56% and 50% reduction of N fertiliser compared to uniform application.

Connecting Crop Nutrient Use Efficiency to Future Soil Productivity

2018 ◽  
Vol 102 (4) ◽  
pp. 8-10
Author(s):  
Fernando García ◽  
Andrés Grasso ◽  
María González Sanjuan ◽  
Adrián Correndo ◽  
Fernando Salvagiotti
Keyword(s):  
Nutrient Management ◽  
Nutrient Use Efficiency ◽  
Management Strategies ◽  
Crop Productivity ◽  
Soil Productivity ◽  
Nutrient Use ◽  
Grain Crop ◽  
Wide Scale ◽  
Use Efficiency

Trends over the past 25 years indicate that Argentina’s growth in its grain crop productivity has largely been supported by the depletion of the extensive fertility of its Pampean soils. Long-term research provides insight into sustainable nutrient management strategies ready for wide-scale adoption.

Influence of long-term fertilization on soil microbial biomass and dehydrogenase activity in relation to crop productivity in an acid Inceptisols

2019 ◽  
Vol 56 (3) ◽  
pp. 305-311
Author(s):  
Debasis Purohit ◽  
Mitali Mandal ◽  
Avisek Dash ◽  
Kumbha Karna Rout ◽  
Narayan Panda ◽  
...  
Keyword(s):  
Microbial Biomass ◽  
Dehydrogenase Activity ◽  
Nutrient Content ◽  
Crop Productivity ◽  
Maturity Stage ◽  
Microbial Biomass Nitrogen ◽  
Biological Potential ◽  
Initiation Stage ◽  
The Impact

An effective approach for improving nutrient use efficiency and crop productivity simultaneously through exploitation of biological potential for efficient acquisition and utilization of nutrients by crops is very much needed in this current era. Thus, an attempt is made here to investigate the impact of long term fertilization in the soil ecology in rice-rice cropping system in post kharif - 2015 in flooded tropical rice (Oryza sativa L.) in an acidic sandy soil. The experiment was laid out in a randomized block design with quadruplicated treatments. Soil samples at different growth stages of rice were collected from long term fertilizer experiment.The studied long-term manured treatments included 100 % N, 100% NP, 100 % NPK, 150 % NPK and 100 % NPK+FYM (5 t ha-1) and an unmanured control. Soil fertility status like SOC content and other available nutrient content has decreased continuously towards the crop growth period. Comparing the results of different treatments, it was found that the application of 100% NPK + FYM exhibited highest nutrient content in soils. With regards to microbial properties it was also observed that the amount of microbial biomass carbon (MBC) and microbial biomass nitrogen ( MBN) showed highest accumulation in 100 % NPK + FYM at maximum tillering stage of the rice. The results further reveal that dehydrogenase activity was maximum at panicle initiation stage and thereafter it decreases. Soil organic carbon content, MBC, MBN and dehydrogenase activity were significantly correlated with each other. Significant correlations were observed between rice yield and MBC at maturity stage( R2 = 0.94**) and panicle initiation stage( R2 = 0.92**) and available nitrogen content at maturity stage( R2 = 0.91**).

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