Agroecological Engineering to Biocontrol Soil Pests for Crop Health

Maine, USA is the largest producer of wild blueberries (Vaccinium angustifolium Aiton), an important native North American fruit crop. Blueberry fields are mainly distributed in coastal glacial outwash plains which might not experience the same climate change patterns as the whole region. It is important to analyze the climate change patterns of wild blueberry fields and determine how they affect crop health so fields can be managed more efficiently under climate change. Trends in the maximum (Tmax), minimum (Tmin) and average (Tavg) temperatures, total precipitation (Ptotal), and potential evapotranspiration (PET) were evaluated for 26 wild blueberry fields in Downeast Maine during the growing season (May–September) over the past 40 years. The effects of these climate variables on the Maximum Enhanced Vegetation Index (EVImax) were evaluated using Remote Sensing products and Geographic Information System (GIS) tools. We found differences in the increase in growing season Tmax, Tmin, Tavg, and Ptotal between those fields and the overall spatial average for the region (state of Maine), as well as among the blueberry fields. The maximum, minimum, and average temperatures of the studied 26 wild blueberry fields in Downeast, Maine showed higher rates of increase than those of the entire region during the last 40 years. Fields closer to the coast showed higher rates of warming compared with the fields more distant from the coast. Consequently, PET has been also increasing in wild blueberry fields, with those at higher elevations showing lower increasing rates. Optimum climatic conditions (threshold values) during the growing season were explored based on observed significant quadratic relationships between the climate variables (Tmax and Ptotal), PET, and EVImax for those fields. An optimum Tmax and PET for EVImax at 22.4 °C and 145 mm/month suggest potential negative effects of further warming and increasing PET on crop health and productivity. These climate change patterns and associated physiological relationships, as well as threshold values, could provide important information for the planning and development of optimal management techniques for wild blueberry fields experiencing climate change.

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Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

10.5194/egusphere-egu21-15589 ◽

2021 ◽

Author(s):

Brianna Pagán ◽

Adekunle Ajayi ◽

Mamadou Krouma ◽

Jyotsna Budideti ◽

Omar Tafsi

Keyword(s):

Machine Learning ◽

Satellite Imagery ◽

Cloud Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Learning Approaches ◽

Area Index ◽

Spatial And Temporal Scales ◽

Field Level ◽

Crop Health

The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level.&#160;Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.&#160;

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Soil and Crop Health Management for the Cultivation of Pigeon Pea: An Overview of Management Practices

Fungal Biology - Management of Fungal Pathogens in Pulses ◽

10.1007/978-3-030-35947-8_9 ◽

2020 ◽

pp. 143-167 ◽

Cited By ~ 1

Author(s):

Christy B. K. Sangma

Keyword(s):

Management Practices ◽

Health Management ◽

Pigeon Pea ◽

Crop Health

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Pengaruh Sifat Fisik Tanah terhadap Hama Simphylid Pada tanaman Nanas (Ananas comosus (L.) Merr) di PT. Great Giant Pineapple Terbanggi Besar Lampung Tengah

Jurnal Penelitian Pertanian Terapan ◽

10.25181/jppt.v15i3.134 ◽

2017 ◽

Vol 15 (3) ◽

Author(s):

Sandi Aji ◽

Afandi Afandi ◽

Lestari Wibowo ◽

K.E.S. Manik

Keyword(s):

Physical Properties ◽

High Hardness ◽

Soil Physical Properties ◽

Soil Sampling ◽

Soil Science ◽

Ananas Comosus ◽

Survey Method ◽

Soil Pests ◽

Poor Soil ◽

Properties Of Soil

This research was conducted in the planting area of pineapple (Ananas comosus) PT. GGP Terbanggi Besar Central Lampung indicated attacked by pests simphylid in March 2014 until May 2014. Analysis of soil physical properties carried out in the Laboratory of Soil Science, Department of Agrotechnology, Faculty of Agriculture, University of Lampung. The method used in this study is a survey method. Soil sampling conducted at three locations indicated simphylid pests. Soil sampling done at some point and some depth. Results from this research that pest symphilid most numerous in one location with a number of acquisition 172 tail where the location of the physical properties of good land which the density value of the content is low, the total pore low, macropores and high hardness low ground , allowing sinphylid be able to live and thrive. While at the location of two and three with the condition density value of the content is high, the total pore high, macropores low, and violence high soil pests simphylid not so much discovered as simphylid can not multiply and survive on the physical condition of poor soil Keywords: Pineapple, Symphilid, and physical properties of soil

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The phytosanitary status of sunflower crops оf north-eastern Forest-Steppe of Ukraine

Bulletin of Sumy National Agrarian University. The series: Agronomy and Biology ◽

10.32845/agrobio.2019.4.1 ◽

2019 ◽

pp. 3-7

Author(s):

V.M. Demenko ◽

O.L. Golinach ◽

V.A. Vlasenko

Keyword(s):

Sharp Increase ◽

Crop Rotations ◽

Forest Steppe ◽

Economic Threshold ◽

Soil Pests ◽

Darkling Beetle ◽

Melolontha Melolontha ◽

Click Beetle ◽

North Eastern ◽

Brachycaudus Helichrysi

The high economic efficiency of sunflower growing contributed to a sharp increase in the sunflower planting acreage in Sumy region. The increase of cultivated areas under sunflower resulted in an oversaturation of crop rotations with this crop. The study of the phytosanitary status of sunflower crops was carried out in the basic farms of the phytosanitary security department of the Main Office of State Consumer Service (Derzhprodsluzhba) in Sumy region. The research methodology was commonly accepted. The main pests of sunflower crops were grey beet weevil (Tanymecus palliates Fabr.), larvae of common click beetle (Agriotes sputator L.), darkling beetle (Opatrum sabulosum L.), larvae of the western may beetle (Melolontha melolontha L.), leafcurl plum aphid (Brachycaudus helichrysi Kalt). The sunflower seedlings were damaged grey beet weevil, darkling beetle. The most widespread soil pests were the larvae of the western may beetle and larvae of common click beetle. Leafcurl plum aphid populated sunflower crops with 6‒8 pairs of true leaves. It continued to spread across the field during the inflorescence stage and the stage of initial blossom. The highest pest colonization was observed at the edge of the field in 2015, 2017 and accounted for 16 % of the plants. In the middle of the field, the aphid colonization was lower than at the edge. During the years of research, the economic threshold of sunflower pest harmfulness was exceeded only in some years. Sunflower damage by grey weevil beet, larvae of common click beetle, darkling beetle, larvae of the western may beetle was weak, and their number was insignificant. The increase of sunflower acreage did not lead to a significant growth of pest number, the exceeding of economic threshold of their harmfulness.

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