PGPR formulations and application in the management of pulse crop health

Pulse crop seed coats are a sustainable source of antioxidant polyphenols, but are typically treated as low-value products, partly because some polyphenols reduce iron bioavailability in humans. This study correlates antioxidant/iron chelation capabilities of diverse seed coat types from five major pulse crops (common bean, lentil, pea, chickpea and faba bean) with polyphenol composition using mass spectrometry. Untargeted metabolomics was used to identify key differences and a hierarchical analysis revealed that common beans had the most diverse polyphenol profiles among these pulse crops. The highest antioxidant capacities were found in seed coats of black bean and all tannin lentils, followed by maple pea, however, tannin lentils showed much lower iron chelation among these seed coats. Thus, tannin lentils are more desirable sources as natural antioxidants in food applications, whereas black bean and maple pea are more suitable sources for industrial applications. Regardless of pulse crop, proanthocyanidins were primary contributors to antioxidant capacity, and to a lesser extent, anthocyanins and flavan-3-ols, whereas glycosylated flavonols contributed minimally. Higher iron chelation was primarily attributed to proanthocyanidin composition, and also myricetin 3-O-glucoside in black bean. Seed coats having proanthocyanidins that are primarily prodelphinidins show higher iron chelation compared with those containing procyanidins and/or propelargonidins.

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Type of pulse crop included in a 2-year rotation with wheat affects total N2O loss and intensity

Biology and Fertility of Soils ◽

10.1007/s00374-021-01562-4 ◽

2021 ◽

Author(s):

Liting Liu ◽

J. Diane Knight ◽

Reynald L. Lemke ◽

Richard E. Farrell

Keyword(s):

Pulse Crop

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Climate Change Patterns of Wild Blueberry Fields in Downeast, Maine over the Past 40 Years

Water ◽

10.3390/w13050594 ◽

2021 ◽

Vol 13 (5) ◽

pp. 594

Author(s):

Rafa Tasnim ◽

Francis Drummond ◽

Yong-Jiang Zhang

Keyword(s):

Climate Change ◽

Vegetation Index ◽

Potential Evapotranspiration ◽

Growing Season ◽

Spatial Average ◽

Climate Variables ◽

Threshold Values ◽

The Past ◽

Wild Blueberry ◽

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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Genomic Resources and Omics-Assisted Breeding Approaches for Pulse Crop Improvement

Pulse Improvement ◽

10.1007/978-3-030-01743-9_2 ◽

2018 ◽

pp. 13-55 ◽

Cited By ~ 2

Author(s):

Javaid Akhter Bhat ◽

S. M. Shivaraj ◽

Sajad Ali ◽

Zahoor Ahmad Mir ◽

Aminul Islam ◽

...

Keyword(s):

Crop Improvement ◽

Genomic Resources ◽

Pulse Crop

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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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Integration of Various Stability Models to Identify High Yielding and Stable Genotypes of Pigeonpea [Cajanus cajan (L.) Millspaugh]

Legume Research - An International Journal ◽

10.18805/lr-4520 ◽

2021 ◽

Author(s):

Amit Kumar Gaur ◽

S.K. Verma ◽

R.K. Panwar ◽

Anju Arora

Keyword(s):

Cluster Analysis ◽

Seed Yield ◽

Hierarchical Cluster Analysis ◽

Cajanus Cajan ◽

Block Design ◽

Hierarchical Cluster ◽

Interaction Effects ◽

Kharif Season ◽

Pulse Crop ◽

Randomized Block Design

Background: Pigeonpea is second most important pulse crop of India after chickpea and it is necessary to identify its high yielding and stable genotypes to feed the increasing population of country. Methods: The present study was laid down in a Randomized Block Design with three replications during kharif season of 2016-2019 at GBPUAT, Pantnagar using twenty genotypes of pigeonpea with an aim to identify the high yielding and stable genotypes. The hierarchical cluster analysis (HCA) based on mean seed yield and Average of Sum of Ranks (ASR) of all measures (Parametric and non-parametric) was used in present study.Result: The pooled ANOVA revealed the presence of significant differences among genotypes, environments and G x E interaction effects. The results of hierarchical cluster analysis indicated the genotypes PA 622 (yield=1774.85 kg/ha, ASR=2.00), PA 620 (yield= 1579.92 kg/ha, ASR=2.18), UPAS 120 (yield=1268.57 kg/ha, ASR=2.87), PA 626 (yield=1571.40 kg/ha, ASR=5.56), PUSA 992 (yield= 1331.17 kg/ha, ASR=5.68) and PA 628 (yield= 1271.50 kg/ha, ASR=7.06) as most stable and high yielding and hence these genotypes can be recommended for pigeonpea improvement programmes.

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