scholarly journals Hymenopteran Parasitoids of Aphid Pests within Australian Grain Production Landscapes

Insects ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 44
Author(s):  
Samantha E. Ward ◽  
Paul A. Umina ◽  
Sarina Macfadyen ◽  
Ary A. Hoffmann
Keyword(s):  
Aphid Species ◽  
Crop Growth ◽  
Diaeretiella Rapae ◽  
Field Variation ◽  
Grain Crops ◽  
Growing Seasons ◽  
Crop Type ◽  
Eastern Australia ◽  
Crop Growth Stage ◽  
Hymenopteran Parasitoids

In grain crops, aphids are important pests, but they can be suppressed by hymenopteran parasitoids. A challenge in incorporating parasitoids into Integrated Pest Management (IPM) programs, however, is that parasitoid numbers can be low during periods within the season when aphids are most damaging. Understanding the population dynamics of key aphid species and their parasitoids is central to ameliorating this problem. To examine the composition and seasonal trends of both aphid and parasitoid populations in south-eastern Australia, samples were taken throughout the winter growing seasons of 2017 and 2018 in 28 fields of wheat and canola. Myzus persicae (Sulzer) was the most abundant aphid species, particularly within canola crops. Across all fields, aphid populations remained relatively low during the early stages of crop growth and increased as the season progressed. Seasonal patterns were consistent across sites, due to climate, crop growth stage, and interactions between these factors. For canola, field edges did not appear to act as reservoirs for either aphids or parasitoids, as there was little overlap in the community composition of either, but for wheat there was much similarity. This is likely due to the presence of similar host plants within field edges and the neighbouring crop, enabling the same aphid species to persist within both areas. Diaeretiella rapae (M’Intosh) was the most common parasitoid across our study, particularly in canola, yet was present only in low abundance at field edges. The most common parasitoid in wheat fields was Aphidius matricariae (Haliday), with field edges likely acting as a reservoir for this species. Secondary parasitoid numbers were consistently low across our study. Differences in parasitoid species composition are discussed in relation to crop type, inter-field variation, and aphid host. The results highlight potential focal management areas and parasitoids that could help control aphid pests within grain crops.

scholarly journals Is what you see what you get? The relationship between field observed and actual aphid parasitism rates in canola crops

2021 ◽  
Author(s):  
Samantha Ward ◽  
Paul A. Umina ◽  
Hazel Parry ◽  
Amber Balfour-Cunningham ◽  
Xuan Cheng ◽  
...  
Keyword(s):  
Growth Stage ◽  
Green Peach Aphid ◽  
South Australia ◽  
Field Work ◽  
Crop Growth ◽  
Diaeretiella Rapae ◽  
Growth Stages ◽  
Parasitoid Species ◽  
Parasitism Rates ◽  
Crop Growth Stage

AbstractBACKGROUNDEstimating parasitoid abundance in the field can be difficult, even more so when attempting to quantify parasitism rates and the ecosystem service of biological control that parasitoids can provide. To understand how ‘observed’ parasitism rates (in-field mummy counts) of the green peach aphid, Myzus persicae (Sulzer) (Homoptera: Aphididae) translate to ‘actual’ parasitism rates (laboratory-reared parasitoid counts), field work was undertaken in Australian canola fields over a growing season. Parasitoids were reared within a controlled laboratory setting.RESULTSTotal observed and actual parasitism rates of M. persicae varied considerably across regions, but less so on a field level. Overall, actual parasitism was on average 2.4 times higher than that observed in the field, with rates an average of 4-fold higher in South Australia. As crop growth stage progressed, the percentage of mummies observed increased. Percentage of parasitoids reared also increased with crop growth stage, averaging 3.4% during flowering and reaching 14.4% during podding/senescing. Although there was a greater diversity of reared parasitoid species at later crop growth stages, actual parasitism rate was unaffected by parasitoid species. Diaeretiella rapae was the most commonly reared parasitoid, increasing in abundance with crop growth stage.CONCLUSIONThese findings indicate that mummy counts alone do not provide a clear representation of parasitism within fields.

Growth and Yield of Flax (Linum usitatissimum) Injured by Trifluralin

Weed Science ◽  
1992 ◽  
Vol 40 (3) ◽  
pp. 460-464
Author(s):  
Ken M. Nawolsky ◽  
Ian N. Morrison ◽  
George M. Marshall ◽  
Allen E. Smith
Keyword(s):  
Stem Elongation ◽  
Crop Growth ◽  
Growth And Yield ◽  
Flax Seed ◽  
Growing Seasons ◽  
Matter Production ◽  
Initial Injury ◽  
Net Assimilation ◽  
Bud Initiation ◽  
Number Of Branches

The relationships between the actual amount of spring-applied trifluralin detected in soil at seeding, initial injury to flax, and crop growth and yield were investigated in southern Manitoba over three growing seasons. As the amount of trifluralin in the soil increased, flax density and dry matter production decreased, such that at a soil concentration equivalent to 1 kg ai ha−1trifluralin, the two were reduced by 40 and 49%, respectively. Recovery from early-season injury was characterized by enhanced crop growth rates (CGRs) and net assimilation rates (NARs) of surviving plants during the remainder of the growing season. Maximum recovery occurred in plots where trifluralin levels in the soil were between 0.8 and 1 kg ha−1at seeding. During the interval between stem elongation and bud initiation, CGRs and NARs of flax in the trifluralin-treated plots exceeded those of flax in the untreated plots by up to 1.5 and 1.2 times, respectively. Additionally, the number of branches per plant increased linearly as trifluralin amounts in the soil increased. Flax seed yield was decreased by trifluralin as described by the equation: flax seed (% of untreated control) = 104.9 - 13.3[trifluralin detected (kg ha−1) at seeding]. Based on this equation, trifluralin levels in the soil of up to 0.7 kg ai ha−1caused less than a 5% reduction in flax yield under weed-free conditions.

scholarly journals TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1037-1042 ◽  
Author(s):  
S. A. Sawant ◽  
M. Chakraborty ◽  
S. Suradhaniwar ◽  
J. Adinarayana ◽  
S. S. Durbha
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Stage ◽  
Crop Growth ◽  
Water Requirement ◽  
Growth Stages ◽  
Crop Water ◽  
Horticultural Crops ◽  
Crop Growth Stages ◽  
Crop Growth Stage

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

scholarly journals FIRST LARGE EXTENT AND HIGH RESOLUTION CROPLAND AND CROP TYPE MAP OF ARGENTINA

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 165-169
Author(s):  
D. de Abelleyra ◽  
S. Verón ◽  
S. Banchero ◽  
M. J. Mosciaro ◽  
T. Propato ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Spatially Explicit ◽  
Growing Seasons ◽  
Herbaceous Crops ◽  
Crop Type ◽  
Field Samples ◽  
Additional Challenge ◽  
Seasonal Indices ◽  
Agricultural Areas ◽  
Dynamics Of Vegetation

Abstract. The availability of spatially explicit information about agricultural crops for large regions in Argentina is scarce. In particular, due to temporal dynamics of agricultural production (i.e. changes in planted crops from year to year) and spectral similarities among herbaceous crops it is difficult to generate crop type maps from remote sensing. Large regions with marked climatic variations, like the main agricultural areas of Argentina, represent an additional challenge. Here we generated a map based on supervised classifications using field samples along 14 agricultural zones. Best classification accuracies were obtained by combining seasonal indices (year, summer and winter), with indices that describe the temporal dynamics of vegetation. Accuracy was increased at regions with high and balanced number of samples and with longer growing seasons. The map allows to identify areas with clusters of one, two or three crops and to characterize areas with different spatial distribution between cropland and no cropland areas.

In-season crop classification using optical remote sensing with random forest over irrigated agricultural fields in Australia

2021 ◽  
Author(s):  
Zitian Gao ◽  
Danlu Guo ◽  
Dongryeol Ryu ◽  
Andrew Western
Keyword(s):  
Random Forest ◽  
Water Use ◽  
Landsat 8 ◽  
Irrigation Season ◽  
Yield Estimation ◽  
Cropping Season ◽  
Crop Type ◽  
Eastern Australia ◽  
Crop Classification ◽  
Type Classification

<p>Timely classification of crop types is critical for agronomic planning in water use and crop production. However, crop type mapping is typically undertaken only after the cropping season, which precludes its uses in later-season water use planning and yield estimation. This study aims 1) to understand how the accuracy of crop type classification changes within cropping season and 2) to suggest the earliest time that it is possible to achieve reliable crop classification. We focused on three main summer crops (corn/maize, cotton and rice) in the Coleambally Irrigation Area (CIA), a major irrigation district in south-eastern Australia consisting of over 4000 fields, for the period of 2013 to 2019. The summer irrigation season in the CIA is from mid-August to mid-May and most farms use surface irrigation to support the growth of summer crops. We developed models that combine satellite data and farmer-reported information for in-season crop type classification. Monthly-averaged Landsat spectral bands were used as input to Random Forest algorithm. We developed multiple models trained with data initially available at the start of the cropping season, then later using all the antecedent images up to different stages within the season. We evaluated the model performance and uncertainty using a two-fold cross validation by randomly choosing training vs. validation periods. Results show that the classification accuracy increases rapidly during the first three months followed by a marginal improvement afterwards. Crops can be classified with a User’s accuracy above 70% based on the first 2-3 months after the start of the season. Cotton and rice have higher in-season accuracy than corn/maize. The resulting crop maps can be used to support activities such as later-season system scale irrigation decision-making or yield estimation at a regional scale.</p><p>Keywords: Landsat 8 OLI, in-season, multi-year, crop type, Random Forest</p>

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals The Polarimetric Sensitivity of SMAP-Reflectometry Signals to Crop Growth in the U.S. Corn Belt

2020 ◽  
Vol 12 (6) ◽  
pp. 1007
Author(s):  
Nereida Rodriguez-Alvarez ◽  
Sidharth Misra ◽  
Mary Morris
Keyword(s):  
United States ◽  
Soil Moisture ◽  
The United States ◽  
Crop Growth ◽  
Corn Belt ◽  
Retrieval Algorithm ◽  
Agricultural Crop ◽  
Crop Height ◽  
Crop Type ◽  
The Impact

Crop growth is an important parameter to monitor in order to obtain accurate remotely sensed estimates of soil moisture, as well as assessments of crop health, productivity, and quality commonly used in the agricultural industry. The Soil Moisture Active Passive (SMAP) mission has been collecting Global Positioning System (GPS) signals as they reflect off the Earth’s surface since August 2015. The L-band dual-polarization reflection measurements enable studies of the evolution of geophysical parameters during seasonal transitions. In this paper, we examine the sensitivity of SMAP-reflectometry signals to agricultural crop growth related characteristics: crop type, vegetation water content (VWC), crop height, and vegetation opacity (VOP). The study presented here focuses on the United States “Corn Belt,” where an extensive area is planted every year with mostly corn, soybean, and wheat. We explore the potential to generate regularly an alternate source of crop growth information independent of the data currently used in the soil moisture (SM) products developed with the SMAP mission. Our analysis explores the variability of the polarimetric ratio (PR), computed from the peak signals at V- and H-polarization, during the United States Corn Belt crop growing season in 2017. The approach facilitates the understanding of the evolution of the observed surfaces from bare soil to peak growth and the maturation of the crops until harvesting. We investigate the impact of SM on PR for low roughness scenes with low variability and considering each crop type independently. We analyze the sensitivity of PR to the selected crop height, VWC, VOP, and Normalized Differential Vegetation Index (NDVI) reference datasets. Finally, we discuss a possible path towards a retrieval algorithm based on Global Navigation Satellite System-Reflectometry (GNSS-R) measurements that could be used in combination with passive SMAP soil moisture algorithms to correct simultaneously for the VWC and SM effects on the electromagnetic signals.

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