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Figure 6 in An exploratory study of bumble bee (Bombus) phenologies and plant interactions in agricultural landscapes in central Georgia, USA

Figure 6. Average weekly captures of Bombus bimaculatus castes across three sites in 2010 near Athens, Georgia, USA. No individuals of this species were captured during the week of 21 June. No surveys were conducted during the week of 28 June due to a lack of available person-hours.

Figure 9 in An exploratory study of bumble bee (Bombus) phenologies and plant interactions in agricultural landscapes in central Georgia, USA

Figure 9. Average captures per week of Bombus griseocollis castes across three sites in 2010 near Athens, Georgia, USA. No surveys were conducted during the week of 28 June due to a lack of available person-hours. No individuals of this species were captured during the week of 26 July.

Figure 8 in An exploratory study of bumble bee (Bombus) phenologies and plant interactions in agricultural landscapes in central Georgia, USA

Figure 8. Caste percentages of total Bombus captures across three sites near Athens, Georgia, USA, in 2010. Bb = Bombus bimaculatus, Bg = B. griseocollis and Bi = B. impatiens. Q = queen, W = worker and M = male castes. Total captures = 1956; Bb total captures = 228, Bg total captures = 529, Bi total captures = 1058.

Figure 5 in An exploratory study of bumble bee (Bombus) phenologies and plant interactions in agricultural landscapes in central Georgia, USA

Figure 5. Average weekly captures of Bombus bimaculatus (Bb), B. griseocollis (Bg), B. impatiens (Bi) and B. a-f-p group (Bafp) across three sites in 2010 near Athens, Georgia, USA. No surveys were conducted during the weeks of 28 June or 6 September due to a lack of available person-hours, or the weeks of 16–30 August due to inclement weather and/or an insufficient number of bees observed at all sites. No B. bimaculatus individuals were captured during the week of 21 June. No B. griseocollis individuals were captured during the week of 26 July. Weekly average captures for B. impatiens are underrepresented during the following weeks due to insufficient supplies: 14 June, 12 July, 2 August, 9 August, 13 September, 4 October.

Figure 7 in An exploratory study of bumble bee (Bombus) phenologies and plant interactions in agricultural landscapes in central Georgia, USA

Figure 7. Caste percentages of total captures from each Bombus species across three sites near Athens, Georgia, USA, in 2010. Bb = Bombus bimaculatus, Bg = B. griseocollis and Bi = B. impatiens. Q = queen, W = worker and M = male castes. Bb total captures = 228, Bg total captures = 529, Bi total captures = 1058.

Figure 10 in An exploratory study of bumble bee (Bombus) phenologies and plant interactions in agricultural landscapes in central Georgia, USA

Figure 10. Average captures per week of Bombus impatiens castes across three sites in 2010 near Athens, Georgia, USA. No individuals of this species were captured during the weeks of 12 April, 19 April, 17 May, 20 September, 27 September. No surveys were conducted during the week of 28 June or 6 September due to a lack of available person-hours, or the 16–30 weeks of August due to inclement weather or lack of sufficient bumble bee observations. Average captures are underrepresented during the following weeks due to limited supplies: 14 June, 12 July, 2 August, 9 August, 13 September, 4 October.

Figure 11 in An exploratory study of bumble bee (Bombus) phenologies and plant interactions in agricultural landscapes in central Georgia, USA

Figure 11. Weekly average captures of Bombus bimaculatus (Bb), B. griseocollis (Bg), B. impatiens (Bi), and B. a-f-p group (Bombus auricomus-pensylvanicus-fraternus, Bafp) across two sites in 2011 near Athens, Georgia, USA. Only one site could be surveyed during the weeks of 22 March–5 April, so total captures are shown for those two weeks. No surveys were conducted during the weeks of 30 May– 13 June due to a lack of available person-hours; or during the weeks of 9–23 May due to inclement weather and/or lack of sufficient bumble bee observations at both sites. Bombus were absent from both sites during the weeks of 23 June–29 September. Surveys were not conducted during the week of 3 October, but the date is included for comparison with Figure 5.

opencc-zeroAug 2022View details →

Agriculture is adapting to phenological shifts caused by climate change, but grassland songbirds are not

Migratory birds time their migration based on cues that signal resource availability for reproduction. However, with climate change, the timing of seasonal events may shift, potentially inhibiting the ability of some species to use them as accurate cues for migration. We studied the relationship between phenological shifts and reproduction by long- and short-distance migratory songbirds—Bobolinks (Dolichonyx oryzivorus) and Savannah Sparrows (Passerculus sandwichensis). Our study population breeds in hayfields and pastures in Vermont, USA, where farmers are also changing management activities in response to climate change. From 2002-2019 we monitored nest initiation dates to quantify correlations with environmental factors and the timing of nest initiation. We collected historical and projected precipitation and temperature data for the breeding grounds, and their respective wintering and stopover sites, the North Atlantic Oscillation (NAO) and the El Niño Southern Oscillation (ENSO). We predicted that winter conditions experienced by the short-distance migrant, the Savannah Sparrow, but not the long-distance migrant, the Bobolink, would explain the timing and success of nesting, however that this timing would be misaligned with changes in agricultural practices by hay farmers. Nest initiation dates did not show significant directional change for either species, but did vary among years. Interannual variation in Savannah Sparrow nest initiation dates was best explained by the interaction between precipitation on the breeding grounds and average wintering site (Wilmington, North Carolina). For Bobolinks, interannual variation in nest initiation dates was best explained by the interaction between breeding ground precipitation and average temperature in their fall stopover site (Barquisimieto, Venezuela). However, first haying dates in Vermont advanced by ~10 days over 18 years. These results suggest that the conflict between the timing of hay harvests and grassland songbird reproduction will increase, further threatening population processes for these species, as early harvests notably decrease annual productivity.

opencc-zeroSep 2022View details →

Grasslands enhance ecosystem service multifunctionality above and below ground in agricultural landscapes

1. Managing agricultural landscapes that can integrate production, biodiversity conservation and the flow of ecosystem services (ES) is of paramount importance to simultaneously meet production goals and environmental challenges. However, the response of farmland biodiversity and multiple ES to land-use change at multiple spatial scales remains poorly understood. 2. We explored the effects of land use at local (grassland vs. oilseed rape fields) and landscape scale (cover of permanent grasslands) on the provision of biodiversity (plants, arthropods, birds), five ES (pollination, pest control, soil fertility, carbon storage and water regulation) and overall ES-multifunctionality. 3. ES-multifunctionality was higher in grasslands than in crop fields, by 25.2% above ground and by 106.1% below ground. Multiple threshold analyses highlighted a particularly poor level of performance for belowground functions in crop fields. This habitat type was however capable of providing numerous aboveground functions simultaneously, although at low levels of performance when compared to the maximum values recorded in the study. Grasslands supported higher biodiversity and provision of pollination, soil fertility, carbon storage and water regulation. 4. Landscape composition influenced the provision of multiple ES: a 10% increase in grassland cover in the landscape enhanced aboveground ES-multifunctionality by 11.0% in both habitats. In particular, grasslands cover in the landscape supported the provision of arthropod diversity, pollination and pest control provided by carabids. 5. Synthesis and applications: The results of this field study show the key importance of preserving seminatural grasslands in agricultural landscapes for the conservation of farmland biodiversity, for the protection of soils and the delivery of multiple ecosystem services critical for crop production. Maximization of multifunctionality necessitates the integration at the landscape scale (0.5-2 km) of seminatural patches within the intensively farmed agricultural matrix. This would require not only the protection of existing grasslands, but also their restoration in simplified landscapes. The promotion of mixed farming (i.e., both crop and livestock production) might increase semi-natural grassland cover at the landscape scale.

opencc-zeroSep 2022View details →

The impacts of tropical agriculture on biodiversity: A meta-analysis

1. Biodiversity underpins all food production and strengthens agricultural resilience to crop failure. However, agricultural expansion is the primary driver of biodiversity loss, particularly in the tropics where crop production is increasing and intensifying rapidly to meet a growing global food demand. It is therefore crucial to ask, how do different crops and crop production systems impact biodiversity? 2. We first use the FAO database of harvested crop area to explore temporal changes in crop area and intensification across the entire tropical realm. We show that the harvested area of tropical crops has more than doubled since 1961, with ever-increasing intensification. The harvested area in 2019 was 7.21 million km2, equivalent to 5.5% of global ice-free land area, or 11.5% of land area in the tropics. 3. Second, we conducted a meta-analysis of 194 studies and 1,364 pairwise comparisons to assess the impact of tropical agriculture on biodiversity, comparing biodiversity values in food crop sites versus natural reference habitats. 4. Our meta-analysis shows that crop type, rotation time and level of shading are important determinants of biodiversity assemblages. Perennial tropical crops that are grown in shaded plantations or agroforests (e.g., banana and coffee) support higher biodiversity, while crops cultivated in unshaded and often homogeneous croplands (e.g., maize, sugarcane, and oil palm), and particularly annual crops, have impoverished biodiversity communities. 5. Policy implications: Our findings highlight the increasing agricultural expansion and intensification over the last sixty years and inform our understanding of how different crops and crop production systems impact biodiversity. Furthermore, they provide insight into the long-term sustainability of tropical food production and may serve as a warning sign for agricultural systems that rely on the ecosystem services provided by biodiversity.

opennotspecifiedJan 2018View details →

In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.

In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.

opencc-by-4.0Apr 2022View details →

Data for global agricultural water scarcity assessment incorporating blue and green water availability under future climate change

This dataset is for the publication Global agricultural water scarcity assessment incorporating blue and green water availability under future climate change by Liu et al., 2022 (Earth's Future, doi: <a href="http://doi.org/10.1029/2021EF002567">10.1029/2021EF002567</a>). Three observation-based global meteorological datasets, namely PGMFD v.2, GSWP3, and WFDEI, were used to calculate ETc over the baseline period. The bias-corrected climate projections of four GCMs (namely GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) provided by the ISIMIP phase 2b (ISIMIP2b) were used to calculate the ETc over the future period.   Liu, X., Liu, W., Tang, Q., Liu, B., Wada, Y., & Yang, H. (2022). Global agricultural water scarcity assessment incorporating blue and green water availability under future climate change. Earth's Future, 10, e2021EF002567. <a href="https://doi.org/10.1029/2021EF002567">https://doi.org/10.1029/2021EF002567</a>

opencc-by-4.0May 2024View details →

Promoting conservation agriculture through push-pull technology as an agroecological transition.

Abstract  Low soil fertility, weeds, pests, and climatic change severely threaten crop productivity and agricultural  sustainability, especially in SSA. Despite decades of research finding adequate technical solutions for most  situations of food systems, the problem of low food productivity has persisted. In an effort to counter this,  intensive agricultural systems have been mooted including the high application of agrochemicals to control  weeds, and pests and increase production. However, these initiatives have not lasted beyond the project cycle  and they have instigated land degradation through unsustainable practices. As a solution, conservation  agricultural practices have been promoted among small-scale farmers. These practices focus on minimizing soil  disturbance, crop diversification, and cover cropping. Push-pull technology is an aspect of conservation  agriculture where intercropping a cereal crop with a repellent plant, such as desmodium and planting an  attractive trap plant, such as brachiaria or Napier grass as a border crop around this intercrop. This paper aimed  at reviewing existing literature to establish the linkage between conservation agriculture components, push-pull  technology, and a sustainable agroecological transition. A list of questions directed the discussion where push pull technology has been proven to be an aspect that promotes conservation agriculture. It has been able to  increase crop yields, reduced tillage, established a cover crop on the farm, and further, PPT has a regenerative  aspect through the integration of livestock husbandry providing organic manure that and together with the  nitrogen fixation ability of the grass, improve soil fertility, conserved soil moisture, and reduce erosion. This  reduces the use of inorganic input, and machinery making farming economical for small-holder farmers.

opencc-by-4.0Feb 2024View details →

Water, Dust, and Environmental Justice: The Case of Agricultural Water Diversions - Data

Replication files and code for paper "Water, Dust, and Environmental Justice: The Case of Agricultural Water Diversions".

opencc-by-4.0May 2024View details →

Adoption of Climate-Smart Agricultural Practices and Its Impact on Crop Productivity: A Case Study of Smallholder Farmers in Nyimba District, Zambia

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opencc-by-sa-4.0May 2024View details →

IoT Agriculture 2014

<h3>Data Sources with Authors</h3> In the master's thesis research conducted by student Mohammed Ismail Lifta (2023-2024) at the Department of Computer Science, College of Computer Science and Mathematics- Tikrit University,Iraq. Data was collected from a smartly-equipped greenhouse. The study was supervised by Assistant Professor Wissam Dawood Abdullah, Director of the Cisco Networking Academy at Tikrit University. It involved the construction of a smart greenhouse equipped with advanced technologies for monitoring and controlling environmental conditions. The study included an application that links data to Google Sheets for remote monitoring and control, providing an effective platform for efficient management of the greenhouse. ( 13 features , 37923 Row) <h3>Columns and Data Types:</h3> date (datetime64): The date and time the measurements were recorded. temperature (int64): The recorded temperature in degrees Celsius. humidity (int64): The percentage of humidity in the environment. water_level (int64): The water level as a percentage. N (int64): The nitrogen level in the soil, scaled from 0 to 255. P (int64): The phosphorus level in the soil, scaled from 0 to 255. K (int64): The potassium level in the soil, scaled from 0 to 255. Fan_actuator_OFF (float64): Indicator for the fan actuator if it is off (0 or 1). Fan_actuator_ON (float64): Indicator for the fan actuator if it is on (0 or 1). Watering_plant_pump_OFF (float64): Indicator for the plant watering pump if it is off (0 or 1). Watering_plant_pump_ON (float64): Indicator for the plant watering pump if it is on (0 or 1). Water_pump_actuator_OFF (float64): Indicator for the water pump actuator if it is off (0 or 1). Water_pump_actuator_ON (float64): Indicator for the water pump actuator if it is on (0 or 1). <h3>Additional Details:</h3> The data was cleaned by removing duplicate rows and missing values. Categorical columns were encoded using One-Hot Encoding technique to facilitate the use of the data in machine learning. The file is ready for analysis and modeling using machine learning tools. <h3>How to Use</h3> This data can be used for environmental research and studies. Proper attribution must be given when using this data in any publication.No Change the dataset. <h3>Contact</h3> For more information or inquiries, please contact the principal researcher: Professor ( Assistant) Wisam Dawood Abdullah (Email: wisamdawood@tu.edu.iq).

opencc-zeroMay 2024View details →

Food production and biodiversity are not incompatible in temperate heterogeneous agricultural landscapes

We need landscape-scale approaches to design and manage agro-ecosystems that can sustain both agricultural production and biodiversity conservation. In this study, yield figures provided by 299 farmers served to quantify the energy-equivalents of food production across different crops in 49 1-km2 landscapes. Our results show that the relationship between bird diversity and food energy production depends on the proportion of farmland within the landscape, with a negative correlation observed in agriculture dominated landscapes (≥ 64–74% farmland). In contrast, neither typical farmland birds nor butterflies showed any significant relationship with total food energy production. We conclude that in European temperate regions consisting of small-scale, mixed farming systems (arable and livestock production), productivity and biodiversity conservation may not be purely antagonistic, particularly when (semi-)natural habitats make up a large fraction of the landscape (≥ 20%).

opencc-by-4.0May 2024View details →

DRAM raw annotations for "Cover Crop Root Exudates Impact Soil Microbiome Functional Trajectories in Agricultural Soils" Seitz et al 2024

Additional File 5: Raw DRAM MAG annotations.

opencc-by-4.0Jun 2024View details →

Data related to: Global land-water competition and synergy between solar energy and agriculture

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