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8 results for “extensive agriculture”
Fig. 1 in Habitat Preference In Territories Of The Red-Backed Shrike Lanius Collurio And Their Food Richness In An Extensive Agriculture Landscape
Fig. 1. Mean numbers of more important invertebrate taxa of 4–10 mm in length in four types of habitat
WRF-Chem output- concentrations for the 4 simulations: reference, urbanization, extension of agricultural areas and more urban parks
<p>We use the WRF-Chem air-quality model with an urban canopy scheme to investigate how land use (urban, agriculture, parks) affects the urban climate and chemical processes (dry deposition, biogenic emissions), resulting in changes in air quality for scenarios proposed by the Urban Master Plan of the Metropolitan Area of Barcelona. We selected a heatwave period, 4th-7th July 2015, with maximum temperatures reaching 40 °C in the interior of the AMB and 35 °C on the coast. Here we provide averaged model output for the heatwave period for the different pollutants, surface temperature, wind speed, PBLH, deposition, and biogenic emissions under different urban scenarios: reference (REF), urbanization (URB), extension of agriculture (AGR), and increase in urban parks (PARK).<br><br>The results for a period before the heatwave (1-3 July 2015), with temperatures 3-4 degrees lower than during the heatwave, are also provided. <br><br>Land-use and irrigation is also provided for each scenaio. </p>
Fig. 3 in Habitat Preference In Territories Of The Red-Backed Shrike Lanius Collurio And Their Food Richness In An Extensive Agriculture Landscape
Fig. 3. Mean numbers of more important invertebrate taxa longer than 10 mm in four types of habitat
Fig. 2 in Habitat Preference In Territories Of The Red-Backed Shrike Lanius Collurio And Their Food Richness In An Extensive Agriculture Landscape
Fig. 2. Comparison of invertebrate biomass 4–10 mm in length in four types of habitat
Fig.4 in Habitat Preference In Territories Of The Red-Backed Shrike Lanius Collurio And Their Food Richness In An Extensive Agriculture Landscape
Fig.4. Comparison of invertebrate biomass longer than 10 mm in four types of habitat
Data from: Nectar resource limitation affects butterfly flight performance and metabolism differently in intensive and extensive agricultural landscapes
Flight is an essential biological ability of many insects, but is energetically costly. Environments under rapid human-induced change are characterized by habitat fragmentation and may impose constraints on the energy income budget of organisms. This may, in turn, affect locomotor performance and willingness to fly. We tested flight performance and metabolic rates in Meadow brown butterflies (Maniola jurtina) of two contrasted agricultural landscapes: intensively managed, nectar-poor (IL) versus extensively managed, nectar-rich landscapes (EL). Young female adults were submitted to four nectar treatments (i.e. nectar quality and quantity) in outdoor flight cages. IL-individuals had better flight capacities in a flight mill and had lower resting metabolic rates (RMR) than EL-individuals, except under the severest treatment. Under this treatment, RMR increased in IL-individuals, but decreased in EL-individuals; flight performance was maintained by IL-individuals, but dropped by a factor 2.5 in EL-individuals. IL-individuals had more canalized (i.e. less plastic) responses relative to the nectar treatments than EL-individuals. Our results show significant intraspecific variation in the locomotor and metabolic response of a butterfly to different energy income regimes relative to the landscape of origin. Ecophysiological studies help improving our mechanistic understanding of the eco-evolutionary impact of anthropogenic environments on rare and widespread species.
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.
Data from: Nectar resource limitation affects butterfly flight performance and metabolism differently in intensive and extensive agricultural landscapes
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