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Figure 4 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province

Figure 4. Reasons for initial engagement (n = 71) in volunteering and the current motivations (n = 86) for volunteers to be involved in the management of invasive alien plant species in Western Cape, South Africa.

Figure 2 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province

Figure 2. Motivations (n = 35) for forming volunteer groups that remove alien invasive plants in Western Cape, South Africa.

Figure 1 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province

Figure 1. Identified volunteer groups (52) in Western Cape of South Africa. Groups that participated in the survey (26) are indicated by circles that also show group sizes (individual members per group). Groups that did not participate in the survey are indicated by blue circles. The green area on the map represents the fynbos biome.

Figure 3 in Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa's Western Cape province

Figure 3. Challenges (n = 26) faced by volunteering by groups in the management of invasive alien plants in Western Cape, South Africa.

Figure 5 in Shifts in ground-dwelling predator communities in response to changes in management intensity in Alpine meadows

Figure 5. Proportions and χ2-test results for the ecological species traits moisture, rarity and ecological tolerance of ground-dwelling predatory arthropods (Arachnida, Carabidae, Staphylinidae, Formicidae) from extensively and intensively managed hay meadows in South Tyrol, Italy.

Figure 4 in Shifts in ground-dwelling predator communities in response to changes in management intensity in Alpine meadows

Figure 4. Non-metric multidimensional scaling (NMDS) of the full species community of predatory invertebrates, including the two treatments (intensive and extensive) and the two seasons (spring and autumn). Each spot represents one pitfall trap. Spider web centres represent the weighted centroids of each management type.

Figure 3 in Shifts in ground-dwelling predator communities in response to changes in management intensity in Alpine meadows

Figure 3. Abundance based accumulation curves for predatory arthropods based on Hill numbers N0 and N1 confronting extensively and intensively used montane hay meadows in South Tyrol, Italy.

Figure 2 in Shifts in ground-dwelling predator communities in response to changes in management intensity in Alpine meadows

Figure 2. The mean (and 95 % confidence interval) activity density (individuals per sampling day), species richness, and exponential Shannon diversity of ground-dwelling predatory arthropods from montane extensively and intensively used hay meadows and two sampling seasons (spring and autumn) in South Tyrol, Italy. No significant effect of management was detected for any biodiversity index.

Figure 1 in Shifts in ground-dwelling predator communities in response to changes in management intensity in Alpine meadows

Figure 1. Maps of the distribution of the six selected hay meadows (EH = extensively used hay meadows; IH = intensively used hay meadows) located in Barbian/Barbiano in the Autonomous Province South Tyrol, Italy.

opencc-by-4.0Feb 2024View details →

Annual crop-specific management history of phosphorus fertilizer input (CMH-P) in the croplands of United States from 1850 to 2022: Application rate, timing, and method

This dataset presents spatiotemporal dynamics of phosphorus (P) fertilizer management (application rate, timing, and method) at a 4km × 4 km resolution in agricultural land of the contiguous U.S. from 1850 to 2022. By harmonizing multiple data sources, we reconstructed the county-level crop-specific P fertilizer use history. We then spatialized and resampled P fertilizer use data to 4 km × 4 km gridded maps based on historical U.S. cropland distribution and crop type database developed by Ye et al. (2024). This dataset contains (1) P fertilizer total consumption and mean application rate at the national level (Tabular); (2) P fertilizer consumption of 11 crops at the state level (Tabular); (3) P fertilizer consumption of permanent pasture (Tabular); (4) P fertilizer consumption of non-farm at the state level (Tabular); (5) P fertilizer application rate of 11 crop types at the state level (Tabular); (6) P fertilizer application rate of 11 crop types at the county level (Tabular); (7) P fertilizer application timing ratio at the state level (Tabular); (8) P fertilizer application method ratio at the state level (Tabular); (9) Gridded maps of P fertilizer application rate based on state-level data; (10) and (11) Gridded maps of P fertilizer application rate based on county-level data; (12)-(20) Gridded maps of P fertilizer application rate for each crop. A detailed description of the data development processes, key findings, and uncertainties can be found in Cao, P., Yi, B., Bilotto, F., Gonzalez Fischer, C., Herrero, M., Lu, C.: Crop-specific Management History of Phosphorus fertilizer input (CMH-P) in the croplands of United States: Reconciliation of top-down and bottom-up data sources, is under review for the journal Earth System Science Data (ESSD). https://essd.copernicus.org/preprints/essd-2024-67/#discussion.  This work is supported by the Iowa Nutrient Research Center, the ISU College of Liberal Arts and Sciences Dean's Faculty Fellowship, and NSF CAREER grant (1945036).

opencc-by-4.0Aug 2024View details →

Dataset on THE INFLUENCE OF INNOVATION, KNOWLEDGE MANAGEMENT, AND E-COMMERCE ADOPTION ON MSME PERFORMANCE, AND ITS IMPACT ON MSMEs SUSTAINABILITY

Dataset on THE INFLUENCE OF INNOVATION, KNOWLEDGE MANAGEMENT, AND E-COMMERCE ADOPTION ON MSME PERFORMANCE, AND ITS IMPACT ON MSMEs SUSTAINABILITY

opencc-by-4.0Sep 2021View details →

Fig. 2 in Trapping soybean looper (Lepidoptera: Noctuidae) in the southeastern USA and implications for pheromone-based research and management

Fig. 2. Mean number of Ctenoplusia oxygramma male moths captured at each of 3 trial locations where they were recorded as present. Note: Bio Pseudoplusia lures were not used at the LA-Crowley location, were installed at the LA-Ben Hur location on 2 Aug 2019, and were installed at the FL-Jay location for the entire trial period.

opencc-by-4.0Sep 2021View details →

Fig. 1 in Re-examination of morphological variations in the female internal genitalia of Helicoverpa armigera and Helicoverpa zea (Lepidoptera: Noctuidae) for identification and pest management

Fig. 1. (A) Female genitalia of Helicoverpa armigera. (B) Texture on appendix bursa of H. armigera. (C) Luminal surface on appendix bursa of H. armigera. (D) Female genitalia of Helicoverpa zea. (E) Texture on appendix bursa of H. zea. (ab) appendix bursa; (bc) bursa copulatrix; (cb) corpus bursa; (db) ductus bursa; (ob) ostium bursa; (pa) pigmented area.

Fig. 1 in Management of stemborers (Lepidoptera: Crambidae) using foliar-applied chlorantraniliprole in rice

Fig. 1. Whitehead density as affected by insecticide treatment applied 35 d (2018) and 22 and 33 d (2019) afer permanent flood (PF) was established, Crowley, Louisiana, USA. CAP = chlorantraniliprole. Bars within graphs accompanied by the same letter do not differ significantly (P> 0.05, Tukey's HSD).

Fig. 4 in Management of stemborers (Lepidoptera: Crambidae) using foliar-applied chlorantraniliprole in rice

Fig. 4. Whitehead density as affected by insecticide treatment (A) and crop type (B), Eagle Lake, Texas, USA, 2019. ST = for seed treatment, whereas CAP = chlorantraniliprole. Except for seed treatment, the remainder of the treatments were applied at permanent flood. Bars within graphs accompanied by the same letter do not differ significantly (P> 0.05, Tukey's HSD).

Fig. 1 in Efficacy of a biopesticide and predatory mite to manage chilli thrips, Scirtothrips dorsalis Hood (Thysanoptera: Thripidae) in strawberry

Fig. 1. Mean (± SE) adult and larval Scirtothrips dorsalis Hood per 3 strawberry leaflets on caged strawberry plants treated with (1) Capsicum oleoresin based biopesticide, (2) predatory mite Amblyseius swirskii, and (3) conventional insecticide spinetoram, and compared with control plants for 7, 14, 21, and 28 d afer treatment. Means with the same letter are not significantly different (P <0.05; Tukey HSD).

Fig. 2 in Efficacy of a biopesticide and predatory mite to manage chilli thrips, Scirtothrips dorsalis Hood (Thysanoptera: Thripidae) in strawberry

Fig. 2. Mean (± SE) plant damage rating caused by feeding of Scirtothrips dorsalis Hood on caged strawberry plants treated with (1) Capsicum oleoresin based biopesticide, (2) predatory mite Amblyseius swirskii, and (3) conventional insecticide spinetoram, and compared with control plants for 7 and 28 d afer treatment. Means with the same letter are not significantly different (P <0.05; Tukey HSD). Images of strawberry damage rating (0–4) shows the scale used to assign damage rating to plants.

Fig. 3 in Management of stemborers (Lepidoptera: Crambidae) using foliar-applied chlorantraniliprole in rice

Fig. 3. Whitehead density as affected by insecticide treatment, Beaumont, Texas, USA, 2019. The Δ symbol following an insecticide treatment designates chlorantraniliprole that was applied at planting. The remainder of the treatments were applied at permanent flood. Bars within graphs accompanied by the same letter do not differ significantly (P> 0.05, Tukey's HSD).

Fig. 5 in Management of stemborers (Lepidoptera: Crambidae) using foliar-applied chlorantraniliprole in rice

Fig. 5. Dose-mortality responses of Eoreuma lofini larval mortality with 95% confidence limits (shaded area) 6 and 10 d afer exposure. Dashed line represents 50 and 90% mortality (LC50 and LC90).