CropPol: a dynamic, open and global database on crop pollination

Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e. berry weight, number of fruits and kg per hectare, among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), Northern America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001-05 (21 studies), 2006-10 (40), 2011-15 (88), and 2016-20 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA). This article is protected by copyright. All rights reserved.

Disentangling direct and indirect effects of habitat fragmentation on wild plants' pollinator visits and seed production

Habitat fragmentation threatens plant and pollinator communities, as well as their interactions. However, the effects of landscape fragmentation on the pollination of wild plant species are not well understood yet, partly because there are many correlated features in fragmented landscapes (e.g., decreased patch size, increased isolation, and patch complexity) whose influences are difficult to disentangle. Using a structural equation modeling approach, we assessed the direct and indirect effects of landscape fragmentation (patch size, isolation and complexity, percentage of surrounding land in forest) on the abundance, functional-group richness, and evenness of pollinators of 24 habitat fragments within an agricultural landscape in Southern Norway. In addition, we studied how these variables affected visitation rates (visits per flower) and seed production (seed set, seed mass) in the four most abundant plant species in the area. Flower abundance was higher in larger and complex patches and decreased with the percentage of forest in the surroundings, while flower richness increased with patch complexity. We found a direct negative relationship between patch complexity and the overall number of pollinator visits that the habitat fragments received. Apart from this direct landscape effect, pollinator visits were mostly affected by the floral communities, with overall flower abundance and richness increasing both total number of pollinator visits and pollinator-group richness, and flower richness having an additional negative influence on pollinator-group evenness. Interestingly, we did not find any direct link between visitation rates and reproductive success for any of the study plant species. Instead, several landscape variables directly affected species seed production, although the effects of landscape on seed production were highly species specific. Patch complexity had a negative effect on seed production in two of the four focal species, while other components of the landscape had species-specific effects. Increasing fragmentation of agricultural landscapes affects pollination interactions at the community level and the reproduction of wild plants. However, understanding the effects of fragmentation on seed production requires going beyond estimating visitation rates, since landscape effects on plant reproduction are not always related to overall interaction frequencies.

Milk and meat fatty acids from sheep fed a plantain-chicory mixture or a grass-based permanent sward

Plantain and chicory are interesting forage species since they present good nutritional quality and are more resistant to drought than many temperate grasses. The fatty acid (FA) profile in milk and meat is related to a growing concern for the consumption of healthy foods, that is, with a lower content of saturated FA, higher polyunsaturated FA (PUFA) and a favourable n-6 : n-3 FAs ratio. Our objective was to evaluate the FA content in ewe's milk and lamb's meat fed a plantain-chicory mixture (PCH) or a grass-based permanent sward (GBS) dominated by perennial ryegrass. Eighteen Austral ewes in mid-lactation were allocated to PCH and GBS treatments. Milk samples were obtained during September (spring). Thirty weaned lambs were finished on both treatments from November to December (7 weeks), slaughtered and their meat sampled. Fat from milk and meat samples was extracted and stored until analysed by gas chromatography. Milk fat from GBS was higher than from PCH (P < 0.05) in C18:0 (11 385 v. 5874 mg/100 g FA), 9c-18:1 (15 750 v. 8565 mg/100 g FA), 11 t-18:1 (4576 v. 2703 mg/100 g FA) and 9c,11 t-18:2 (1405 v. 921 mg/100 g FA) and lower in 18:2n-6 (827 v. 1529 mg/100 g FA) and 18:3n-3 (943 v. 1318 mg/100 g FA) FA. Total mono-unsaturated FA was higher in GBS than PCH (P < 0.05). Meat fat from PCH swards presented a higher (P < 0.05) content than GBS for 18:2n-6 (46.8 v. 28.2 mg/100 g FA), linolenic (24.6 v. 14.2 mg/100 g FA), polyunsaturated FA (119.7 v. 73.4 mg/100 g FA), n-6 (65.9 v. 40.8 mg/100 g FA) and n-3 (53.8 v. 32.5 mg/100 g FA), respectively. No effect of treatment (P > 0.05) was detected for 9c-18:1 (283.9 v. 205.8 mg/100 g FA), 11 t-18:1 (26.2 v. 19.3 mg/100 g FA) and 9c,11 t-18:2 (10.1 v. 7.6 mg/100 g FA), for PCH and GBS. These results suggest that grazing a PCH mixture results in a higher concentration of PUFA in ewes' milk and in lambs' fat, as compared to a GBS sward.

Prediction of crude protein and neutral detergent fibre concentration in residues of in situ ruminal degradation of pasture samples by near-infrared spectroscopy (NIRS)

An important constraint of in situ degradability studies is the need to analyse a high number of samples and often with insufficient amount of residue, especially after the longer incubations of high-quality forages, that impede the study of more than one nutritional component. Near-infrared spectroscopy (NIRS) has been established as a reliable method for predicting composition of many entities, including forages and other animal feedstuffs. The objective of this work was to evaluate the potential of NIRS for predicting the crude protein (CP) and neutral detergent fibre (NDF) concentration in rumen incubation residues of permanent and sown temperate pastures in a vegetative stage. In situ residues (n = 236) from four swards were scanned for their visible-NIR spectra and analysed for CP and NDF. Selected equations developed by partial least-squares multivariate regression presented high coefficients of determination (CP = 0.99, NDF = 0.95) and low standard errors (CP = 4.17 g/kg, NDF = 7.91 g/kg) in cross-validation. These errors compare favourably to the average concentrations of CP and NDF (146.5 and 711.2 g/kg, respectively) and represent a low fraction of their standard deviation (CP = 38.2 g/kg, NDF = 34.4 g/kg). An external validation was not as successful, with R2 of 0.83 and 0.82 and a standard error of prediction of 14.8 and 15.2 g/kg, for CP and NDF, respectively. It is concluded that NIRS has the potential to predict CP and NDF of in situ incubation residues of leafy pastures typical of humid temperate zones, but more robust calibrations should be developed.

Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy

Visible and near infrared reflectance spectroscopy (VIS-NIRS) was used to discriminate meat and meat juices from three livestock species. In a first trial, samples of Longissimus lumborum muscle, corresponding to beef (31) llamas (21) and horses (27), were homogenised and their spectra collected in reflectance (NIRSystems 6500 scanning monochromator, in the range of 400-2500 nm). In the second trial, samples of meat juice (same muscle) from the same species (20 beef, 19 llama and 19 horse) were scanned in folded transmission (transflectance). Discriminating models (PLS regression) were developed against "dummy" variables, testing different mathematical treatments of the spectra. Best models indentified the species of almost all samples by their meat (reflectance) or meat juice (transflectance) spectra. A few (three of beef and one of llama, for meat samples; one of beef and one of horse, for juice samples) were classified as uncertain. It is concluded that NIRS is an effective tool to recognise meat and meat juice from beef, llama and horses.

Evaluation of the chemical composition of dry dogfoods commercialized in Chile used for growing dogs

The nutritional quality of dry dogfood commercialized in Chile for growing dogs was studied. Samples from at least three different batches of 26 dogfood brands were mixed. The resultant samples (n=26) were chemically analyzed to determine their concentrations of dry matter (DM), gross energy, fiber, ash, crude protein, essential amino acids, total fat, linoleic acid and minerals. The metabolizable energy (ME) content of each sample was estimated using modified atwater factors. The results from the chemical analyses were compared with the nutrient profiles published by the American Association of Feed Control Officials (AAFCO). Dogfoods that were found to contain an estimated ME of over 4,000kcal/kg DM were corrected for their high energy density before comparison. All of the dogfoods contained adequate levels of protein, total fat, linoleic acid, iron, copper, manganese and selenium. The concentration of tryptophan was adequate in 92.3% of the samples. All of the other essential amino acids were present in adequate quantities. However, the situation was different for many of the minerals. Only 92.3% of the dogfoods contained an adequate Ca:P ratio. A total of 96.2% of the dogfoods contained an adequate level of Ca, 96.2% for P, 96.2% for Mg, 92.3% for I, 88.5% for Cl, 80.8% for Na, 80.8% for Zn and only 34.6% were adequate for K content. Overall, only 23% of the dogfoods evaluated in this study fulfilled all of the requirements established by the AAFCO in terms of their content of crude protein, amino acids, total fat, linoleic acid, and minerals. It appears that the majority of the dogfoods evaluated in this study (77%) would not satisfy all nutritional requirements of the growing dog.

Nutritional evaluation of commercial dry dog foods by near infrared reflectance spectroscopy

Near infrared reflectance spectroscopy (NIRS) was used to predict the nutritional value of dog foods sold in Chile. Fifty-nine dry foods for adult and growing dogs were collected, ground and scanned across the visible/NIR range and subsequently analysed for dry matter (DM), crude protein (CP), crude fibre (CF), total fat, linoleic acid, gross energy (GE), estimated metabolizable energy (ME) and several amino acids and minerals. Calibration equations were developed by modified partial least squares regression, and tested by cross-validation. Standard error of cross validation (SE(CV)) and coefficient of determination of cross validation (SE(CV)) were used to select best equations. Equations with good predicting accuracy were obtained for DM, CF, CP, GE and fat. Corresponding values for and SE(CV) were 0.96 and 1.7 g/kg, 0.91 and 3.1 g/kg, 0.99 and 5.0 g/kg, 0.93 and 0.26 MJ/kg, 0.89 and 12.4 g/kg. Several amino acids were also well predicted, such as arginine, leucine, isoleucine, phenylalanine-tyrosine (combined), threonine and valine, with values for and SE(CV) (g/kg) of 0.89 and 0.9, 0.94 and 1.3, 0.91 and 0.5, 0.95 and 0.9, 0.91 and 0.5, 0.93 and 0.5. Intermediate values, appropriate for ranking purposes, were obtained for ME, histidine, lysine and methionine-cysteine. Tryptophan, minerals or linoleic acid were not acceptably predicted, irrespective of the mathematical treatment applied. It is concluded that NIR can be successfully used to predict important nutritional characteristics of commercial dog foods.