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Bacterial strains isolation and identificationFifty six bacterial cultures were isolated from both management systems (G1 and G2) of mango orchards (rhizosphere) at CISH, Lucknow, India. Isolation of microorganisms using spread plate methods revealed that the Nutrient agar medium had the highest number of colony appearances compared to the Rose Bengal Agar medium. Microbial enumeration showed organic system enriched with higher bacterial and fungal population than conventional system (Fig. 1). From organic system, thirty seven bacteria were isolated out of which, twenty-three isolates were (G+), and fourteen were (G−). While, in the conventional system, nineteen bacteria were isolated, out of which fifteen were (G+) and four were (G−) isolates.Figure 1Comparative microbial enumeration of organic and conventional treated mango rhizosphere soil the CFU mL−1 of selected samples showing growth of fungus and bacterial populations under two different treatments i.e. organic and conventional. The results are the average of five replicates (n = 5), with bars representing standard error. Significant differences based on the analysis variance (ANOVA) are shown by different letters above the error bars, followed by the post hoc DMRT test (p ≤ 0.05) using the software SPSS.Full size imagePlant growth promotion propertiesFor plant growth promotory properties out of fifty-six bacterial isolates total, ten bacterial cultures (2, 3, 4, 8, 15, 23 and 31) from the organic system showed positive results for phosphate solubilization. In contrast, three bacterial cultures (I1, I8 and I9) from the inorganic system (conventional system) showed positive phosphate solubilization in Pikovaskya’s agar medium. For siderophore production, bacterial cultures (2, 3, 4, 8, 12 and 26) from the organic system showed positive results, while four bacterial cultures (I1, I6, I8 and I9) inorganic system showed positive results. Bacterial cultures (2, 3, 4 and 8) from the organic system showed positive results for K-solubilization, while five bacterial cultures (I1, I2, I7, I8 and I9) from the inorganic system showed positive K-solubilization. A total of ten isolates (7 from organic and 3 from the inorganic system) possessed Zn-solubilizing activity. The test isolated from the organic system showed better Zn (ZnO), Zn3 (PO4)2, and (ZnCO3) solubilization as compared to test culture isolated from the inorganic system (Supplementary S1.8).Acetylene reduction assay (ARA)Results from acetylene reduction assay showed in aerophilic condition, bacterial isolates 1, 3, 4 (from organic treated soil) and I1, I8 and I9 (conventional system) showed 134.8, 37.70, 36.73, 13.15, 16.70 and 12.87 ppm of ethylene tube−1 h−1, respectively. In case of microaerophilic condition, bacterial isolates 4, 9, I9 showed 24.17, 19.14, and 12.71 ppm ethylene, respectively. Results indicate possible use of these bacterial isolates as a bioinoculant agent for horticultural crops, especially mango and other subtropical climate fruit crops.Soil enzymatic studyThe soil enzymatic activity in the organic system (G1) showed better dehydrogenase activity than the conventional system (G2). For both methods, alkaline phosphatase almost showed similar activity (at pH 11), while in the case of acid phosphatase showed better activity in the inorganic system (G2) as compared to the organic system (G1) at pH level 6.5 (Fig. 2). The dehydrogenase enzyme oxidizes the organic matter, and it belongs to the oxidoreductase type of enzyme. In the process of respiration of soil microorganisms, the dehydrogenase enzyme facilitates the transfer of protons and electrons from the substrate to the acceptor. It was significant to observe that the dehydrogenase activity was higher in organic treated soils (0.784 µg TPF g−1 h−1) than in conventional system (0.053 µg TPF g−1 h−1).Figure 2Comparative soil enzymes activities of conventional and organic treated mango rhizosphere soil the dehydrogenase, acid phosphatase and alkaline phosphatase activities were showing in µg TPF formed g−1 of soil h−1 and µg PNP g−1 soil h−1 respectively. The results are the average of five replicates (n = 5), with bars representing standard error. Significant differences based on the analysis variance (ANOVA) are shown by different letters above the error bars, followed by the post hoc DMRT test (p ≤ 0.05) using the software SPSS.Full size imageAlpha biodiversity with samples and rarefaction curvesIn this segment, by measuring Shannon, Chao1, and observed species metrics, we analyze the microbial diversity within the samples. The chao1 metric measures the richness of the ecosystem, while the Shannon metric is the formula for calculating reported OTU abundances and accounts for both prosperity and equality. The rarefaction curve is provided in Fig. 3 for each metric. Using QIIME software, the metric measurement was done. The impact of both treatments on the microbial complexity and abundance in the sample was also revealed using the Shannon diversity Index (depicting richness and evenness) and Chao 1 representing only richness. Shannon’s diversity index of the bacterial community in the treatment (G1 and G2) was 8.06 and 8.12. The Simpson index in ecology is used to quantify biological diversity in a region, which was also nearly similar in both the treatments. Chao 1 richness estimator showed an increase in species richness. Rarefaction analysis conducted to confirm species richness revealed a difference in the number of reads and OTUs between the samples. The Rare fraction curve had a similar pattern for both samples and showed an impact on the bacterial population in the experiment (Fig. 3a–c).Figure 3Shanon (a), Chao1 (b) curves and observed species (c) obtained for the samples (G1 and G2).Full size imageBacterial diversity analysis at phyla levelTaxonomic study of the 16S rRNA gene amplicon reads yielded seven classifiable bacterial phyla. Six phyla, namely Acidobacteria, Actinobacteria, Bacteroides, Proteobacteria, Firmicutes, and Chloroflexi were dominant in both the systems. The Organically treated soil (G1) sample harbored a higher percentage of Bacteroidetes (14.55%), Actinobacteria (7.45%), and Proteobacteria (10.82%) as compared to conventional treatment (G2) 8.98%, 5.71%, and 6.64%, respectively. However, phylum Acidobacteria(13.6%), Firmicutes(4.84%), and Chloroflexi (2.56) were higher abundance in conventional treatment as compared to the organic treatment, which showed the same phyla with lesser quantity, i.e., 5.63%, 0.91%, and 0.79% respectively (Fig. 4a).Figure 4Comparative microbiome (a-phylum and b-order) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 region) approach.Full size imageDistribution of bacterial community at order levelThe bacterial orders in both systems were diversified. The most abundant orders in organic and conventional systems were Chitinophagales (Organic-11.32%, Conventional-43%), Elev-16S-573 (Organic-3.09%, Conventional-8.69%), Pedosphaerales (Organic-1.56%, Conventional-3.55%), Opitutales (Organic-2.46%, Conventional-0.27%), Chthoniobacterales (Organic-1.35%, Conventional-2.84%), Bacillales (Organic-0.91%, Conventional-4.84%) and Solibacterales (Organic-1.39%, Conventional-2.26%) (Fig. 4b).Bacterial community distribution at family levelBacterial family members were identified and enriched including Pedosphaeraceae (O-1.56%, C-3.55%), Opitutaceae (O-2.46%, C-0.27%), Chthoniobacteraceae (O-1.03%, C-2.68%), Steroidobacteraceae (O-2.05%, C-0.73%), Bacillaceae (O-0.77%, C-4.55%), Chitinophagaceae (O-10.99%, C-5.06%), and Xanthomonadaceae (O-1.39%, C-0.06%) and other families (Fig. 5a).Figure 5Comparative microbiome (a-family and b-genus) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 region) approach.Full size imageBacterial community distribution at the genus levelComparative abundance of unidentified genus in organic system were uncultured soil bacterium, Glycomyces, Chitinophaga, Lysobacter, Udaeobacter, Bacillus (not detected, 1.85%, 4.77%, 1.19%,1.03% and 0.75% respectively) whereas same genus-group were observed in conventional system with different percentage i.e., 0.11%, not detected, 0.56%, 0.04%, 2.67%, 4.54% respectively (Fig. 5b).Bacterial communities at species levelBecause most of the species were unidentified and uncultured bacterium based on relative abundance, they could not be assigned a species name in either sample. Few species are identified in both systems, like Sphingomonas sp. (O-1.57%, C-1.05%), Bacillus drentensis (O-0.25%, C-2.65%), and Chitinophaga sp. (O-4.64%, C-0.11%) (Fig. 6).Figure 6Comparative microbiome (Species) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 regions) approach.Full size imageHeat map and PCA analysisUnder long-term exposure of organic and conventional treatments, a microbial shift was observed in the rhizosphere microbiome of mango orchards. Based on percent abundance, nine different microbial genera Acidobacteria, Actinobacteria, Bacteroidetes and Proteobacteria formed Cluster I. While, Firmicutes, Chloroflexi and Opitutales were abundances in cluster II. Cluster III includes Chitinobacterales, Bacillales, Chitinophagarales and Otherales genera. Whereas cluster IV (Elev7-16S-573, Otherales, Solibacterales and Pedobacteriaceae), cluster V (Opitutaceae, Chitnobacteraceae, Bacillaceae, Chitinophagaceae and Otherales), cluster VI (Xanthomonadaceae, Uncultured soil bacterium, Candidatus-Udaeobacter, Lysobacter and Bacillus), cluster VII (Chitinophaga, Glycomyces and Other), cluster VIII (Uncultured bacterium and Others) and cluster IX (Bacillus drentensis and Others) (Fig. 7). The cluster I observed with the highest abundance was closely related to clusters II and III. Cluster IV to IX created large groups and is distantly related to cluster I to III of the microbial groups in organic and conventional systems (Fig. 7). In the organic system (G1), microbial groups like Proteobacteria, Actinobacteria, Bacteroidetes, and Opitutaceae were largely dominated and provided benefits to the mango rhizosphere in terms of nutrient availability, plant growth promotion, and protection against biotic and abiotic stress. Phylum Proteobacteria and Actinobacteria are closely linked with the rhizosphere and identified as potential PGPR. Acidobacteria and firmicutes, on the other hand, were dominated primarily by conventional systems and serve as a bio-indicator of anthropogenic stress caused by excessive chemical fertilizer application. Undefined Acidobacteria is oligotrophic in nature and considered as an indicator of low organic carbon and acidic environment. To desire higher productivity, the indiscriminate use of chemical fertilizers or pesticides in conventional systems leads to low nutrient availability, microbial shift, less PGPR, and developing the environment for Acidobacteria, Firmicutes and Chloroflexia group of microorganisms. Principal component analysis (PCA) was performed for both systems (organic-component 1; conventional-component 2). The total variables of principal component analysis were the percentage of different parameters such as alkaline phosphatase, acid phosphatase, DHA, Acetylene reduction assay (ARA1, ARA2, ARA3), and CFU mL−1 (bacteria and fungi). The results of PCA yielded two components that explained 100% of the total variance in the data and had an Eigen value of 6.1 for component 1. In contrast, 1.8 for component 2 and together they described 100% of the total variance in the data (Fig. 8). In the organic system, the loading factor with score plot indicates that component-1 is positively associated with DHA, ARA1, ARA2, alkaline phosphatase, acid phosphatase while negatively correlated with CFU ARA3 activity. Component-1 explains the 76.42% variance of the experimental data, while component-2 explains 23.58%. The second component (PC2) represents the positive association with DHA, ARA1, ARA2, ARA3 activity, and CFU while negatively correlated with alkaline phosphatase and acid phosphatase. In the conventional system, the loading factor with score plot indicates that component-1 is positively associated with single variable acid phosphatise while negatively correlated with DHA, ARA1, ARA2, ARA3, CFU, and alkaline phosphatase activity. The second component (PC2) of the conventional system showed positive association with DHA, ARA1, ARA2, ARA3 activity, and CFU, while the negative association with alkaline phosphatase and acid phosphatase.Figure 7Comparative (G1 organic and G2 conventional) heat map of dominant microbial diversity and their clusters in terms of T1 (phylum), T2 (order), T3 (family), T4 (Genus) and T5 (Species).Full size imageFigure 8PCA analysis of different parameters for organic and conventional systems.Full size image More

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Influence of soil organic matter on zeta potentialIn this study, the zeta potential of a clayey red soil was compared among 4 types of long-term treatments including manure, NPK + straw, NPK and CK in a subtropical climate. Generally, the manure treatment which also had the greatest concentration of SOC resulted in the highest clay zeta potential (less intense charge imbalance), while NPK + straw did not result in the second highest zeta potential as expected compared to the NPK and CK treatments. Variation in clay zeta potential among types of fertilization might be related with their different SOM content, because SOM had an influence on the zeta potentials via affecting the negative charges of soils19. The zeta potential of manure and NPK + straw treatments having high SOC agreed with earlier studies in Marchuk et al.9 that decreases of SOC via NaOH treatments decreased the negative zeta potential value9, where Claremont soil originally having high SOC (2.2%) displayed a greater degree of decline in negative zeta potential (from − 29 to − 34.9 mV) than Urrbrae having lower SOC (1.4%) (− 66.3 to − 68 mV). However, zeta potential in water dispersible clay responded to SOC contrastly in the study of Melo et al.12 , where Londrina soil with high SOC (5–20 g kg−1) displayed lower negative zeta potential values in water dispersible clay than that in Rondon soil (SOC 5 to 12 g kg−1) in subtropical Brazil.Differences of SOC effect on zeta potential in our study and other studies were probably because ionic strength in bulk solution also affected the intensity of soil charge imbalance. Generally, in tropical and subtropical Ferralsols, high amounts of SOM that was released following the breakdown of macroaggregate provided an excess of negative charges and intensified the imbalance in charge, resulting in more negative in zeta potential of clay12. In contrast to Ferralsols in Brazil, red soil (highly-weathered) in our study showed higher negative zeta potential in manure soils with higher SOM. This was because high ionic strength in bulk solution might counterbalance the negative charges from SOM, and attenuated the imbalance in charges. Hence, manure treatment which provided greater EC and Ca2+, Mg2+ concentration and possibly higher ionic strength was reasonable to allow for more charge balance and greater negative zeta potential values than other treatment.In this study, NPK + straw treatment exhibited similar negative zeta potential values as that in NPK but slightly lower than manure, probably due to the effect of SOM functional group from straw and soil solution concentration. Straw can increase the humin content as reported in the study of Sheng et al.11, and then a decrease of negative zeta potential can be induced as addition of humic acid on a Luvisol20. But the negative humic effect from straw on zeta potential was probably stronger than the positive effect from the increased bulk soil solution concentration in NPK + straw relative to NPK in Fig. 3 where increase of bulk solution concentration was found to increase the negative charge numbers and the negative zeta potential in Ultisol and Oxisol15. Therefore, our hypothesis that organic treatments decreased negative zeta potential value of soil was not supported for manure treatment, but was for NPK + straw treatment.NPK + straw’s similar effect on negative zeta potential as NPK treatment was probably also related with their similar pH values. The effect of pH on the potential of clay surfaces can be related to the amount of variable charge on the external surface of the clay particles. Negative zeta potential decreased with rising pH of the solution due to deprotonation of the functional groups on the surface of the organic matter and Fe/Aloxides in NPK + straw treated soils. An increase of soil pH (from 3.5 to 7.5) influenced zeta potential through production of more negative net surface charges on soils in subtropical Australia21,22. Therefore, the pH in our study after KCl adjustment that showed a first increase and then decrease pattern with the increase of concentration, can help to explain the bell shape pattern of negative zeta potential (first decrease and then increase). However, in our study, the pH pattern with increment of KCl concentration was different from the results in study of Yu et al.8 where a continuous decline pattern in pH of two soils (Vertisol and Ultisol) was reported when the KCl concentration increased from 10–5 to 10–1 mol L−1. This is probably because the Ultisol possessed high amount of variable charges from Fe or Al oxides, which resulted in the diffusion layer attracted more positive charged cations (i.e. K+) from bulk solution to balance the increased negative charge on the surface of colloidal particles in order to maintain the electrical neutrality of the system15. This indicated that when KCl concentration was low, between 0 and 10–2 mol L−1, part of K+ was attracted to the diffuse double layer and the remaining K+ hydration allowed for raising in soil pH. When KCl concentration was beyond 10–2 mol L−1, many Al3+ions on soil exchange site were released into solution (0.03 to 0.12 mg L−1) through K+ exchange and probably dropped soil pH (data not shown).Studies also found that the effect of SOM on zeta potential of clay also varied for soils in different climate. Yu et al.8 compared rice straw incorporation effect on two soils (Ultisol and Vertisol) and found that similar SOC content resulted in contrasting effects on surface potential of two types of soils, where surface potential of Ultisol continuously increased while firstly increased and became stable for Vertisol with increase of treated solution concentration. Different SOM effect on soil potential properties of two soils were probably associated with presence of soil variable charges in Ultisol23. SOM and Fe/Al (hydro)oxides in Ultisol carried a larger number of variable surface charges, and resulted in a strong overlapping of oppositely charged electric double layers (EDLs) between SOM and Fe/Al (hydro)oxides at low concentration8. The overlapping of oppositely charged EDLs between SOM and Fe/Al probably yielded in an increase in negative surface charge for Ultisols compared to Vertisol.Effect of SOM and zeta potential on soil aggregationIncrement in content of SOM after additions of straw or other organic treatments can improve aggregate stability6,24,25. The hydrophobic organic compounds that coated around soil particle can act as nucleus of aggregate formation and reduce the destruction effect from water infiltration26,27. The hydrophobic-C/hydrophilic-C increased from 1.04 to 1.07, from 1.22 to 1.27 for chicken manure and maize residues treatments, respectively, when soil water conditions changed from water deficiency to natural rainfall treatment28. This indicated that a small change of hydrophobic-C/hydrophilic-C might result in substantial change in soil water, which was a critical factor of aggregate development28. Xue et al.24 also reported that a small difference of aromatic percentage between tillage + straw and no tillage + straw treatments resulted in significant differences for aggregate ( > 0.25 mm). Hence, small variation in soil hydrophobic-C groups can yield in soil aggregate variation. In our study, the manure treatment, which had higher SOM and hydrophobic-C (aromatic C) while lower hydrophilic-C than other treatments, was probably reasonable to yield in its higher stability than others. In these previous studies, the positive effect of SOM on soil aggregate development was attributed to the increment in van der Waals force between soil particles. However, different from our study, Melo et al.12 reported that Londrina soil with high SOC released greater water dispersible clay (60–80%) than that in Rondon with low SOC (50–70%) after mechanical breakdown of macroaggregate. This was probably due to the repulsive force prevailing attractive force between soil particles as affected by more negative zeta potential or surface potential8.Clay zeta potential influenced the powerful electrostatic fields, soil internal forces and aggregate stability9. Decrease in negative clay zeta potential mainly yielded an increase in the soil microaggregate portion ( More

Study siteThe study covered the Brazilian Atlantic Forest domain (Fig. 2), which is located on the east coast of Brazil between latitudes 5° and 30° south, expanding over 500 km inland in the south. It consists of a total area of 1,085,151 km2 with limits defined by the Brazilian Ministry of the Environment48. The total area covered by Sentinel-2 tiles overlapping with the Atlantic Forest domain is ~2,006,959 km2. This latter area was used to compute the descriptive statistics of detections.DataSentinel 2 imagesThe pink or magenta blossoms of Pleroma trees were mapped using Sentinel-2 multi-spectral data with 10 m spatial resolution taken approximately every five days under the same viewing conditions. We used only Sentinel-2 images with Level-1C correction—which are orthoimage products, i.e. map projections of acquired images using a digital elevation model to correct ground geometric distortions—and delivered in images of 100 km × 100 km. Pixel radiometric measurements were provided in Top-Of-Atmosphere (TOA) reflectances (coded in 12 bits)49.In the analysis, 213 Sentinel-2 tiles covering the Brazilian Atlantic Forest domain were used, totaling 2,006,959 km2 which is equivalent to ~20 billion Sentinel-2 pixels with 10 m spatial resolution (Fig. 2a). Amongst the 213 selected tiles, 36 had 2 orbits to download to obtain the full tile image due to the overlapping orbit paths (called replicates in the following text).For each tile and replicate (213 + 36), the times series between 31 June 2016 and the 1 July 2020 was downloaded from the Google Cloud Storage Sentinel-2 repository (https://cloud.google.com/storage/docs/public-datasets/sentinel-2). To reduce the dataset size, we retained only images with less than 80% cloud cover; and, from the month outside the flowering months of the Pleroma trees (July to November), we kept only images with less than 25% cloud cover. The complete dataset was made up of 33,798 Sentinel-2 images.Four spectral bands available at 10 m spatial resolution were used: Red (665 nm), Green (560 nm), Blue (490 nm) and NIR (842 nm). A border of 120 pixels with NA values was added to the image to produce images of 10240 × 10240 pixels to ease automation of the image analysis workflow, which generally works with 2n × 2n size pixel images. In our case here, the deep learning analysis was made with 128 × 128 pixel images and an additional 8 × 8 border. Sentinel L1C reflectance values are in the range of 0–10000 and were converted to 8 bits (0–254) with the following rules : for Red, Green and Blue bands, we kept the minimum value between 2540 and the original pixel value, divided this value by 10 and converted the result to integer; and for the NIR band, we keep the minimum value between 2540 and the original pixel value divided by a constant equaling 3.937, divided this value by 10 and converted the result to integer. While it was not expected to have RGB pixel values for vegetation with reflectance above 2540, it occured frequently for the NIR values. Dividing the NIR band values by the constant 3.937 enabled scaling the full range of the original NIR values between 0 and 2540 without losing too much information. For each tile, all 4 bands were saved in one GeoTIFF of 8 bits to ease storage and processing. The size of the complete dataset was 5.59 teraoctets. The automatic download, scaling and conversion of the images to 8 bits took about 25 days (from 16 July 2020 to 3 August 2020 and from 10 September 2020 to 13 September 2020).Environmental dataFigure 12Environmental and climatic variables used in the study to analyse spatial distribution of Pleroma trees (a) elevation (m), (b) slope (°), (c) tree cover (%), (d) mean annual precipitation (mm yr−1), (e) annual mean of minimum temperatures (°C), and (f) maximal annual temperatures (°C).Full size image
To test the association of Pleroma trees with elevation and slope, elevation data from the Shuttle Radar Topography Mission (SRTM) were used50 (Fig. 12a). Specifically, we used the 3 arc-seconds (~90 m) spatial resolution digital elevation database (version 4) provided by the CGIAR Consortium for Spatial Information51. This dataset, in comparison to the original NASA STRM dataset, has been processed to fill data voids. From this dataset, we used the variables elevation (m) and computed slope (°) considering the four neighbor pixels (Fig. 12b). To analyse the relationship between Pleroma trees presence and forest tree cover, we used the tree cover percentage for the year 2000 at 30 m of spatial resolution, which we obtained from the global forest cover dataset (Fig. 12c), which is based on Landsat time series52.The association of Pleroma trees with local climate was tested using the annual means of precipitation and air temperatures (Fig. 12d–f). The mean annual precipitation over the study period was computed from the CHIRPS v2p0 monthly precipitation dataset at 0.05° of spatial resolution produced by University of California, Santa Barbara (UCSB). CHIRPS data are global rainfall estimates from rain gauges and satellite observations53. The mean of maximum and minimum air surface temperatures over the study period were computed from the Aqua/AIRS L3 Daily Standard Physical Retrieval (AIRS-only) at 1° of spatial resolution V7.0 (AIRS3STD). AIRS, the Atmospheric Infrared Sounder on NASA’s Aqua satellite, gathers daily infrared energy emitted from Earth’s surface and atmosphere globally and provides 3D measurements of temperature and water vapor through the atmospheric column54. The annual mean of minimum and maximum air surface temperatures was calculated using the daily air surface temperature measured from the descending orbital pass, which occurs at 1:30 am local time (’SurfAirTemp_D’), and the ascending orbital pass, which occurs at 1:30 pm (’SurfAirTemp_A’).Additionally, maps produced by the Brazilian Institute of Geography and Statistics (IBGE) of the geomorphological units and rivers of Brazil were used to describe the spatial distribution of the blossom detections33.All environmental variables were resampled to a raster of 1280 × 1280 m spatial resolution using an average interpolation to match the resolution of the Pleroma tree detection dataset.ModelNeural network architectureThis detection model is a deep learning neural network (Fig. 13), more specifically an encoder with a VGG16-like structure35, that given an image (input image) return the probability of presence of Pleroma trees with flowers in the image. The model inputs are 4 bands RGB-NIR images made up of 136 × 136 pixels at 10 m of spatial resolution (Fig. 13). Sentinel-2 tiles of 10240 × 10240 pixels were cropped based on a regular grid of 128 × 128 pixels, and 4 neighbouring pixels were added on each side to create an overlap between the patches. The resulting images are 136 × 136 pixels in size. However, in the training, the presence or absence of blooming Pleroma was given only for the images of 128 × 128 pixels without consideration of the borders. This enable to avoidance of the border effect that is common in convolutional neural networks. Each image of 136 × 136 pixels goes through a data augmentation process that consists in random vertical and horizontal flips. No additional data augmentation necessary due to the natural data augmentation provided by atmospheric conditions and illumination. After data augmentation, the images were then fed to the detection encoder. The encoder was made up of 5 consecutive convolution and pooling blocks, one fully connected layer (dense 100) and a final output layer with a softmax activation that provided the probability of presence of blooming Pleroma trees in the image (Fig. 13). Additionally, one drop-out layer was used at the end of the fully connected layer to perform further implicit data augmentation and avoid overfitting during training. The model has a total of 25,448,686 parameters, of which 25,440,622 are trainable. The model was coded in R language55 with Rstudio interface to Keras and TensorFlow 2.256,57,58,59.Figure 13Architecture of the Pleroma blossom detection model.Full size imageNetwork trainingTo make the training sample, a manual sample was produced for the Sentinel-2 tile 23KMQ, in the area where we had previously made a high resolution map of blooming Pleroma6, and for five other tiles where flowering Pleroma were detected visually from high resolution Google Earth images (22JFQ, 22JGQ, 23KLP, 23KLQ and 23KNQ, respectively). What is identified in the Sentinel-2 images are forest stands dominated by Pleroma and not single individuals. Pleroma trees have a small stature (8–12 m height) and crown of less than 10 m and one tree alone cannot influence sufficiently the reflectance to be clearly detectable in Sentinel-2 images. However, they occur very frequently clumped together, a common behaviour of this pioneer Genus. These flowering Pleroma dominated forest stands were easy to identify visually in the Sentinel-2 images because they combined several very distinctive features. First, an intense magenta-to-deep-purple color, which is an unusual color for other land covers in this ecological domain. Second, these identified Pleroma pixels should be undoubtedly identified as forested pixels and have a green color outside the blooming season. Third, Pleroma dominated forests often formed continuous magenta-to-deep-purple patches of size ranging from some 10 m × 10 m pixels to more than thousands of pixels and the shape of the patches tend to present linear features, likely representing the border of the space that was colonized by the Pleroma trees. Fourth, individual crowns were not visible, and the texture of the patches was very smooth during the blooming season with sometimes some inclusions of tree crowns of green color. Finally, texture of the Pleroma dominated forest stands outside the blooming season shown a smooth green texture, more homogeneous and with less shade than other forests. The first sample was constituted of images of background and of blooming Pleroma dominated forest stands that were following the previously described criteria. From this sample, we train a first model and applied it to the complete time series of Sentinel-2. From the results of this model, we obtained a first map of Pleroma trees and were also able to identify the main detection errors of this model, mainly clouds and dirt roads proximity with some unidentified agriculture fields or sometimes Eucalyptus plantation. The results of this first model were checked visually to produce a second larger sample (which was used for the results presented in this study) made up of images containing blooming Pleroma dominant to monospecific forest stands, a set of background images without blooming Pleroma and images identified erroneously by the first model as containing blooming Pleroma. While a large majority of the detected pixels were undoubtedly forest stands dominated by Pleroma trees, some other isolated trees of the genus Handroanthus (Ipê in Brazil or Lapacho in Argentina) with pink flowers and large crowns covering several pixels of 10 m × 10 m were also detected and kept in the training sample. For these particular Handroanthus trees, crowns were visible during and sometimes also outside the blooming season, which was not the case for detected Pleroma dominated forest stands. Finally, as our model detected also large Handroanthus trees, we must acknowledge that other tree species with highly similar features could also potentially being detected.The final training samples comprised a total of 158,612 images of 136 × 136 pixels. Among them, 35,541 contained blooming Pleroma trees and 123,071 images contained only background. Among the background images, there was nine different images types: images without blooming Pleroma, i.e., background such as other land covers, urban structures, water surfaces and agriculture and other land uses (57,007), images with forests containing Pleroma but outside the flowering period (23,427), images with clearly identified detection errors mainly located in the east of the São Paulo state (12,965), clouds and detection errors in clouds (10,991), images clearly identified as detection errors near the State of Bahia (9030), other detection errors over Atlantic Forest (5843), images of forests without Pleroma trees during the season of blooming (2170), images with identified detection errors in the northern part of Atlantic Forest (1126) and images with no data (512). Of these images, 80% (126,890) were used for training and 20% (31,722) used for validation.During network training, we used a standard stochastic gradient descent optimization, a binary cross-entropy loss and the optimizer RMSprop60 with a learning rate of 1e-4. We used the accuracy (i.e. the frequency with which the prediction matches the observed value) as the metrics of the model. However, due to the imbalance between the number of blooming Pleroma and background images, the metric of the model was weighted by one for the background and, for the Pleroma, by the ratio between the number of background images and the number of images containing blooming Pleroma: that is, ~3.5. The network was trained for 5000 epochs, where each epoch was made of 61 batches with 2048 images per batch and the model with the best weighted accuracy was kept for prediction (epoch 4331 and weighted accuracy of 99.58%). The training of the models took approximately 9 hours using a Nvidia RTX2080 Graphics Processing Unit (GPU) with an 8 GB memory.PredictionTo avoid border effects, each 10240 × 10240 pixels Sentinel-2 image was cropped on a regular grid of 128 × 128 pixels (1280 × 1280 m), and 4 neighboring pixels were added on each side to create an overlap between the patches. The function gdal_retile61 was used for this operation. Prediction was then made for each subset image: for each image, the detection model returned 0 or 1 if a blooming Pleroma was found in the image. Then the results were spatialized again using the grid, but this time, each cell of the grid only received 1 value, the prediction, resulting in a raster of 80 columns and 80 rows and a spatial resolution of 1280 m, of the same extent as the Sentinel-2 image. The value of the pixels (1 or 0) indicated the presence or absence of blooming Pleroma trees in this squared area of 1280 m of side. Prediction using GPU of a single tile of 10240 × 10240 pixels took approximately 1 minute on a Nvidia GTX1080 with an 8 GB memory and 45 s on a Nvidia RTX2080 with an 8 GB memory. The prediction for the complete Sentinel-2 time series presented in this work took approximately 22 days using a Nvidia GTX1080 GPU—from the 30 October 2020 to the 20 November 2020.Spatio-temporal analysisTo analyse the seasonality of the detections, daily maps of flowering Pleroma detections were produced for the studied period on a grid overlapping the entire Atlantic Forest (projection UTM 23S and datum WGS84) with a spatial resolution of 1280 m to match the resolution of the predictions. For each day, each pixel of the grid was given a classification: observed with flowering Pleroma, observed without flowering Pleroma, observed with clouds (using the cloud cover mask for Sentinel-2 images of this day) or as non-observed (no image or NA data for the pixel on that day). These daily grids were use to produce the map of flowering Pleroma trees (number of detections of flowering Pleroma for each pixel along the time series), the map of the total number of observations per pixel and the map of the total number of observations without clouds.To analyse the seasonality of blooming, the detection results were aggregated by month—even with a 5-day frequency there were still too few observations to analyse each annual timing and duration of flowering, and changes of the flowering dates between years were not expected based on the existing botanical information of the species. For each pixel, the number of detections per month were divided by the total number of observations without clouds per month. This enabled to normalized the detection values between zero and one and made sense given that we were not interest in the number of detections but rather in the times of the year when the number of detections was the highest: the peak of the blooming.To find the characteristics of these time series—one or more blooming peaks and the days when the blooming begins, peaks and stops—the normalized time series of mean monthly observations of flowering Pleroma were filtered using the Fourier transform (FT) (Eq. 1). This decomposition was made the keeping only the annual, bis- and tris-annual frequencies that compose the blooming signal, and to provide a continuous representation of the discrete blooming observations. In other worlds, the Fourier transform of the normalized time series observations enabled to model and compute the values of blooming for each day of the year and better estimate the days blooming started, peaked, and ended. While initially, a decomposition with only annual and biannual frequencies was expected to fit well to the times series (as more than two peaks per year were not expected), it appeared that when the two peaks were close in time (such as in a 2–3 month interval), only annual and biannual frequencies were not sufficient to give a good model of the signal, and the triannual frequency was added to resolve this issue. Furthermore, it was assumed that other periods in the signal were only constituted by noise.The blooming signal was modelled by the following equation:$$begin{aligned} {widehat{bloom}}(t)& = bloom_0 + pow_0 ,left( p_{4} sin left( 2pi frac{1}{4} t + rho _4right) right. \ & quadleft. + p_{6} sin left( 2pi frac{1}{6} t + rho _6right) + p_{12} sin left( 2pi frac{1}{12} t + rho _{12}right) right) end{aligned}$$
(1)
with (p_4 + p_6 + p_{12}=1) and for (t=1,ldots ,12 times n), ({widehat{bloom}}) is the filtered blooming time series; (bloom_0) is as an estimate of the mean annual blooming; t is the time in months; (rho _4), (rho _6) and (rho _{12}) are the delay of signal components with periods of 4 months, 6 months and 12 months, respectively; (pow_0) is the power of the signal and (p_4), (p_6) and (p_{12}) are the relative proportions in the signal of the periods of 4 months, 6 months and 12 months, respectively.To ease optimisation and cohere with the biological significance of our model, some data cleaning and adjustments were made. First, pixels with less than 4 observations over the 4-year period were removed from the analysis. Second, isolated peaks with only 1 or 2 observations during the 4-year period and between months without Pleroma detection were set to 0. Third, all the values of the normalized blooming time series were multiplied by 10, which seemed to ease the convergence of the optimisation algorithm. Fourth, the first months before and after the blooming period were set to a negative value equal to − 0.15 × the maximum value of the pixel time series. This was made based on the assumption that blooming is quite fast (based on the observation data) and happens between the month when the blooming is first observed and the previous month (and when blooming is last observed and the next month), and it forced the model to go below the 0 value during this period. Fifth, a weight was added to each point corresponding to its value, as we were interested in estimating accurately the peak value. A weight value of 0 was set to the month with a 0 value, and a weight of 1 was set to the months with negative blooming values (pre- and post-peak months). Finally, to facilitate the optimization, the time series of values and weights was replicated 3 times (n = 3). While this did not change the periodicity of the signal, it enabled to estimates better the value of the first and last month of the time series, as well as to ease optimisation. The parameters (bloom_0), (pow_0), (p_4), (p_6), (p_{12}), (rho _4), (rho _6) and (rho _{12}) were then estimated by a weighted least square minimization using the weights previously described. The accuracy of the model was given by the weighted R2 computed with the observed and predicted values of blooming for each month. As the Fourier transform is highly flexible, it can adjust almost perfectly to the data: the median of weighted R2 was close to 1 (with a 95% confidence interval—from percentile 2.75 to 97.5—of 0.86.0 to 1).Figure 14Examples of observed time series of detections in cloud-free images (%) and their daily estimation modeled using the Fourier transform.Full size imageAfter the decomposition of the blooming signal, a daily time series of 1 year of ({widehat{bloom}}) was computed with the obtained parameters (365 values) (Fig. 14). Daily values of months with a weight of 0 were set to 0 as well as predicted negative values. Then all peaks and pits were identified in the ({widehat{bloom}}) time series. A peak or pit is an observation that is preceded and followed by, respectively, lower or higher observations62,63. For each peak, the day of start and stop were identified using the pit values. After this analysis, we were able to describe the blooming time series: that is, if there were one or more peaks and, for each peak, the days when the blooming initiated, peaked and stopped.To determine if different populations could be identified based on flowering timing, a cluster analysis was performed. A classical K-means clustering analysis was made on a dataset containing, for each pixel where Pleroma were detected, the days of start, peak, and end of blooming, the associated normalized blooming values and the xy coordinates of the pixels. If there were two peaks for a pixels, a line for each peak was created in the dataset. As the xy coordinates were in metres, they carried most of the variance in the dataset. To avoid the artefact of having clusters based only on the distance between pixels, the xy coordinates were divided by 100,000 and rounded to the nearest unit. Before the clustering analysis, all variable were scaled and centered. The number of clusters was determined based on the curve representing the total within-cluster sum of squares as a function of the number of clusters, and also to have the maximum number of clusters.To describe the association of Pleroma trees with environmental variables, we first reclassified each environmental variables into 10 classes according to the variable’s quantiles. Then a bootstrap procedure was applied. For the number N of Pleroma trees detections, N random point locations were sampled within the Atlantic Forest domain, and the value of each environmental variable at each point was extracted and stored. This operation was repeated 100 times. It enabled to compare the number of Pleroma trees in each quantile class with the mean and gave us a 95% confidence interval for the number of points obtained by random spatial sampling in each class. Using the elevation as an example, the null hypothesis of no spatial association between Pleroma trees and elevation was rejected at a level of 0.05% if the number of Pleroma trees in a quantile class of the elevation was outside the (0.025, 0.975) quantiles of the empirical distribution of elevation obtained by random location sampling in the same class. The same analysis of association with the environmental variables was made for the Handroanthus population identified by the K-means clustering analysis.All analyses were performed using R project software55. More

Magnesium soil contents and relationships to livestock healthMagnesium (Mg) deficiency (hypomagnesaemia) in ruminant livestock is a serious issue for the agricultural sector and accounts for a significant number of animal deaths annually. It is caused by a diet deficient in Mg, or due to an imbalance in the supply of Mg in comparison to other mineral cations1. Hypomagnesaemia is likely to be responsible for lower-productivity and diminished well-being in more animals in a herd compared to those displaying acute symptoms, given that herds/flocks generally receive a common diet2,3. If Mg deficiency could be prevented, it would be of benefit to both animal welfare and economic productivity. Recent research has confirmed that whilst hypomagnesaemia is commonly reported by UK farmers, the reported use of preventative measures is low, and the use of pasture interventions is lower still4. Pasture interventions can include the application of Mg-rich fertiliser or lime products, or selection of sward species with a propensity to take-up elevated Mg concentrations4,5.One aspect of dietary supply is the geographic control of pasture and farm-produced fodder. It is known that total Mg and plant-available Mg concentrations in soil are controlled by geological and geographic factors6,7,8,9 and that there is little evidence for any changes in pasture soil Mg concentrations through time6,10. The magnesium content of soil relates to that of the bedrock, where it is high in the bedrock it is high in soil and vice versa. Thus, the composition of all pasture and farm-grown fodder will be influenced by this natural environmental endowment as well as pasture management decisions.Grass productivity and soil pHA key pasture management activity is that of soil pH—which here is reported as measured in water (pHw), consistent with standard agronomic laboratory practice in the UK11. Grassland mineral soil is recommended to be maintained at pHw ≥ 6.0 in Britain12,13. In Ireland, where grass-clover pasture is more widely practiced, a pHw threshold of 6.5 is recommended14. However, multiple lines of evidence exist that indicate pasture soil is frequently below these pHw recommendations. Private sector on-farm sample data summaries from the UK consistently show pH typically below recommendations in pasture soil: the most recent annual data synthesis reports 57% of grassland soil with pHw ≤ 5.99, and 27% with pHw 6.00–6.498. This is consistent with systematically collected public sector data across the north of Ireland, where 84% of pasture samples were below the clover-grass recommended threshold of pHw 6.515.Grassland production is widespread in Wales and western England (Fig. 1). Two environmental factors jointly contributing to the lower pH in these regions are (1) geological—these areas are most often on soils which are developed over rocks with low concentrations of base cations (Figs. 1 and 2); and, (2) these areas are also often upland areas, associated with typically higher rainfall16 which will further leach base cations. Added to these environmental factors are the application of nitrogen fertilisers which have an acidifying effect17. Thus, many pastoral areas require treatment using agricultural lime in order to optimize soil pH for grass growth18.The use of liming materialsThe opportunity to improve grazing livestock Mg nutrition through use of Mg-rich lime is identified in guidance available to farmers12. This can have the dual benefits of maintaining soil pH for grass growth and ensuring Mg levels in livestock feed is at sufficient levels19,20,21. The combination of soil treatment for pH and Mg would therefore appear to be an efficient solution to solve issues surrounding Mg deficiency22. Conversely, for many soils with existing high Mg levels it may be important to treat with low Mg liming materials to ensure an optimal Ca–Mg balance to preserve the soil structure23.The use of Mg lime is only one of many methods of controlling Mg levels in livestock feed. Other methods, such as direct additions to feeds, salt licks, pelletised fertilisers products are also effective in reducing incidences of hypomagnesaemia and need to be considered as part of holistic review of a individual farms requirements, this is discussed in Kumssa et al.5.Maintaining optimal soil pH will directly affect the productivity of grass used for grazing, and will increase fertiliser use efficiency14. However, in some cases, for example upland sheep farming, a low investment—low return approach, with minimum interventions such as liming, may be entirety sensible and appropriate to the farm business and local landscape24,25.The extent to which agricultural lime is used in Britain is captured through the annual British Survey of Fertiliser Practice (BSFP) and can also be inferred from commodity production statistics. Production can be regarded as a good proxy for consumption since due to its high bulk and low price it is not exported in significant quantities.The BSFP, is an annual Department for Environment, Food and Rural Affairs (DEFRA) survey26, which representatively samples fertiliser and lime use across the British farming sector. This captures information on lime use in three geological material categories as used in arable and pastoral systems, as well as use of sugar beet lime and ‘other’ options. Sugar beet lime use is very low on grassland (generally unrecorded on ‘permanent’ pasture); ‘other’ categories are generally on a par with Mg-lime, but more detailed liming characteristics are not reported. Figure 3 shows a clear trend in decreasing production of agricultural liming material over the last 40 years. Lime use in the UK peaked in the late 1950s and mid 1960s likely due to a subsidy for agricultural lime in place at the time, this ended in 1978, causing prices to increase and subsequently lime use to decrease27. The use of agricultural lime has continued on a declining, or flat trend, likely due to reluctance to engage in soil treatments that are seen to be costly and a lack of knowledge over its potential benefits.Figure 1Classes of land cover considered by this study, based on Land Cover Map 201533. Some features of this map are based on digital spatial data licensed from the UK Centre for Ecology and Hydrology. Created using ArcMap 10.7.1, ESRI, 2019.Full size imageFigure 4 shows the use of geological lime products to be low at present in respect of the proportion of fields to which lime is reported to be applied, and that this is particularly pervasive on permanent grassland, with a 10-year average to 2019 of 2.9% (range 2.0–4.1%). Of this, a 10-year average of 0.4% of fields had Mg-lime applied, with 1.8% of fields having limestone applied. The limited use of chalk (0.1% of fields) probably reflects the distance between the majority of pasture and the outcrop of the chalk. Recent grassland ( More

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