Phosphorus Non-Point Pollution from Equestrian Wastes and the Need for Recycling

South Florida and much of the rest of the World suffers from harmful algal blooms (HABs) and controls of both nitrogen (N) and phosphorus (P) pollution are required to curtail the onset, spread and/or expansion of these blooms. This report covers our studies on several aspects of equestrian waste (viz. horse manure) aimed at yielding an overview of phosphorus and its pollution stemming from non-point horse manure sources in portions of Palm Beach County Florida. Methods included a modified Hedley extraction sequence, emphasizing ‘easily extractable phosphorus’ (EEP), and 31P nuclear magnetic resonance (NMR) spectroscopic identification of organic phosphorus (Po) species. Samples included fresh and aged horse manure, pasture soils, horse feed and pasture grasses, and canal waters adjacent to equestrian or agricultural fields. Easily extractable Phosphorus (EEP) averaged about 54-77% of the total horse manure phosphorus. Total phosphorus ranged from 13,020 – 22,300 mg per kilogram dry weight. (≈60-100 lbs. P2O5 / ton and on a wet weight basis, this equates to 4,000 to 14,818 grams-P/ U.S. ton or 8.8 to 32.6 pounds of phosphorus (≈ 20-75 lb. P2O5) per wet weight ton of horse manure. Considering the values of EEP in fresh samples from a single horse, we found a range of 8,000 – 17,000 mg-P/kg (8-17 g-P/kg) dry weight horse manure. Soil samples yielded the highest P in the NaOH extract of the Hedley sequence. This equates to the Al, Fe and ester forms. Phosphorus (viz. EEP) runoff is viewed here as a non-point P pollution source.

African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ($$N \approx 150,000$$ N ≈ 150 , 000 ) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the (Machine Learning in ) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.

Spectrophotometric Quantification of Phytic Acid During Embryogenesis in Bambara groundnut (Vigna subterranea L.) Through Phosphomolybdenum Complex Formation

In relation to seed vigour and hyperosmotic stress, phytic acid has implications in imparting drought tolerance and enrichment of seed mineral reserves respectively. The present study was undertaken to determine the variation in phytic acid during seed development and physiological maturity of 4 Bambara groundnut landraces. The landraces were grown in field during 2017–2018 rain season at Ukulinga, Pietermaritzburg. The phytate content was estimated indirectly from 14-65 days after flowering (DAF) by using a spectrophotometer, evaluating the total extractable phosphorus absorbance at 720 nm. An analysis is described for the rapid determination of phosphorus in developing seeds. The colour complex (phosphomolybdenum) formed under acidic conditions absorbs maximally at 720 nm in acidic (pH<4.5) solutions. Absorbance of the chromophore when measured spectrophotometrically at 720 nm, it obeys Beer’s law over the range of 0 to 75 ppm of standard phosphorus solution. There were significant differences (P<0.001) in total extractable phosphorus at 14, 21, 28, 35, 42 and 65 DAF. The highest and lowest extractable phosphorus was recorded in G340A and Kazai respectively. Pi seed content was between 1.51 and 5.69 mgkg-1 at 14 DAF, at physiological maturity (65 DAF) Pi was recorded between 21.73 and 32.23 mgkg-1. We drew conclusions that Bambara groundnut landraces may differ in both (1) phytic acid accumulation rate and (2) phytic acid content at physiological maturity. The results reported open the possibility of a specific seed selection criterion for improving the mineral element value of Bambara groundnut through the identification of landraces with high-Pi (phytic acid).

African Soil Properties and Nutrients Mapped at 30--m Spatial Resolution using Two-scale Ensemble Machine Learning

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mappedat all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGOfunded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. Inthis paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensivecompilation of soil samples (N ≈ 150, 000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and totalnitrogen (N), total carbon, Cation Exchange Capacity (eCEC), extractable — phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg),sulfur (S), sodium (Na), iron (Fe), zinc (Zn) — silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariatelayers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives)images. Our 5–fold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC=0.900) tomore poorly predictable extractable phosphorus (CCC=0.654) and sulphur (CCC=0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11,B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 mresolution covariates. Climatic data images — SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature — however, remainedas the overall most important variables for predicting soil chemical variables at continental scale. The publicly available 30–m soil maps aresuitable for numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmentalprograms, or targeting of nutrition interventions.