Recent phosphorus (P) pollution in the United States, mainly in Maine, has raised some severe concerns over the use of P fertilizer application rates in agriculture. Phosphorus is the second most limiting nutrient after nitrogen and has damaging impacts on crop yield if found to be deficient. Therefore, farmers tend to apply more P than is required to satisfy any P loss after its application at planting. Several important questions were raised in this study to improve P efficiency and reduce its pollution. The objective of this study was to find potential reasons for P pollution in water bodies despite a decrease in potato acreage. Historically, the potato was found to be responsible for P water contamination due to its high P sensitivity and low P removal (25–30 kg ha−1) from the soil. Despite University of Maine recommended rate of 56 kg ha−1 P, if soil tests reveal that P is below 50 kg ha−1, growers tend to apply P fertilizer at the rate of 182 kg ha−1 to compensate for any loss. The second key reason for excessive P application is its tendency to get fixed by aluminum (Al) in the soil. Soil sampling data from UMaine Soil Testing Laboratory confirmed that in Maine reactive Al levels have remained high over the last ten years and are increasing further. Likewise, P application to non-responsive sites, soil variability, pH change, and soil testing methods were found to be other possible reasons that might have led to increases in soil P levels resulting in P erosion to water streams.
Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems. This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on bo
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