Evaluation of management practices and remediation techniques for improving water quality in agricultural systems

Surface water quality impairment is often associated with agricultural activities. In this study, the effects of three sugarcane residue management techniques, namely burning (BR), shredding (SR), and retention (RR) of residues on: surface water quality, carbon export, and chemical composition of organic matter in the runoff sediments were characterized. Separate studies were conducted to evaluate predictive relationships for biochemical oxygen demand (BOD) in agricultural effluents, and the effectiveness of bauxite residues (red and brown muds) in reducing soluble nutrient/pollutant release from manure-impacted soils. All the selected water quality parameters were determined using EPA-approved analytical methods. The RR technique exported lower total suspended solids (TSS), total phosphorus (TP), BOD5, and inorganic anion loads compared to the BR and SR techniques during the study period. Rainfall amount correlated with TSS, BOD5, total Kjeldahl nitrogen (TKN), TP, nitrate-N, and nitrite-N exports in each treatment, and runoff turbidity significantly correlated with TSS (R2 = 0.95, P \u3c 0.001).The BR treatment exhibited higher total carbon (TC), total organic carbon (TOC), and particulate organic carbon (POC) export, and these parameters were also positively correlated to runoff turbidity and TSS (R2 = 0.42-0.87, P \u3c 0.001). The pyrolysis-GC/MS analysis of the runoff sediments indicated higher intensity of lignin-derived compounds in the BR treatment than in the RR and SR treatments. Polysaccharide-derived compounds, dominated by levoglucosan, tended to decrease over the growing season in all the treatments, and were lower in the BR treatment. Examination of a wide range of simulated agricultural effluents showed that short-term BOD measurements (BOD2 and BOD5) significantly correlated with TOC, POC, and dissolved organic carbon (DOC) (R2 = 0.62-0.77, P \u3c 0.001), as well as to nitrite-N and total N (R2 = 0.45-0.66, P \u3c 0.001), and improved relationships were obtained with multivariate regression analyses. However, these relationships weaken progressively with increasing incubation times. Application of bauxite residues, especially 2% of the neutralized red muds, significantly (P \u3c 0.05) reduced the soluble P, organic C, heavy metals, and also the BOD of runoff water from manure-impacted soils. Overall, appropriate management practices and amendment techniques could improve water quality in selected agricultural systems

Relating suspended solids and phosphorus in surface water runoff from agricultural soils to soil salinity measurements

Runoff of sediments and nutrients, particularly phosphorus (P) from agricultural fields is considered as one of the main causes of water quality impairment. Very little research has been done on relating suspended solids in runoff to soil test information. This two-part study was aimed at:1) evaluating the relationship between total suspended solids (TSS), P forms in runoff, and soil salinity measurements, particularly electrical conductivity (EC), and 2) establishing the relationships between runoff P forms and the various soil test P measures, across a variety of selected Louisiana calcareous and acid soils. In the first part of the study, five Louisiana soils with clay content of 27 to 44% were selected, treated with different concentrations of salt solution (7.5 to 30 dS m-1), subjected to simulated rainfall, and various runoff parameters were measured. The TSS, total phosphorus (TP), and particulate phosphorus (PP) in runoff were found to decrease with consecutive simulated rainfall event. A highly significant relationship existed between TSS and turbidity of the runoff water (R2 = 0.92, P \u3c 0.001). Each of TSS, turbidity, TP and PP negatively correlated to soil EC (R2 = 0.22-0.29, P \u3c 0.05). A very significant relationship was observed between TP and TSS in runoff (R2 = 0.73, P \u3c 0.001). In the second part of the study, nine soils of varying chemical and physical properties (pH, % clay, CaCO3 etc.) were used. The results revealed that among the measures of soil P examined, only water extractable P and Mehlich III P were reliable indicators of DP losses, explaining about 86% and 57% respectively, of the variability in runoff DP. The study showed that Olsen P (R2 = 0.73, P \u3c 0.01), NH4-oxalate P (R2 = 0.50, P \u3c 0.05), and NaOH P (R2 = 0.50, P \u3c 0.05), reasonably correlated with runoff TP. Among the calcareous soils, Bray II P, NH4-oxalate P and NaOH P each explained about 40% of the variability associated with TP in runoff water. Along with soil test P measures, soil EC relationship with TSS could be useful in predicting P losses in runoff and hence requires further examination

Dynamical Systems of the BCM Learning Rule: Emergent Properties and Application to Clustering

The BCM learning rule has been used extensively to model how neurons in the brain cortex respond to stimulus. One reason for the popularity of the BCM learning rule is that, unlike its predecessors which use static thresholds to modulate neuronal activity, the BCM learning rule incorporates a dynamic threshold that serves as a homeostasis mechanism, thereby providing a larger regime of stability. This dissertation explores the properties of the BCM learning rule – as a dynamical system– in different time-scale parametric regimes. The main observation is that, under certain stimulus conditions, when homeostasis is at least as fast as synapse, the dynamical system undergoes bifurcations and may trade stability for oscillations, torus dynamics, and chaos. Analytically, it is shown that the conditions for stability are a function of the homeostasis time-scale parameter and the angle between the stimuli coming into the neuron. When the learning rule achieves stability, the BCM neuron becomes selective. This means that it exhibits high-response activities to certain stimuli and very low-response activities to others. With data points as stimuli, this dissertation shows how this property of the BCM learning rule can be used to perform data clustering analysis. The advantages and limitations of this approach are discussed, in comparison to a few other clustering algorithms

Oscillations and chaos in the dynamics of the BCM learning rule

The BCM learning rule originally arose from experiments intended for measuring the selectivity of neurons in the primary visual cortex, and it dependence on input stimuli. This learning rule incorporates a dynamic LTP threshold, which depends on the time averaged postsynaptic activity. Although the BCM learning rule has been well studied and some experimental evidence of neuronal adherence has been found in the other areas of the brain, including the hippocampus, there is still much to be known about the dynamic behavior of this learning rule

Can Soybean Seeding Rates Be Reduced Without Affecting Yields in Louisiana? (Bulletin #892)

A major agronomic objective for commercial soybean production is to reduce the minimum plant population required for optimal yield (i.e., minimal optimal plant population). This has occurred because seed cost, once a minor production expense, has become a major cost, accounting for about 42 percent of direct operating costs for an average U.S. soybean grower (U.S. Soy Crop Statistics, 2011).https://digitalcommons.lsu.edu/agcenter_bulletins/1003/thumbnail.jp

Fate and Distribution of Heavy Metals in Wastewater Irrigated Calcareous Soils

Accumulation of heavy metals in Jordanian soils irrigated with treated wastewater threatens agricultural sustainability. This study was carried out to investigate the environmental fate of Zn, Ni, and Cd in calcareous soils irrigated with treated wastewater and to elucidate the impact of hydrous ferric oxide (HFO) amendment on metal redistribution among soil fractions. Results showed that sorption capacity for Zarqa River (ZR1) soil was higher than Wadi Dhuleil (WD1) soil for all metals. The order of sorption affinity for WD1 was in the decreasing order of Ni > Zn > Cd, consistent with electrostatic attraction and indication of weak association with soil constituents. Following metal addition, Zn and Ni were distributed among the carbonate and Fe/Mn oxide fractions, while Cd was distributed among the exchangeable and carbonate fractions in both soils. Amending soils with 3% HFO did not increase the concentration of metals associated with the Fe/Mn oxide fraction or impact metal redistribution. The study suggests that carbonates control the mobility and bioavailability of Zn, Ni, and Cd in these calcareous soils, even in presence of a strong adsorbent such as HFO. Thus, it can be inferred that in situ heavy metal remediation of these highly calcareous soils using iron oxide compounds could be ineffective

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function