Application of machine-learning models for diagnosing health hazard of nitrate toxicity in shallow aquifers

There is a growing concern about health hazards linked to nitrate (NO₃) toxicity in groundwater due to overuse of nitrogen fertilizers in rice production systems of northern Iran. Simple-cost-effective methods for quick and reliable prediction of NO₃ contamination in groundwater of such agricultural systems can ensure sustainable rural development. Using 10-year time series data, the capability of adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) models as well as six geostatistical models was assessed for predicting NO₃ concentration in groundwater and its noncarcinogenic health risk. The dataset comprised 9360 water samples representing 26 different wells monitored for 10 years. The best predictions were found by SVM models which decreased prediction errors by 42–73 % compared with other models. However, using well locations and sampling date as input parameters led to the best performance of SVM model for predicting NO₃ with RMSE = 4.75–8.19 mg l^?1 and MBE = 3.3–5.2 mg l^?1. ANFIS models ranked next with RMSE = 8.19–25.1 mg l^?1 and MBE = 5.2–13.2 mg l^?1 while geostatistical models led to the worst results. The created raster maps with SVM models showed that NO₃ concentration in 38–97 % of the study area usually exceeded the human-affected limit of 13 mg l^?1 during different seasons. Generally, risk probability went beyond 90 % except for winter when groundwater quality was safe from nitrate viewpoint. Noncarcinogenic risk exceeded the unity in about 1.13 and 6.82 % of the study area in spring and summer, respectively, indicating that long-term use of groundwater poses a significant health risk to local resident. Based on the results, SVM models were suitable tools to identify nitrate-polluted regions in the study area. Also, paddy fields were the principal source of nitrate contamination of groundwater mainly due to unmanaged agricultural activities emphasizing the importance of proper management of paddy fields since a considerable land in the world is devoted to rice cultivation.     

Meta-analyses of oil yield in Cuphea PSR23

Oil content and composition of Cuphea seed are of special economic value as raw materials for industrial and food applications. The inherent unpredictability in determining and predicting Cuphea’s oil yield is attributed, in part, to the indeterminate growth habit and the persistence of the domestication syndrome of this semi-domesticated potential oilseed crop. Meta-analysis using multivariate statistical modeling, computer simulations, and custom profiling was carried out on a database collated from several studies carried out in growth chamber, greenhouse and field experiments. Meta-analyses identified the importance of, and quantified direct and indirect relationships and tradeoffs between and within functional traits classified within five interrelated plant modules. Several multivariate statistical analyses procedures were employed in predicting oil content and oil yield, as performance measures of Cuphea at the plant and population levels of integration. The most parsimonious partial least squares regression model identified plant-, capsule-, and seed-based traits that can be used in reconstructing the best configuration needed for high agronomic performance at the individual plant and population levels. Variance-based structural equation modeling suggested that the variation in relative growth rate was strongly linked to differences in specific leaf area and leaf mass ratio; both traits expressed large positive direct and indirect effects on oil yield, but not oil content. Results of custom profiling suggested that seed yield, oil% and oil yield can be optimized by trait adjustments within the phenotypic and metabolic modules. Adjustments to thousand-seed weight and protein content would influence seed yield, oil yield and oil%, in a decreasing order. Improvements in eco-physiological traits, nutrient ratios and structural traits would lead to a slightly higher oil% and eventually higher oil yield.     

Flowering, capsule and seed characteristics in Cuphea

We modeled the flowering and capsule set dynamics, quantified the level of variation in seed characteristics, elucidated the inter-relationships between seed and capsule physical dimensions, and quantified their impact on single seed weight in the cuphea germplasm line PSR23. Temporal patterns of flowering and capsule set were largely time-dependent and highly auto-correlated, with significant autoregressive parameters of number of flowers (0.76) and number of capsules (0.74). Large numbers of seeds per capsule were associated with large coefficient of variation in single seed weight. Seed number and seed weight per capsule, when log-transformed, exhibited a slope of −0.65 indicating that cuphea plants have only a limited capacity to maintain seed weight by adjusting seed number if resources vary. Capsule tissue/seed weight ratio and number of seeds per capsule have the largest potential impact on single seed weight. Sensitivity analyses indicated that capsule perimeter (>76 mm), capsule major (>23 mm) and minor axes (>7.0 mm), capsule circularity (between 0.350 and 0.375), capsule area (<85 mm²), capsule tissue/seed weight ratio (<0.40), and number of seeds per capsule (∼9.0) would optimize single seed weight. This information would help plant breeders exploit genotypic variability in seed and capsule characteristics and agronomists identify optimum trait combinations to produce high yields of this potential oilseed crop.     

Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis

In this study, the body measurements (BMs) of Holstein cows were determined using digital image analysis (IA) and these were used to estimate the live weight (LW) of each cow. For this purpose, an image capture arrangement was established in a dairy cattle farm. BMs including wither height (WH), hip height (HH), body length (BL), hip width (HW), plus the LWs of cows were first determined manually, by direct measurement. Then the digital photos of cows were taken from different directions synchronously and analyzed by IA software to calculate WH, HH, BL and HW of each cow. After comparing the BMs obtained by IA with the manual measurements, the accuracy was determined as 97.72% for WH, 98.00% for HH, 97.89% for BL and 95.25% for HW. The LW estimation using BMs was then performed by the aid of the regression equations, and the correlation coefficient between the estimated and real (manual) LW values obtained by weighing was calculated as 0.9787, which indicates the IA method is appropriate for LW estimation of Holstein cows.     

Biological nitrogen fixation by two Acacia species and associated root-nodule bacteria in a suburban Australian forest subjected to prescribed burning

Purpose

Prescribed burning is a forest management practice which can lead to nitrogen (N)-limited conditions. This study aimed to explore whether biological N₂ fixation (BNF) remained the main source of N acquisition for two understorey Acacia species in a Eucalyptus-dominated suburban forest of subtropical Australia, 3 to 6 years after prescribed burning. Root-nodule bacteria associated with these acacias were also characterised to unravel the differences in rhizobial communities between sites and species.

Material and methods

Two sites, burned 3 and 6 years before sample collection, were selected within a dry subtropical forest of south-east Queensland, Australia. Leaves were collected from individuals of Acacia disparrima and A. leiocalyx at each site to determine leaf total carbon (C) and N content, C and N isotope composition (δ¹³C and δ¹⁵N) and the percentage of N derived from atmospheric N₂. Nodules were harvested from both acacia species at each site to isolate root nodule bacteria. Bacterial isolates were processed for 16S rDNA gene sequencing.

Results and discussion

Generally, no differences were found in plant physiological variables between the two acacia species. Six years after the fire, both species still depended upon BNF for their N supply, with a higher dependence in winter than in summer. Fire, although of low intensity, was likely to have created a N-limited environment which induced the reliance of legumes on BNF. Root nodule bacteria were dominated by non-rhizobial endophytes, mainly from the Firmicutes phylum. No difference in nodule bacterial diversity was found between sites. The relative abundance of rhizobial genera varied amongst plant species and sites, with a shift in dominance from Bradyrhizobium to Rhizobium species between sites 1 and 2.

Conclusions

Our results show that even 6 years after burning, ecosystem remained under N stress and BNF was still the main mechanism for N acquisition by the understorey legumes.

     

Equilibrium,kinetics and artificial intelligence characteristic analysis for Zn (II) ion adsorption on rice husks digested with nitric acid

Paddy and Water Environment - The use of low-cost adsorbents produced from rice husks that can selectively remove zinc (II) ion from wastewater effluent has been investigated. The disorder and...