Soil Electrical Conductivity Classification: A Basis For Site-Specific Management In Semiarid Cropping Systems

Site specific management (SSM) has the potential to improve both economic and ecological outcomes in agriculture. Effective SSM requires strong and temporally consistent relationships between identified management zones, underlying soil physical, chemical and biological parameters defining yield potential, and crop yield. In a farm-scale (250 ha) experiment in semiarid northeastern Colorado, each of eight 31-ha fields was individually mapped for soil apparent electrical conductivity (ECa) and classified into four management zones (ranges of ECa). Soil analyses revealed a strong negative relationship between ECa zones and soil parameters associated with innate fertility (P ≤ 0.06). The objective of the present study was to further evaluate ECa as a basis for SSM by examining its relationship to actual yield using two years of yield maps for winter wheat (Triticurn aestivum L.) and corn (Zea mays L.). Within field wheat yields were strongly related to ECa, particularly when regressing mean wheat yields within ECa classes against mean ECa within ECa classes (r2 = 0.95 to 0.99). Yield response curves revealed a boundary line of maximum yield that decreased with increasing EC . In this semiarid dryland system, ECa-based management zones can be used in the SSM of wheat for: (1) yield goal determination, (2) soil sampling to assess residual fertilizer concentrations and soil attributes affecting herbicide efficacy, and (3) prescription maps for metering fertilizer, pesticide and seed inputs. Inconsistent relationships were found between ECa and corn yields indicating that, while soil factors controlled wheat yields, corn yields were more influenced by weather

Bifidobacterium breve with α-Linolenic Acid and Linoleic Acid Alters Fatty Acid Metabolism in the Maternal Separation Model of Irritable Bowel Syndrome

peer-reviewedThe aim of this study was to compare the impact of dietary supplementation with a Bifidobacterium breve strain together with linoleic acid & α-linolenic acid, for 7 weeks, on colonic sensitivity and fatty acid metabolism in rats. Maternally separated and non-maternally separated Sprague Dawley rats (n = 15) were orally gavaged with either B. breve DPC6330 (109 microorganisms/day) alone or in combination with 0.5% (w/w) linoleic acid & 0.5% (w/w) α-linolenic acid, daily for 7 weeks and compared with trehalose and bovine serum albumin. Tissue fatty acid composition was assessed by gas-liquid chromatography and visceral hypersensitivity was assessed by colorectal distension. Significant differences in the fatty acid profiles of the non-separated controls and maternally separated controls were observed for α-linolenic acid and arachidonic acid in the liver, oleic acid and eicosenoic acid (c11) in adipose tissue, and for palmitoleic acid and docosahexaenoic acid in serum (p<0.05). Administration of B. breve DPC6330 to MS rats significantly increased palmitoleic acid, arachidonic acid and docosahexaenoic acid in the liver, eicosenoic acid (c11) in adipose tissue and palmitoleic acid in the prefrontal cortex (p<0.05), whereas feeding B. breve DPC6330 to non separated rats significantly increased eicosapentaenoic acid and docosapentaenoic acid in serum (p<0.05) compared with the NS un-supplemented controls. Administration of B. breve DPC6330 in combination with linoleic acid and α-linolenic acid to maternally separated rats significantly increased docosapentaenoic acid in the serum (p<0.01) and α-linolenic acid in adipose tissue (p<0.001), whereas feeding B. breve DPC6330 with fatty acid supplementation to non-separated rats significantly increased liver and serum docosapentaenoic acid (p<0.05), and α-linolenic acid in adipose tissue (p<0.001). B. breve DPC6330 influenced host fatty acid metabolism. Administration of B. breve DPC6330 to maternally separated rats significantly modified the palmitoleic acid, arachidonic acid and docosahexaenoic acid contents in tissues. The effect was not observed in non-separated animals.This work was supported by the Science Foundation of Ireland – funded Centre for Science, Engineering and Technology, the Alimentary Pharmabiotic Centre

Bifidobacterium breve with a-linolenic acid alters the composition, distribution and transcription factor activity associated with metabolism and absorption of fat

This study focused on the mechanisms that fatty acid conjugating strains - Bifidobacterium breve NCIMB 702258 and Bifidobacterium breve DPC 6330 - influence lipid metabolism when ingested with α-linolenic acid (ALA) enriched diet. Four groups of BALB/c mice received ALA enriched diet (3% (w/w)) either alone or in combination with B. breve NCIMB 702258 or B. breve DPC 6330 (109 CFU/day) or unsupplemented control diet for six weeks. The overall n-3 PUFA score was increased in all groups receiving the ALA enriched diet. Hepatic peroxisomal beta oxidation increased following supplementation of the ALA enriched diet with B. breve (P < 0.05) and so the ability of the strains to produce c9t11 conjugated linoleic acid (CLA) was identified in adipose tissue. Furthermore, a strain specific effect of B. breve NCIMB 702258 was found on the endocannabinoid system (ECS). Liver triglycerides (TAG) were reduced following ALA supplementation, compared with unsupplemented controls (P < 0.01) while intervention with B. breve further reduced liver TAG (P < 0.01), compared with the ALA enriched control. These data indicate that the interactions of the gut microbiota with fatty acid metabolism directly affect host health by modulating n-3 PUFA score and the ECS

Social interaction-induced activation of RNA splicing in the amygdala of microbiome-deficient mice

Social behaviour is regulated by activity of host-associated microbiota across multiple species. However, the molecular mechanisms mediating this relationship remain elusive. We therefore determined the dynamic, stimulus-dependent transcriptional regulation of germ-free (GF) and GF mice colonised post weaning (exGF) in the amygdala, a brain region critically involved in regulating social interaction. In GF mice the dynamic response seen in controls was attenuated and replaced by a marked increase in expression of splicing factors and alternative exon usage in GF mice upon stimulation, which was even more pronounced in exGF mice. In conclusion, we demonstrate a molecular basis for how the host microbiome is crucial for a normal behavioural response during social interaction. Our data further suggest that social behaviour is correlated with the gene-expression response in the amygdala, established during neurodevelopment as a result of host-microbe interactions. Our findings may help toward understanding neurodevelopmental events leading to social behaviour dysregulation, such as those found in autism spectrum disorders (ASDs)

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Soil sample timing, nitrogen fertilization, and incubation length influence anaerobic potentially mineralizable nitrogen

Understanding the variables that affect the anaerobic potentially mineralizable N (PMNan) test should lead to a standard procedure of sample collection and incubation length, improving PMNan as a tool in corn (Zea mays L.) N management. We evaluated the effect of soil sample timing (preplant and V5 corn development stage [V5]), N fertilization (0 and 180 kg ha−1) and incubation length (7, 14, and 28 d) on PMNan (0–30 cm) across a range of soil properties and weather conditions. Soil sample timing, N fertilization, and incubation length affected PMNan differently based on soil and weather conditions. Preplant vs. V5 PMNan tended to be greater at sites that received \u3c 183 mm of precipitation or \u3c 359 growing degree-days (GDD) between preplant and V5, or had soil C/N ratios \u3e 9.7:1; otherwise, V5 PMNan tended to be greater than preplant PMNan. The PMNan tended to be greater in unfertilized vs. fertilized soil in sites with clay content \u3e 9.5%, total C \u3c 24.2 g kg−1, soil organic matter (SOM) \u3c 3.9 g kg−1, or C to N ratios \u3c 11.0:1; otherwise, PMNan tended to be greater in fertilized vs. unfertilized soil. Longer incubation lengths increased PMNan at all sites regardless of sampling methods. Since PMNan is sensitive to many factors (sample timing, N fertilization, incubation length, soil properties, and weather conditions), it is important to follow a consistent protocol to compare PMNan among sites and potentially use PMNan to improve corn N management

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions