Effectiveness of Combined Biochar and Lignite with Poultry Litter on Soil Carbon Sequestration and Soil Health

Healthy soils are important to ensure satisfactory crop growth and yield. Poultry litter (PL), as an organic fertilizer, has proven to supply the soil with essential macro and micronutrients, enhance soil fertility, and improve crop productivity. Integrating this treatment has the potential to improve soil physical and biological properties by increasing soil carbon, C. However, rapid decomposition and mineralization of PL, particularly in the hot and humid southeastern U.S., resulted in losing C and reduced its effect on soil health. Biochar and lignite have been proposed to stabilize and mitigate C loss through application of fresh manure. However, their combined effects with PL on C sequestration and soil health components are limited. A field experiment was conducted on Leeper silty clay loam soil from 2017 to 2020 to evaluate the combined effect on soil properties when applying biochar and lignite with PL to cotton (Gossypium hirsutum L.). The experimental design was a randomized complete block involving nine treatments replicated three times. Treatments included PL and inorganic nitrogen, N, fertilizer with or without biochar and lignite, and an unfertilized control. Application rates were 6.7 Mgkg⋅ha−1 for PL, 6.7 Mgkg⋅ha−1 for biochar and lignite and 134 kg⋅ha−1 for inorganic N fertilizer. Integration of PL and inorganic fertilizer with biochar and lignite, resulted in greater soil infiltration, aggregate stability, plant available water, reduced bulk density and penetration resistance as compared to the sole applications of PL and inorganic fertilizer.

Effects of Plant Density on Boll Retention and Yield of Cotton in the Mid-South

The number of cotton (Gossypium hirsutum L.) plants being grown per unit of land area has gained attention due to the technology fees associated with seed containing value added traits. We investigated boll retention, yield, and yield components of cotton grown with reduced stands of 20% to 40% from the uniform planting pattern of four seeds per 30.5 cm of row. Five field experiments were conducted from 2012-2014 using eight treatments arranged in a randomized complete design with six replications. Yield and yield component data were collected. The plant one-row skip one-row treatment resulted in significant yield losses across all five experiments compared to the uniform planting pattern. Treatments with 20% stand reductions did not result in lower total yields;however, each plant in these treatments had to produce two additional bolls to maintain yield. Treatments which had at least 61 cm skips, 40% stand reduction, resulted in lower yields. Treatments had minor affects on boll weight, and lint percentage. The uniform planting pattern produced 67% of its yield from position one bolls compared to about 50% for treatments with reduced stands. Reduced stand treatments produced about 20% of their yield on monopodial branches compared to 10% for the uniform treatment. With modern precision planting equipments, opportunities exist to reduce seed rate and maintain yield;however, many production risk factors must also be considered before a reduced seeding rate is adopted.

An Adaptive Sequential Replacement Method for Variable Selection in Linear Regression Analysis

With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.

Biochar derived from papermill factories improves soil physical and hydraulic properties in no-till cotton fields

Whether biochar produced as a by-product of energy generation from the papermill industry,and often disposed in landfills,can be gainfully applied to commercial croplands has not been investigated.The objective of this study was to investigate the physical and hydraulic properties of soils in commercial cotton fields managed as no-till systems following repeated applications of biochar generated as a waste of a papermill plant.Undisturbed cores and disturbed soil samples were collected from 0-5 and 5-10 cm layers from five commercial no-till fields in Mississippi,USA that received 6.7 Mg ha^(−1) year^(−1) biochar for 0,2,3,5 or 10 years.A number of physical,hydraulic,and chemical properties of these samples were measured in the lab.The results showed that biochar reduced the degree of soil compactness and increased soil aggregation and structural stability index.The findings were particularly apparent for the 10 years of consecutive application,which increased soil aggregate stability by up to 67%,reduced bulk density from 1.40 to 1.26 g cm^(−3),and reduced degree of compactness from 73.2%to 62.8%.Biochar increased soil porosity but much of this increase(55%)occurred for small pores(<0.5μm)with little effect on storage pores(0.5-50μm)or transmission pores(>50μm).Consequently,biochar increased soil field capacity by up to 26%,but PAW increased by only 17%.Biochar significantly increased soil physical quality index score in the 0-5 cm layer from 0.16 to 0.26 and the increase was positively correlated with the number of years of application.The results suggest biochar generated as a byproduct of papermill could be land-applied in real-world crop production systems to improve soil health as an alternative to disposal in landfills.

Evaluation of Moving Grid Adjustment (MGA) Method in Field Variation Control

Spatial variation is often encountered when large scale field trials are conducted which can result in biased estimation or prediction of treatment (i.e. genotype) values. An effective removal of spatial variation is needed to ensure unbiased estimation or prediction and thus increase the accuracy of field data evaluation. A moving grid adjustment (MGA) method, which was proposed by Technow, was evaluated through Monte Carlo simulation for its statistical properties regarding field spatial variation control. Our simulation results showed that the MGA method can effectively account for field spatial variation if it does exist;however, this method will not change phenotype results if field spatial variation does not exist. The MGA method was applied to a large-scale cotton field trial data set with two representative agronomic traits: lint yield (strong field spatial pattern) and lint percentage (no field spatial pattern). The results suggested that the MGA method was able to effectively separate the spatial variation including blocking effects from random error variation for lint yield while the adjusted data remained almost identical to the original phenotypic data. With application of the MGA method, the estimated variance for residuals was significantly reduced (62.2% decrease) for lint yield while more genetic variation (29.7% increase) was detected compared to the original data analysis subject to the conventional randomized complete block design analysis. On the other hand, the results were almost identical for lint percentage with and without the application of the MGA method. Therefore, the MGA method can be a useful addition to enhance data analysis when field spatial pattern exists.

基于KINEROS2对坡面径流侵蚀及总氮流失的模拟分析

坡面土壤侵蚀及养分流失是造成土地生产力下降、地表水体富营养化等环境问题的主要原因.采用物理模型对次降雨条件下坡面径流量、侵蚀量及养分流失量进行准确估算具有重要意义.本文通过不同降雨强度(20、50、75mm/h)、不同坡长(1、5、10、15、20m)下径流小区内人工模拟降雨试验,分析了坡面径流侵蚀特征及总氮(TN)流失特征,构建了估算坡面总氮流失量的经验模型,并将其嵌入基于次降雨的KINEROS2(A Kinematic Runoff and Erosion Model)模型中进行模拟验证.结果表明:降雨强度是影响坡面径流率、侵蚀率及总氮流失率的主要因素;降雨强度、坡长及其交互作用与径流量、侵蚀量之间呈多项式关系(决定系数R^2>0.95);坡面总氮主要以结合态的形式流失,且坡面TN流失量与径流量、侵蚀量之间呈一元线性关系(R^2>0.92);KINEROS2对次降雨条件下坡面径流量、侵蚀量的模拟精度较高(相对误差RE,0.03%~11.31%);嵌入坡面TN流失模型的KINEROS2对径流中TN流失量、泥沙中TN流失量及坡面TN流失量模拟效果较好(R^2>0.91,纳什系数NSE>0.76).