Although seed and pod traits are important for peanut breeding, little is known about the inheritance of these traits. A recombinant inbred line (RIL) population of 156 lines from a cross of Tifrunner x NC 3033 was genotyped with the Axiom_Arachis1 SNP array and SSRs to generate a genetic map composed of 1524 markers in 29 linkage groups (LG). The genetic positions of markers were compared with their physical positions on the peanut genome to confirm the validity of the linkage map and explore the distribution of recombination and potential chromosomal rearrangements. This linkage map was then used to identify Quantitative Trait Loci (QTL) for seed and pod traits that were phenotyped over three consecutive years for the purpose of developing trait-associated markers for breeding. Forty-nine QTL were identified in 14 LG for seed size index, kernel percentage, seed weight, pod weight, single-kernel, double-kernel, pod area and pod density. Twenty QTL demonstrated phenotypic variance explained (PVE) greater than 10% and eight more than 20%. Of note, seven of the eight major QTL for pod area, pod weight and seed weight (PVE >20% variance) were attributed to NC 3033 and located in a single linkage group, LG B06_1. In contrast, the most consistent QTL for kernel percentage were located on A07/B07 and derived from Tifrunner.

Traits such as seed weight, shelling percent, percent sound mature kernels, and seed dormancy determines the quality of peanut seed. Few QTL (quantitative trait loci) studies using biparental mapping populations have identified QTL for seed dormancy and seed grade traits. Here, we report a genome-wide association study (GWAS) to detect marker–trait associations for seed germination, dormancy, and seed grading traits in peanut. A total of 120 accessions from the U.S. peanut mini-core collection were evaluated for seed quality traits and genotyped using Axiom SNP (single nucleotide polymorphism) array for peanut. We observed significant variation in seed quality traits in different accessions and different botanical varieties. Through GWAS, we were able to identify multiple regions associated with sound mature kernels, seed weight, shelling percent, seed germination, and dormancy. Some of the genomic regions that were SNP associated with these traits aligned with previously known QTLs. For instance, QTL for seed dormancy has been reported on chromosome A05, and we also found SNP on the same chromosome associated with seed dormancy, explaining around 20% of phenotypic variation. In addition, we found novel genomic regions associated with seed grading, seed germination, and dormancy traits. SNP markers associated with seed quality and dormancy identified here can accelerate the selection process. Further, exploring the function of candidate genes identified in the vicinity of the associated marker will assist in understanding the complex genetic network that governs seed quality.

This study evaluates the performance of an indirect injection (IDI) diesel engine fueled with cotton seed biodiesel while assessing the engine's multi-fuel capability. Millions of tons of cotton seeds are available in the south of the US every year and approximately 10% of oil contained in the seeds can be extracted and transesterified. An investigation of combustion, emissions, and efficiency was performed using mass ratios of 20-50% cotton seed biodiesel (CS20 and CS50) in ultra-low sulfur diesel #2 (ULSD#2). Each investigation was run at 2400 rpm with loads of 4.2 - 6.3 IMEP and compared to the reference fuel ULDS#2. The ignition delay ranged in a narrow interval of 0.8-0.97ms across the blends and the heat release rate showed comparable values and trends for all fuel blends. The maximum volume averaged cylinder temperature increased by approximately 100K with each increase in 1 bar IMEP load but the maximum remained constants across the blends. The gas to cylinder walls radiation and convection heat fluxes for both ULSD#2 and CS50 were 1.8 MW/m2 and 2.5MW/m2 respectively. The energy specific fuel consumption (ESFC) for CS50 was 4.4% less than ULSD#2 at 6.3 bar IMEP. The indicated thermal efficiency of the engine ranged from approximately 45-50% while the mechanical efficiency ranged from 70-80%. The nitrogen oxide (NOx) and soot showed minimum values with the CS20 fuel blend. Overall, the study confirmed that concentrations of up to 50% cotton seed biodiesel in ULSD are effective at maintaining performance and efficiency of a small bore IDI engine and the high tolerance of such an engine to biodiesel.

Proper crop rotation is essential to maintaining high peanut yield and quality. However, the economic considerations of sustainable cropping systems must incorporate commodity prices, production costs, and yield responses of the crops within the cropping system. Research was conducted at the USDA/ARS National Peanut Research Laboratory's Multi-crop Irrigation Research Farm in Shellman, Georgia to determine the average net returns of irrigated and non-irrigated cropping systems consisting of peanut (Arachis hypogea L.), cotton (Gossypium hirsutum L.), and corn (Zea mays L.). Five replicated cropping systems provided data on yield responses from irrigated and non-irrigated rotation sequences defined as: continuous peanuts (PPP), cotton/peanuts/cotton (CPC), corn/peanuts/corn (MPM), cotton/cotton/peanuts (CCP), and cotton/corn/peanuts (CMP). The peanut yield in the PPP rotation was 3300 kg/ha in the non-irrigated treatment. Non-irrigated yields in CPC and MPM rotation sequences were 3940 and 3890 kg/ha, respectively and yields in CCP and CMP rotation sequences were 4770 and 4710 kg/ha, respectively. The peanut yield in the PPP rotation was 4080 kg/ha in the irrigated treatment. Irrigated yields in CPC and MPM rotation sequences were 5280 and 5230 kg/ha, respectively and yields in CCP and CMP rotation sequences were 5940 and 6010 kg/ha, respectively. The economic returns of the cropping systems were analyzed for 3 different price level combinations. Production costs (variable and fixed) were obtained from partial budgets. Returns were defined as the 3 year average net returns of each cropping system and were calculated for each price level combination which resulted in 57 comparable average net returns for the irrigated and non-irrigated treatments. Net returns were influenced by rotation sequence, price, and irrigation.