Advancing agricultural research using machine learning algorithms

Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and background management combinations that are not applicable nor translatable to all farms. A method that accurately evaluates the effectiveness of infinite cropping system interactions (involving multiple management practices) to increase maize and soybean yield across the US does not exist. Here, we utilize extensive databases and artificial intelligence algorithms and show that complex interactions, which cannot be evaluated in replicated trials, are associated with large crop yield variability and thus, potential for substantial yield increases. Our approach can accelerate agricultural research, identify sustainable practices, and help overcome future food demands

Soybean Yield, Evapotranspiration, Water Productivity, And Soil Water Extraction Response To Subsurface Drip Irrigation And Fertigation

Soybean [Glycine max (L.) Merr.] yield, irrigation water use efficiency (IWUE), crop water use efficiency (CWUE), evapotranspiration water use efficiency (ETWUE), and soil water extraction response to eleven treatments of full, limited, or delayed irrigation versus a rainfed control were investigated using a subsurface drip irrigation (SDI) system at a research site in south-central Nebraska. The SDI system laterals were 0.40 m deep in every other row middle of 0.76 m spaced plant rows. Actual evapotranspiration (ETa) was quantified in all treatments and used to schedule irrigation events on a 100% ETa replacement basis in all but three of the eleven treatments (i.e., 75% ETa replacement was used in two, and 60% ETa replacement was used in one). The irrigation amount (Ia) applied at each event was 100% of the ETa amount, except for two 100% ETa treatments in which only 65% or 50% of the water needed to cover the treatment plot area was applied to enable a test of a partial surface area-based irrigation approach. The first irrigation event was delayed until soybean stage R3 (begin pod) in two 100% Ia treatments, but thereafter they were irrigated with either 100% or 75% ETa replacement. Two 100% ETa and 100% Ia treatments also were used to evaluate soybean response to nitrogen (N) application methods (i.e., a preplant method versus N injection using the SDI system). Soybean ETa varied from 452 mm for the rainfed treatment to 600 mm (30% greater) for the fully irrigated treatment (100% ETa and 100% Ia) in 2007, and from 473 to 579 mm (20% greater) for the same treatments, respectively, in 2008. Among the irrigated treatments, 100% ETa and 65% Ia had the lowest 2007 ETa value (557 mm), whereas 100% ETa and 50% Ia had the lowest 2008 ETa (498 mm). The 100%, 75%, and 60% ETa treatments with 100% Ia had respective actual ETa values that declined linearly in 2008 (i.e., 579, 538, and 498 mm), but not in 2007. Seasonal totals for ETa versus Ia exhibited a linear relationship (R2 = 0.68 in 2007 and R2 = 0.67 in 2008). Irrigation enhanced soybean yields from rainfed yield baselines of 4.04 ton ha-1 in 2007 and 4.82 ton ha-1 in 2008) to a maximum of 4.94 ton ha-1 attained in 2007 with the delay to R3 irrigation treatment (its yield was significantly greater, p \u3c 0.05, than that of the seven other treatments) and 4.97 ton ha-1 attained in 2008 with the 100% ETa and 100% Ia preplant N treatment. Seed yield had a quadratic relationship with irrigation water applied and a linear relationship with ETa that was stronger in the drier year of 2007. Each 25.4 mm incremental increase in seasonal irrigation water applied increased soybean yield by 0.323 ton ha-1 (beyond the intercept) in 2007 and by 0.037 ton ha-1 in 2008. Each 25.4 mm increase in ETa generated a yield increase of 0.114 ton ha-1 (beyond the intercept) in 2007, but only 0.02 ton ha-1 in the wetter year of 2008. This research demonstrated that delaying the onset of irrigation until the R3 stage and practicing full irrigation thereafter for soybean grown on silt loam soils resulted in yields (and crop water productivity) that were similar to full-season irrigation scheduling strategies, and this result may be applicable in other regions with edaphic and climatic characteristics similar to those in south-central Nebraska

Soybean Yield, Evapotranspiration, Water Productivity, And Soil Water Extraction Response To Subsurface Drip Irrigation And Fertigation

Soybean [Glycine max (L.) Merr.] yield, irrigation water use efficiency (IWUE), crop water use efficiency (CWUE), evapotranspiration water use efficiency (ETWUE), and soil water extraction response to eleven treatments of full, limited, or delayed irrigation versus a rainfed control were investigated using a subsurface drip irrigation (SDI) system at a research site in south-central Nebraska. The SDI system laterals were 0.40 m deep in every other row middle of 0.76 m spaced plant rows. Actual evapotranspiration (ETa) was quantified in all treatments and used to schedule irrigation events on a 100% ETa replacement basis in all but three of the eleven treatments (i.e., 75% ETa replacement was used in two, and 60% ETa replacement was used in one). The irrigation amount (Ia) applied at each event was 100% of the ETa amount, except for two 100% ETa treatments in which only 65% or 50% of the water needed to cover the treatment plot area was applied to enable a test of a partial surface area-based irrigation approach. The first irrigation event was delayed until soybean stage R3 (begin pod) in two 100% Ia treatments, but thereafter they were irrigated with either 100% or 75% ETa replacement. Two 100% ETa and 100% Ia treatments also were used to evaluate soybean response to nitrogen (N) application methods (i.e., a preplant method versus N injection using the SDI system). Soybean ETa varied from 452 mm for the rainfed treatment to 600 mm (30% greater) for the fully irrigated treatment (100% ETa and 100% Ia) in 2007, and from 473 to 579 mm (20% greater) for the same treatments, respectively, in 2008. Among the irrigated treatments, 100% ETa and 65% Ia had the lowest 2007 ETa value (557 mm), whereas 100% ETa and 50% Ia had the lowest 2008 ETa (498 mm). The 100%, 75%, and 60% ETa treatments with 100% Ia had respective actual ETa values that declined linearly in 2008 (i.e., 579, 538, and 498 mm), but not in 2007. Seasonal totals for ETa versus Ia exhibited a linear relationship (R2 = 0.68 in 2007 and R2 = 0.67 in 2008). Irrigation enhanced soybean yields from rainfed yield baselines of 4.04 ton ha-1 in 2007 and 4.82 ton ha-1 in 2008) to a maximum of 4.94 ton ha-1 attained in 2007 with the delay to R3 irrigation treatment (its yield was significantly greater, p \u3c 0.05, than that of the seven other treatments) and 4.97 ton ha-1 attained in 2008 with the 100% ETa and 100% Ia preplant N treatment. Seed yield had a quadratic relationship with irrigation water applied and a linear relationship with ETa that was stronger in the drier year of 2007. Each 25.4 mm incremental increase in seasonal irrigation water applied increased soybean yield by 0.323 ton ha-1 (beyond the intercept) in 2007 and by 0.037 ton ha-1 in 2008. Each 25.4 mm increase in ETa generated a yield increase of 0.114 ton ha-1 (beyond the intercept) in 2007, but only 0.02 ton ha-1 in the wetter year of 2008. This research demonstrated that delaying the onset of irrigation until the R3 stage and practicing full irrigation thereafter for soybean grown on silt loam soils resulted in yields (and crop water productivity) that were similar to full-season irrigation scheduling strategies, and this result may be applicable in other regions with edaphic and climatic characteristics similar to those in south-central Nebraska

Development of Single-Seed Near-Infrared Spectroscopic Predictions of Corn and Soybean Constituents using Bulk Reference Values and Mean Spectra

Rapid, non-destructive single-seed compositional analyses are useful for many areas of crop science, including breeding and genetics. Seeds are sometimes unique and require preservation due to small samples, which necessitates development of methods for total non-destructive measurement. Near-infrared reflectance spectroscopy (NIRS) can be used for non-destructive single-seed composition prediction, but the reference methods used to develop prediction models are usually destructive. Reference methods are costly, and extensive sets of seeds must be used to obtain prediction models for multiple constituents. In this research, single-seed NIRS prediction models were developed for common constituents of soybeans and corn using composition values from bulk reference measurement and respective averaged single-seed spectra as opposed to single-seed reference and spectra. The bulk reference model and a true single-seed model for soybean protein were also compared to determine how well the bulk model performs in predicting single-seed protein. This provided a basis for evaluating bulk model performance for other constituents. Bulk model statistics indicated that bulk models should perform well for soybean protein and oil, but not well for fiber; corn bulk models should perform well for protein, oil, starch, and seed density. Bulk model predictions of single-seed soybean reference protein show, at best, that bulk models work reasonably well, with a standard error of prediction (SEP) = 1.82%) compared to an SEP of 0.97% for a true single-seed protein model. Bias correction may be needed, though, depending how the bulk model is developed. Overall, the bulk models should be useful for selecting single seeds in breeding programs targeting specific composition traits and segregating individual samples based on composition

Dissecting the Genetic Basis of Local Adaptation in Soybean

Soybean (Glycine max) is the most widely grown oilseed in the world and is an important source of protein for both humans and livestock. Soybean is widely adapted to both temperate and tropical regions, but a changing climate demands a better understanding of adaptation to specific environmental conditions. Here, we explore genetic variation in a collection of 3,012 georeferenced, locally adapted landraces from a broad geographical range to help elucidate the genetic basis of local adaptation. We used geographic origin, environmental data and dense genome-wide SNP data to perform an environmental association analysis and discover loci displaying steep gradients in allele frequency across geographical distance and between landrace and modern cultivars. Our combined application of methods in environmental association mapping and detection of selection targets provide a better understanding of how geography and selection may have shaped genetic variation among soybean landraces. Moreover, we identified several important candidate genes related to drought and heat stress, and revealed important genomic regions possibly involved in the geographic divergence of soybean

Genome-wide Association Mapping of Qualitatively Inherited Traits in a Germplasm Collection

Genome-wide association (GWA) has been used as a tool for dissecting the genetic architecture of quantitatively inherited traits. We demonstrate here that GWA can also be highly useful for detecting many major genes governing categorically defined phenotype variants that exist for qualitatively inherited traits in a germplasm collection. Genome-wide association mapping was applied to categorical phenotypic data available for 10 descriptive traits in a collection of ~13,000 soybean [Glycine max (L.) Merr.] accessions that had been genotyped with a 50,000 single nucleotide polymorphism (SNP) chip. A GWA on a panel of accessions of this magnitude can offer substantial statistical power and mapping resolution, and we found that GWA mapping resulted in the identification of strong SNP signals for 24 classical genes as well as several heretofore unknown genes controlling the phenotypic variants in those traits. Because some of these genes had been cloned, we were able to show that the narrow GWA mapping SNP signal regions that we detected for the phenotypic variants had chromosomal bp spans that, with just one exception, overlapped the bp region of the cloned genes, despite local variation in SNP number and nonuniform SNP distribution in the chip set

Changes In Nitrogen Use Efficiency And Soil Quality After Five Years Of Managing For High Yield Corn And Soybean

Average corn grain yields in the USA have increased linearly at a rate of 1.7 bu/acre over the past 35 years with a national yield average of 140 bu/acre. Corn yield contest winners and simulation models, however, indicate there is ~100 bu/a in exploitable corn yield gap. Four years (1999-2002) of plant development, grain yield and nutrient uptake were compared in intensive irrigated maize systems representing (a) recommended best management practices for a yield goal of 200 bu/acre (M1) and (b) intensive management aiming at a yield goal of 300 bu/acre (M2). For each management level, three levels of plant density (30000-P1, 37000-P2 and 44000-P3 seed/acre) were compared in a continuous corn and corn- soybean rotation. Over five years, the grain yields increased 11% as a function of management and this effect was manifest under higher plant densities. A high yield of 285 bu/acre was achieved at the M2, P2 treatment in 2003. Higher population resulted in greater demand for N and K per unit grain yield. Over the past five years, nitrogen use efficiency has steadily improved in the M2 treatment due to improvements in soil quality. Intensive management and population levels significantly increased residue carbon inputs with disproportionately lower soil respiration. Closing the yield gap requires higher plant population and improved nutrient management to maintain efficient and profitable improvement in maize production. Soil quality improvements and higher residue inputs under intensive management should make this task easier with time

Insufficient nitrogen supply from symbiotic fixation reduces seasonal crop growth and nitrogen mobilization to seed in highly productive soybean crops

Nitrogen (N) supply can limit the yields of soybean [Glycine max (L.) Merr.] in highly productive environments. To explore the physiological mechanisms underlying this limitation, seasonal changes in N dynamics, aboveground dry matter (ADM) accumulation, leaf area index (LAI) and fraction of absorbed radiation (fAPAR) were compared in crops relying only on biological N2 fixation and available soil N (zero-N treatment) versus crops receiving N fertilizer (full-N treatment). Experiments were conducted in seven high-yield environments without water limitation, where crops received optimal management. In the zero-N treatment, biological N2 fixation was not sufficient to meet the N demand of the growing crop from early in the season up to beginning of seed filling. As a result, crop LAI, growth, N accumulation, radiation-use efficiency and fAPAR were consistently higher in the full-N than in the zero-N treatment, leading to improved seed set and yield. Similarly, plants in the full-N treatment had heavier seeds with higher N concentration because of greater N mobilization from vegetative organs to seeds. Future yield gains in high-yield soybean production systems will require an increase in biological N2 fixation, greater supply of N from soil or fertilizer, or alleviation of the trade-off between these two sources of N in order to meet the plant demand.Fil: Cafaro la Menza, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Nebraska - Lincoln; Estados UnidosFil: Monzon, Juan Pablo. Universidad de Nebraska - Lincoln; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Lindquist, John L.. Universidad de Nebraska - Lincoln; Estados UnidosFil: Arkebauer, Timothy J.. Universidad de Nebraska - Lincoln; Estados UnidosFil: Knops, Johannes M. H.. Universidad de Nebraska - Lincoln; Estados UnidosFil: Unkovich, Murray. University of Adelaide; AustraliaFil: Specht, James E.. Universidad de Nebraska - Lincoln; Estados UnidosFil: Grassini, Patricio. Universidad de Nebraska - Lincoln; Estados Unido

Fine mapping and cloning of the major seed protein quantitative trait loci on soybean chromosome 20

Soybean [Glycine max (L.) Merr.] is a unique crop species because it has high levels of both protein and oil in its seed. Of the many quantitative trait loci (QTL) controlling soybean seed protein content, alleles of the cqSeed protein-003 QTL on chromosome 20 exert the greatest additive effect. The high-protein allele exists in both cultivated and wild soybean (Glycine soja Siebold & Zucc.) germplasm. Our objective was to fine map this QTL to enable positional-based cloning of its underlying causative gene(s). Fine mapping was achieved by developing and testing a series of populations in which the chromosomal region surrounding the segregating high- versus low-protein alleles was gradually narrowed, using marker-based detection of recombinant events. The resultant 77.8 kb interval was directly sequenced from a G. soja source and compared with the reference genome to identify structural and sequence polymorphisms. An insertion/deletion variant detected in Glyma.20G85100 was found to have near-perfect +/- concordance with high/low-protein allele genotypes inferred for this QTL in parents of published mapping populations. The indel structure was concordant with an evolutionarily recent insertion of a TIR transposon into the gene in the low-protein lineage. Seed protein was significantly greater in soybean expressing an RNAi hairpin downregulation element in two independent events relative to control null segregant lineages. We conclude that a transposon insertion within the CCT domain protein encoded by the Glyma.20G85100 gene accounts for the high/low seed protein alleles of the cqSeed protein-003 QTL

High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence

Background: The Soybean Consensus Map 4.0 facilitated the anchoring of 95.6% of the soybean whole genome sequence developed by the Joint Genome Institute, Department of Energy, but its marker density was only sufficient to properly orient 66% of the sequence scaffolds. The discovery and genetic mapping of more single nucleotide polymorphism (SNP) markers were needed to anchor and orient the remaining genome sequence. To that end, next generation sequencing and high-throughput genotyping were combined to obtain a much higher resolution genetic map that could be used to anchor and orient most of the remaining sequence and to help validate the integrity of the existing scaffold builds. Results: A total of 7,108 to 25,047 predicted SNPs were discovered using a reduced representation library that was subsequently sequenced by the Illumina sequence-by-synthesis method on the clonal single molecule array platform. Using multiple SNP prediction methods, the validation rate of these SNPs ranged from 79% to 92.5%. A high resolution genetic map using 444 recombinant inbred lines was created with 1,790 SNP markers. Of the 1,790 mapped SNP markers, 1,240 markers had been selectively chosen to target existing un-anchored or un-oriented sequence scaffolds, thereby increasing the amount of anchored sequence to 97%. Conclusion: We have demonstrated how next generation sequencing was combined with high-throughput SNP detection assays to quickly discover large numbers of SNPs. Those SNPs were then used to create a high resolution genetic map that assisted in the assembly of scaffolds from the 8× whole genome shotgun sequences into pseudomolecules corresponding to chromosomes of the organism