Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature

The spatial information about crop grain protein concentration (GPC) can be an important layer (i.e., a map that can be utilized in a geographic information system) with uses from nutrient management to grain marketing. Recently, on- and off-combine harvester sensors have been developed for creating spatial GPC layers. The quality of these GPC layers, as measured by the coefficient of determination (R2) and the root mean squared error (RMSE) of the relationship between measured and predicted GPC, is affected by different sensing characteristics. The objectives of this synthesis analysis were to (i) contrast GPC prediction R2 and RMSE for different sensor types (on-combine, off-combine proximal and remote); (ii) contrast and discuss the best spatial, temporal, and spectral resolutions and features, and the best statistical approach for off-combine sensors; and (iii) review current technology limitations and provide future directions for spatial GPC research and application. On-combine sensors were more accurate than remote sensors in predicting GPC, yet with similar precision. The most optimal conditions for creating reliable GPC predictions from off-combine sensors were sensing near anthesis using multiple spectral features that include the blue and green bands, and that are analyzed by complex statistical approaches. We discussed sensor choice in regard to previously identified uses of a GPC layer, and further proposed new uses with remote sensors including same season fertilizer management for increased GPC, and in advance segregated harvest planning related to field prioritization and farm infrastructure. Limitations of the GPC literature were identified and future directions for GPC research were proposed as (i) performing GPC predictive studies on a larger variety of crops and water regimes; (ii) reporting proper GPC ground-truth calibrations; (iii) conducting proper model training, validation, and testing; (iv) reporting model fit metrics that express greater concordance with the ideal predictive model; and (v) implementing and benchmarking one or more uses for a GPC layer

Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature

The spatial information about crop grain protein concentration (GPC) can be an important layer (i.e., a map that can be utilized in a geographic information system) with uses from nutrient management to grain marketing. Recently, on- and off-combine harvester sensors have been developed for creating spatial GPC layers. The quality of these GPC layers, as measured by the coefficient of determination (R2) and the root mean squared error (RMSE) of the relationship between measured and predicted GPC, is affected by different sensing characteristics. The objectives of this synthesis analysis were to (i) contrast GPC prediction R2 and RMSE for different sensor types (on-combine, off-combine proximal and remote); (ii) contrast and discuss the best spatial, temporal, and spectral resolutions and features, and the best statistical approach for off-combine sensors; and (iii) review current technology limitations and provide future directions for spatial GPC research and application. On-combine sensors were more accurate than remote sensors in predicting GPC, yet with similar precision. The most optimal conditions for creating reliable GPC predictions from off-combine sensors were sensing near anthesis using multiple spectral features that include the blue and green bands, and that are analyzed by complex statistical approaches. We discussed sensor choice in regard to previously identified uses of a GPC layer, and further proposed new uses with remote sensors including same season fertilizer management for increased GPC, and in advance segregated harvest planning related to field prioritization and farm infrastructure. Limitations of the GPC literature were identified and future directions for GPC research were proposed as (i) performing GPC predictive studies on a larger variety of crops and water regimes; (ii) reporting proper GPC ground-truth calibrations; (iii) conducting proper model training, validation, and testing; (iv) reporting model fit metrics that express greater concordance with the ideal predictive model; and (v) implementing and benchmarking one or more uses for a GPC layer

Sulfur Fertilization in Soybean: A Meta-analysis on Yield and Seed Composition

Sulfur (S) deficiency has been recently reported in soybean [Glycine max (L.) Merr.] producing regions across the United States. However, field studies have often failed to demonstrate a strong relationship between yield and S fertilization and generally attributing the lack of yield response to unfavorable weather and high soil S supply. In addition, only a few reports described seed composition changes due to S availability under contrasting field conditions. Therefore, our goals were (i) to implement a meta-analytic model to quantify the effect of S application at different growth stages on yield and seed concentration of protein, oil, essential non-S amino acids, and S amino acids (SAA, cysteine and methionine); ii) identify environmental factors underpinning the response of S to these plant traits. Field experiments were carried out from 2017 to 2019 growing seasons with a total of 44 unique site-years conditions across 18 locations in 8 states. Mineral S fertilizer (sulfate/ elemental S) was supplied depending on the study at sowing, vegetative and/or reproductive stages. A random-effects multilevel meta-analysis was conducted. The effect sizes compared yield and seed composition responses relative to the unfertilized control. A principal component analysis (PCA) separated distinctive environmental conditions and a sub-grouped meta-analysis with the main environmental factors was later executed to understand the response of the plant traits with those factors. Seed protein concentration increased by 0.3 % when S was applied at sowing. The concentration of SAA increased by ca. 1% regardless of the fertilization timing. Sites exposed to drought stress (18–29% reduction of potential transpiration) neither presented changes in yield nor seed composition due to S fertilization. Soils with organic matter between 25 and 32 g kg-1 (medium cluster) displayed significant responses to S application. This research brings extensive data and provides a comprehensive analysis of weather and soil attributes influencing soybean yield and seed composition responses to S availability

Reflectance Measurements from Aerial and Proximal Sensors Provide Similar Precision in Predicting the Rice Yield Response to Mid-Season N Applications.

Accurately detecting nitrogen (N) deficiency and determining the need for additional N fertilizer is a key challenge to achieving precise N management in many crops, including rice (Oryza sativa L.). Many remotely sensed vegetation indices (VIs) have shown promise in this regard; however, it is not well-known if VIs measured from different sensors can be used interchangeably. The objective of this study was to quantitatively test and compare the ability of VIs measured from an aerial and proximal sensor to predict the crop yield response to top-dress N fertilizer in rice. Nitrogen fertilizer response trials were established across two years (six site-years) throughout the Sacramento Valley rice-growing region of California. At panicle initiation (PI), unmanned aircraft system (UAS) Normalized Difference Red-Edge Index (NDREUAS) and GreenSeeker (GS) Normalized Difference Vegetation Index (NDVIGS) were measured and expressed as a sufficiency index (SI) (VI of N treatment divided by VI of adjacent N-enriched area). Following reflectance measurements, each plot was split into subplots with and without top-dress N fertilizer. All metrics evaluated in this study indicated that both NDREUAS and NDVIGS performed similarly with respect to predicting the rice yield response to top-dress N at PI. Utilizing SI measurements prior to top-dress N fertilizer application resulted in a 113% and 69% increase (for NDREUAS and NDVIGS, respectively) in the precision of the rice yield response differentiation compared to the effect of applying top-dress N without SI information considered. When the SI measured via NDREUAS and NDVIGS at PI was ≤0.97 and 0.96, top-dress N applications resulted in a significant (p < 0.05) increase in crop yield of 0.19 and 0.21 Mg ha-1, respectively. These results indicate that both aerial NDREUAS and proximal NDVIGS have the potential to accurately predict the rice yield response to PI top-dress N fertilizer in this system and could serve as the basis for developing a decision support tool for farmers that could potentially inform better N management and improve N use efficiency

EFFECT OF COVER CROPS ON SOIL ATTRIBUTES, PLANT NUTRITION, AND IRRIGATED TROPICAL RICE YIELD

In flood plains, cover crops are able to alter soil properties and significantly affect rice nutrition and yield. The aims of this study were to determine soil properties, plant nutrition, and yield of tropical rice cultivated on flood plains after cover crop cultivation with conventional tillage (CT) and no-tillage system (NTS) at low and high nitrogen (N) fertilization levels. The experimental design was a randomized block in a split-split-plot scheme with four replications. In the main plots were cover crops sunhemp (Crotalaria juncea and C. spectabilis), velvet bean (Mucuna aterrima), jackbean (Canavalia ensiformis), pigeon pea (Cajanus cajan), Japanese radish (Raphanus sativus), cowpea (Vigna unguiculata) and a fallow field. In the subplots were the tillage systems (CT or NTS). The nitrogen fertilization levels in the sub-subplots were (10 kg N ha-1 and 45 kg N ha-1). All cover crops except Japanese radish significantly increased mineral soil nitrogen and nitrate concentrations. Sunhemp, velvet bean, and cowpea significantly increased soil ammonium content. The NTS provides higher mineral nitrogen and ammonium content than that by CT. Overall, cover crops provided higher levels of nutrients to rice plants in NTS than in CT. Cover crops provide greater yield than fallow treatments. Rice yield was higher in NTS than in CT, and greater at a higher rather than lower nitrogen fertilization level

Seed Inoculation with Azospirillum Brasilense in the U.S. Soybean Systems

Symbiotic nitrogen (N) fixation (SNF) is critical to satisfying the nutritional need of soybean (Glycine max (L.) Merr.) and maintaining productivity and high seed protein concentration. Due to its low environmental impact, a key factor for increasing the sustainability of soybean systems is to enhance SNF. Seed inoculation with the free-living Azospirillum brasilense alone or with Bradyrhizobium japonicum (herein called co-inoculation) are plausible strategies that have been explored in tropical environments but lack information in temperate regions. Following this rationale, this study aimed to evaluate the impact of seed inoculation with Azospirillum brasilense (herein called Azospirillum) alone or combined with Bradyrhizobium japonicum (herein called Bradyrhizobium) in a range of environments in the United States (US) for: (i) seed yield, (ii) relative abundance of ureides (RAU) as a proxy of SNF, and (iii) seed protein concentration. Twenty-five field studies across the US states with the same experimental design were performed during the 2019 and 2020 growing seasons. The primary outcomes of this research were: (i) yield responses to co-inoculation were considered significant in only 2 out of 25 site-years, (ii) RAU was not increased by Azospirillum inoculation or co-inoculation, and lastly, (iii) seed protein concentration was marginally associated with the inoculation strategies. Although Azospirillum did not impose remarkable gain in any observed plant traits, future studies should focus on mechanistically understanding whether Azospirillum can naturalize in temperate region soils. Still, strategies for enhancing SNF are required for sustainably improving productivity and quality for US soybean systems

Historical trend on seed amino acid concentration does not follow protein changes in soybeans

Abstract Soybean [Glycine max (L.) Merr.] is the most important oilseed crop for animal industry due to its high protein concentration and high relative abundance of essential and non-essential amino acids (AAs). However, the selection for high-yielding genotypes has reduced seed protein concentration over time, and little is known about its impact on AAs. The aim of this research was to determine the genetic shifts of seed composition for 18 AAs in 13 soybean genotypes released between 1980 and 2014. Additionally, we tested the effect of nitrogen (N) fertilization on protein and AAs trends. Soybean genotypes were grown in field conditions during two seasons under a control (0 N) and a N-fertilized treatment receiving 670 kg N ha−1. Seed yield increased 50% and protein decreased 1.2% comparing the oldest and newest genotypes. The application of N fertilizer did not significantly affect protein and AAs concentrations. Leucine, proline, cysteine, and tryptophan concentrations were not influenced by genotype. The other AAs concentrations showed linear rates of decrease over time ranging from − 0.021 to − 0.001 g kg−1 year−1. The shifts of 11 AAs (some essentials such as lysine, tryptophan, and threonine) displayed a relative-to-protein increasing concentration. These results provide a quantitative assessment of the trade-off between yield improvement and seed AAs concentrations and will enable future genetic yield gain without overlooking seed nutritional value