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On‐farm research has emerged in recent years as a unique approach to involve farmers and other agricultural stakeholders as active participants in knowledge development and as an effective method of technology and innovation transfer across farms. This study assessed the perspective and knowledge of US farmers regarding on‐farm research via the implementation of a 24‐question survey distributed across most of the US Midwest and South‐Central regions. We found that farmers generally are willing to engage with universities to conduct on‐farm research and were 40% more likely to adopt practices supported by on‐farm research findings than research not conducted on‐farm. Notably, a shift toward conservation practices was made, with cover crops and no‐till at the forefront. Insights of this nature have implications for fostering collaborations, addressing constraints, and maximizing the impact of on‐farm research, offering guidance for sustainable agriculture progress in the United States and beyond. Results from this research survey could be used to initiate much‐needed policies to promote on‐farm research. Further, information on the benefits and drawbacks of on‐farm research could be used in the development of studies that benefit both farmers and researchers. Farmers are likely to collaborate with universities to conduct on‐farm research.Farmers were 40% more likely to adopt practices supported by findings of on‐farm research compared to off‐farm research.The farmers’ primary motivators for doing on‐farm research were productivity and profitability.The farmers’ primary source of field information is universities.Results from this research survey can drive further policy and initiatives promoting on‐farm research.

Sorghum [Sorghum bicolor(L.) Moench] is an important staple food for human consumption and a source of animal feed in the semiarid regions of the world. Sustained positive rates of crop improvement are necessary to supply food and feed to a growing population. However, land allocated to sorghum and its inclusion in production systems has been in constant decline. Here we report the rate of sorghum genetic gain in a commercial breeding program in the United States and provide evidence that a modest yield improvement is an important factor limiting land allocation to this crop. A 6‐year study that evaluated 50 sorghum genotypes commercialized between the decades of 1960 and 2010 was conducted in 19 environments within the US Sorghum Belt region. Yield varied between 500 and 850 g m−2. Here we show a positive rate of genetic gain of 2.63 g m−2y−1on average across three different maturity groups grown in the United States. Rates ranged from 2.1 to 4.3 g m−2y−1across maturity groups. This result contrasts with a stagnant rate of crop improvement for many regions of the world, yet the rates are insufficient to reverse the negative trend in planted area. Breeding technologies are proposed to hasten genetic gain in sorghum to reverse the loss of on‐farm agricultural biodiversity. Genetic gain dependence on maturity in sorghum breeding is unknown.Relative genetic gain within and among C4 cereals may determine land allocation.Overall low genetic gain in sorghum is negatively affecting agricultural biodiversity.

Understanding physiological changes in response to long‐term selection for yield can inform breeding decisions and hasten genetic gain. The objective of this study was to characterize changes over time in yield‐relevant physiological traits for hybrids with different years of release for grain sorghum (Sorghum bicolorL. Moench). Field trials were conducted during the 2018 and 2019 seasons in 8 site‐years across the states of Kansas and Texas including 20 commercially available grain sorghum hybrids released by Pioneer between 1963 and 2017. Detailed yield‐related physiological traits were determined in 2 site‐years including grain yield and its components, grain filling, plant biomass, panicle length, and water‐soluble carbohydrates (WSC) during the reproductive period. Consistent with estimates using historical yield data, sorghum yield improvement was 27 kg ha−1yr−1. For the 2 site‐years with detailed yield‐related physiological traits, no changes in final grain weight, grain‐filling duration, and rate over time were documented. In contrast, grain number increased at a rate of 100 grains m−2yr−1. Modern hybrids had larger panicle size and showed greater accumulation of WSC during vegetative period (as measured at the start of flowering) and greater remobilization of WSC during the reproductive period (after flowering) to grain, thus, maintaining grain size on the increased grain number per unit area and harvest index. These findings suggest that WSC dynamics play a critical role on past genetic yield gain in sorghum and its potential for future improvements should be considered. Sorghum yield improvement was 27 kg ha−1yr−1over the last six decades in the United States.Grain number increased at 100 grains m−2yr−1, with also a 0.4% per year gain in harvest index.No changes in grain size, and neither in grain‐filling duration nor in the rate were recorded.Modern hybrids had larger panicle size with greater water‐soluble carbohydrates.Water‐soluble carbohydrates remobilization plays a key role in sustaining grain size with increased grain number.

Retrospective studies are critical to investigate changes in physiological traits related to nitrogen (N) uptake and yield formation. N is a key yield‐limiting nutrient in sorghum (Sorghum bicolorL. Moench), as in most crops, so improving its N internal efficiency (NIE, yield to N uptake ratio) could increase productivity and sustainability. Understanding changes in N efficiency across time linked to carbon (C) economy can provide insights for future breeding targets. This study aims to characterize N and C dynamics (via studying changes in water‐soluble carbohydrates, WSC) of 20 commercial U.S. sorghum hybrids released from 1963 to 2017. Field trials were conducted during the 2018 and 2019 growing seasons in Riley County, KS. Seasonal changes in biomass accumulation, N uptake, and WSC were characterized during both vegetative and reproductive periods. Modern hybrids had greater NIE compared with those from the 1960s. Overall, beyond the yield gain documented for modern sorghum hybrids, the NIE increased at the expense of a reduction in grain N and, in minor proportion, due to increase in the N harvest index (NHI) at maturity for newer relative to older hybrids. Newer hybrids evidenced greater N remobilization from the stover to the grains during the reproductive period. This study demonstrates the physiological mechanisms for an increase in N and C utilization behind yield genetic gain for sorghum hybrids. Future yield gain in sorghum could be pursued by enhancing N uptake to sustain further genetic progress. Nitrogen internal efficiency (NIE) increased during the past six decades for U.S. sorghum.Improvement on NIE was mainly linked to yield gain and dilution in grain N in modern hybrids.Nitrogen remobilization plays a key role in the historical enhancement of NIE in sorghum.Stover N content at maturity decreased over time to sustain grain N demand.

This study aimed to (i) investigate the soybean phosphorus (P) uptake linked to changes in soil P fractions, (ii) evaluate the influence of soil P level and soil texture on changes in the forms of P in soybean organs, and (iii) explore the contents of P in the plant organs as potential indicators of changes in soil P fractions. Soybean plants were grown under sand and clay soil textures (<20% and 41–60% clay, respectively) and contrasting P levels (Mehlich-1): sand high and low P (80 and 8 mg dm−3, respectively), clay high and low P (45 and 4 mg dm−3, respectively). Chemical P fractionations were performed in the rhizosphere soil and plant organs (roots, stems and petioles, leaves, and pods). The outcomes indicated that inorganic–P increased in the soil rhizosphere after soybean cultivation. Soil P pools with moderate and low lability, such as organic–P and calcium (Ca)–P, can be accessed in the rhizosphere, even under high P availability. Moreover, the P uptake better correlates with changes in the soil available–P (extracted by Mehlich-3). Lastly, the level of P in the soil differently affected P forms in the plant organs. In both soil textures, low P levels decreased the organic–P in the pods, the non-soluble–P in the roots, and the inorganic–P in plant leaves, stems, and petioles. Our study showed the possibility of using plant variables to predict soil P changes, representing a significant advance in understanding plant-soil interactions.

Seed expense is a critical component affecting soybean (Glycine maxL.) production cost. Achieving potential benefits of adjusting seeding rates by yield environment requires translation from theory to practice through decision support tools. This article introduces the soybean variable‐rate seeding (VRS) simulator, an interactive web‐tool designed to facilitate VRS decisions. A group of agronomists and farmers provided input and feedback to the tool's original design, usability, interactive features, and tool outputs. The simulator generates variable rate seeding recommendations by (a) aggregating and classifying 3 yr of historical soybean yield maps using either yield level (low, medium, or high) or temporal yield stability (stable or unstable) classes, and (b) estimating the difference between the farmers current uniform seeding rate and the optimal seeding rates calibrated for different yield environments of the U.S. Midwest region. For each yield environment, the tool suggests a seeding rate change accompanied by an expected yield increase. Marginal economic and break‐even price analyses of each VRS prescription choice are also provided considering soybean plant survival rate, seed cost, market price, and crop production costs. A step‐by‐step description is provided using a dataset of real, historical, on‐farm soybean yield maps to illustrate the tool's features and limitations. Simulated results indicate that compared to typical fixed seeding rate choices, following the tool's recommendations could improve profits between US$5 and 57 ha–1. The development of this tool contributes to a growing trend of user‐friendly interfaces that assist in translating core agronomic knowledge into practical solutions. The soybean variable‐rate seeding simulator is an interactive web‐tool designed to facilitate variable rate seeding decisions.The simulator converts 3 yr of yield maps into variable seeding rate recommendations.The simulator provides economic results based on yield environment, plant survival rate, seed cost, and market price.Potential benefits of recommendations ranged from US$5–57 ha–1compared with fixed seeding rates.This tool contributes to translating core agronomic knowledge into practical solutions.

One of the main challenges of using low plant densities in restrictive and variable environments is to maximize the use of resources in better‐than‐expected years. In this context, tillering could be an alternative to increase reproductive and vegetative plasticity. The objectives of this study were to (a) characterize the correlation between environmental conditions, tiller traits, crop grain yield, and grain yield advantages due to tillers; (b) determine the grain yield response to tillering (i.e., grain yield difference between tillered and nontillered crops) for a wide range of environments; and (c) to evaluate the effect of tiller presence on grain yield of the main shoot, considering its effect on the apical and subapical ears. Tillered and nontillered crops were evaluated under rainfed conditions during two seasons (2018–2019 and 2019–2020). These experiments were carried out at 11 sites in the southern Argentinean Pampas, varying sowing date (22 Oct. to 5 Dec.), plant density (2–3 plants m−2), genotype (AX7784 and AX7761), and soil depth. Grain yield (3.2–11.9 Mg ha−1) was correlated with tillers productivity, mainly explained by postflowering precipitations. The contribution of tillers to grain yield was more proportional than their consequent yield depression at the main shoot. Tillers either increased (3 sites) or maintained (8 sites) grain yield, and their contribution increased as the environment improved without any detrimental effect in the most restrictive environments. Tillering has the potential for increasing resource (radiation, water, nitrogen) use efficiency under low plant density strategies adopted for restrictive environments. Tillers either increased (3 sites) or maintained (8 sites) crop yield.Grain yield was correlated with tillers productivity, mainly explained by postflowering precipitations.Tiller contribution to grain yield was greater than the consequent yield depression of the main shoot.Tiller contribution to yield increased as the environment improved (high yield).Tillering has the potential for increasing resource use efficiency under low plant density strategies.

Studies at a regional scale suggest that although maize (Zea maysL.) yield increased substantially, sensitivity to water deficit also increased concurrently with crop improvement. This study assessed changes in yield and yield stability after two decades of breeding by evaluating two cohorts of hybrids released by the AQUAmax program and comparing them to a non‐AQUAmax control. Studies were conducted in 2019 and 2020 seasons at four sites under well‐watered, moderate and severe stress conditions. Plant densities varied from 2.5 to 12.5 pl m−2. AQUAmax hybrids yielded more than non‐AQUAmax hybrids under water deficit conditions with the magnitude of the difference dependent on plant density. The sensitivity in yield across environments with a wide range of total crop evapotranspiration (350–800 mm) was lower for AQUAmax than non‐AQUAmax, so both yield and yield under stress conditions (yield stability), were enhanced for AQUAmax hybrids. The median differences between observed yields and the 80% quantile yield–evapotranspiration front were 379 for AQUAmax and 427 g m−2for non‐AQUAmax hybrids. We conclude that deliberate selection of hybrids for yield performance under water deficit underpinned the sustained improvement in yield stability after two decades of drought breeding. Future research should focus on understanding the root causes for the suboptimal utilization of drought tolerant maize for the diversity of environments at a regional scale. Yield improvement for drought is critical to enable a circular bioeconomy.Maize susceptibility to drought at regional scale is increasing with crop improvement.Genotype × management technology (G×M‐tech) is not the root cause of the higher instability.Socioeconomic factors and policy may underpin suboptimal use of G×M‐tech at regional scale.

The soybean [Glycine max(L.) Merr.] compositional quality is mainly provided by the seed concentration of protein and oil. These traits are critical for sustaining global use, and although there is demand for high protein soybean, no mechanism to differentiate production is in place. At the opposite end of the supply chain, farmers are remunerated on a mass basis without having any incentive regarding seed composition. This study evaluated farmers' perspectives and knowledge on soybean quality and their propensity to adopt quality improvement technologies. Farmers from the main U.S. producing regions (n = 271) were investigated with a self‐administrated survey containing 21 questions during 2020 and 2021. Our results show that 84% are unaware of the current protein and oil levels from their own production. A small portion (1.4%) make management decisions (e.g., choice of genotypes or monitor quality) based on the implications on seed quality. However, practices already in place are likely to enhance the quality of seed, namely N nutrition (via rhizobia [12.9%] or fertilizer [5.9%]) and late‐season crop protection (17.1%). If farmers are financially rewarded by US$0.50 per bushel, a mindset change may occur. Based on these results, we concluded that shifts in the U.S. production system targeting protein or oil markets are possible, and the constraints are mainly related to on‐farm management. However, the challenges for improving the U.S. soybean competitiveness in global or niche markets also rely upon other segments of the production chain, specifically breeders, technology suppliers, and logistical structure. Crop management linked to seed compositional quality is already carried out in soybean systems.Farmers relate improvement of seed composition mainly with crop nutrition.Less than 13% of the participants are aware of their levels of protein and oil.Premium prices over seed quality can stimulate adoption of new technologies.A US$0.50 per bushel premium would justify on‐farm investments targeting quality.

Intensive study for more than 100 yr has resulted in a good understanding of corn's (Zea maysL.) growth and development. However, abnormal development of ears in corn was reported in several U.S. states, including Texas, Colorado, Kansas, Nebraska, Iowa, and Illinois, during 2016, stretching our understanding. A comprehensive review of the literature was conducted to identify abnormal ears’ symptoms, causes, and timing of development. This study aimed to (a) describe and summarize previously reported ear symptoms, (b) document recent widespread symptoms of major concern, and (c) describe our current understanding of the potential cause(s) and expected development timing for abnormal ears. In total, 10 previously reported symptoms of corn ears were found, including tassel, fasciated, arrested, pinched, blunt, silk‐balled, incomplete kernel set, banana‐shaped, zipper, and tipped‐back. Three additional recent widespread symptoms of major concern associated with significant yield reduction across a wide area in 2016 were described: multi‐ears, barbell‐ears, and short‐husk ears. The information available on several of the symptoms was limited, and the specific causes were unknown, highlighting the need for more research in this area. Despite this and based on existing knowledge, possible causal factors and postulated development timing (i.e., when the stress may have occurred) are presented for all symptoms. Abnormal ear development can be seen as the response to complex interactions among genetics, environment, and management practices. Ear abnormalities are detrimental to grain yield and quality, and their mitigation is imperative to efficient corn systems, crop resiliency, and sustainability. Abnormal ears are a likely response to G × E × M interactions.Ten previously reported abnormality symptoms are discussed.Three recent widespread symptoms of major concern associated with lower yields are included.Abnormal ears can reduce grain yield and quality.The understanding and mitigation of abnormal ears are imperative for sustainable agriculture.

Corn (Zea maysL.)–soybean [Glycine max(L.) Merr.] is among the most typical crop rotations in the U.S. Corn Belt, and N is the most limiting nutrient for both crops. This study aims to assess the effects of N management for corn on the following soybean crop. Two corn–soybean rotation N fertilizer rate studies—a long‐term study (1983–2020, Case Study I) and a two‐season study (2019–2020, Case Study II)—were conducted in Kansas (United States). Case Study I focused on soybean seed yield as the response variable, whereas Case Study II included a detailed seasonal characterization of soil N, symbiotic N fixation (SNF), and plant N uptake for soybean considering N fertilizer rates on the previous corn crop. Apparent N budgets from corn (N fertilizer minus grain N removal) ranged from approximately −100 to approximately +50 kg N ha−1, and soybean yields were slightly or not affected by corn N management. Case Study I showed that long‐term N budgets in corn crops did not affect the following soybean crop yields. In Case Study II, the previous corn N management produced negative or small N surplus that influenced neither soil residual N nor SNF, without compromising soybean productivity. Farmers applying close to economic optimum N rates on corn will likely not generate scenarios of N surplus to compromise SNF or soybean yields. Forthcoming research should further address how long‐term and large soil N mining or surplus in corn may enhance or inhibit N fixation for the next soybean crop. Soybean yields were slightly or not affected by N management of the previous corn crop.Soil N during soybean season was not affected by previous corn N management.Apparent N budgets from previous corn were mostly below zero or slightly positive.Small N surplus (<50 kg N ha−1) did not influence soybean yields and N fixation.Long‐term N budgets in corn crops did not affect next‐season soybean yields.

Microbes are decomposers of crop residues, and climatic factors and residue composition are known to influence microbial growth and community composition, which in turn regulate residue decomposition. However, the succession of the bacterial community during residue decomposition in Northeast China is not well understood. To clarify the property of bacterial community succession and the corresponding factors regulating this succession, bags containing maize residue were buried in soil in Northeast China in October, and then at different intervals over the next 2 years, samples were analyzed for residue mass and bacterial community composition. After residue burial in the soil, the cumulative residue mass loss rates were 18, 69, and 77% after 5, 12, and 24 months, respectively. The release of residue nitrogen, phosphorus, and carbon followed a similar pattern as mass loss, but 79% of residue potassium was released after only 1 month. The abundance, richness, and community diversity of bacteria in the residue increased rapidly and peaked after 9 or 20 months. Residue decomposition was mainly influenced by temperature and chemical composition in the early stage, and was influenced by chemical composition in the later stage. Phyla Actinobacteria, Bacteroidetes, and Firmicutes dominated the bacterial community composition in residue in the early stage, and the abundances of phyla Chloroflexi, Acidobacteria, and Saccharibacteria gradually increased in the later stage of decomposition. In conclusion, maize residue decomposition in soil was greatly influenced by temperature and residue composition in Northeast China, and the bacterial community shifted from dominance of copiotrophic populations in the early stage to an increase in oligotrophic populations in the later stage.

Common bean (Phaseolus vulgarisL.) has been grown under no‐till (NT), but nitrogen (N) management remains unclear when a previous cover crop is part of the farming system. In this study, we hypothesized that N applied to living cover crops, preceding the main grain crop of the rotation, can increase productivity and N‐use efficiency of the grain crop. Common bean was grown after palisade grass [Urochloa brizantha(Hochst. Ex A. Rich.) R.D. Webster] and ruzigrass [Urochloa ruziziensis(R. Germ. and C.M. Evrard) Crins] cover crops combined with N management [application to living cover crops 35 days before common bean seeding (DBS), 1 DBS, conventional, and control (zero‐N application)] in four experiment‐years. Dry matter (DM) and N content for palisade grass was consistently greater at termination relative to ruzigrass. Nitrogen application 35 DBS increased cover crop DM, N content, and the decomposition rate relative to the other N treatments. Greater common bean yield occurred following palisade grass than ruzigrass, and N application 35 DBS increased common bean yield compared to 1 DBS and conventional in one out of four experiment‐years. Only minor differences in the agronomic efficiency (AE) of applied N to cover crops were detected between 35 DBS and 1 DBS, and only for one experiment‐year, when palisade grass resulted in greater AE than ruzigrass. Applying N early to living crops such as palisade grass is an alternative N management strategy to the traditional approach for N fertilization in common bean. Productivity and N content for palisade grass was greater than ruzigrass at termination.Palisade grass cover crop improved common bean yields relative to ruzigrass.Nitrogen applied to living cover crops is an alternative N management approach of applying N only to common bean.

Balanced fertilization is one of the most critical management factors for attaining high yields. Assessing pod yield and plant nutrient demand can provide theoretical guidance for fertilization management of high yielding peanut (Arachis hypogaeaL.). Following this rationale, a large dataset was collected from field experiments (n = 313) across the main peanut planting regions of China for a period spanning from 2000 to 2019 to evaluate nutrient uptake behavior of peanut under current optimum fertilization management. Across the entire dataset, the results showed that average peanut pod yield and harvest index (HI) were 4.5 Mg ha−1and .54, respectively. The average peanut nitrogen (N), phosphorus (P), and potassium (K) requirements were of 48, 7.1, and 21 kg Mg−1pod yield, respectively. Across five ranges of pod yields (<3, 3–4, 4–5, 5–6, and >6 Mg ha−1), both nutrient uptake and HI increased with increasing pod yield. The N and K requirements (kg Mg−1) decreased with increasing pod yield, respectively, mainly due to reductions in pod N and K concentrations. The P requirement increased with yield, attributed to an increase in straw P concentration as P luxury uptake occurs under high pod yield. Increasing pod yields in peanut demands increasing nutrient uptake.Nitrogen and K requirements per Mg pod yield decreased with increasing yield in peanut.Phosphorus requirement per Mg pod yield increased with increasing yield in peanut.Nutrient harvest index explained the different nutrient requirements per Mg pod yield.Phosphorus luxury uptake should be considered in future peanut fertilization recommendations.

Long‐term selection in maize (Zea maysL.) favored single‐stalked phenotypes limiting vegetative growth. However, reduced plant densities create conducive environments to the expression of vegetative branches called tillers. Tiller expression has motivated discussions about its yield effect in variable environments, but tiller research is lacking for modern corn genotypes. The objectives of this study were to (a) quantify the relative importance of management, environment, and interactions on the yield effect of tiller expression for two modern genotypes; (b) understand effects of observed tiller density, plant density, and their interaction on yield; and (c) identify key environmental determinants of yield response to tiller density in modern genotypes. In 10 environmentally diverse site‐years across Kansas, tiller presence and removal were evaluated in two commercial corn hybrids (P0657AM and P0805AM) across three target plant density levels (25,000, 42,000, and 60,000 plants ha−1). Yields were increased or unaffected by greater plant densities and tiller presence within site‐years. Environments varied in yield responsiveness to tiller density, but fine‐tuning plant density was needed to maximize yields. Sites with yields most responsive to tiller density were characterized by good soil properties and photothermal quotient values (e.g., soils with high organic matter and climates with greater solar radiation and cooler temperatures). Favorable growing conditions can be exploited by plasticity traits such as tillering in unpredictable environments with annually variable optimum plant densities while limiting potential yield loss and producer risk due to disproportionate plant density. Tiller presence never reduced yield in evaluated environments and plant densities.In favorable scenarios, corn tillers have plant density compensation potential.Plant density adjustment was necessary to maximize yield for all environments.Corn tillers have plasticity potential to optimize defensive management strategies.

Nutrient balance is essential for attaining high yield and improving profits in agricultural farming systems, and crop nutrient uptake ratio and stoichiometry can indicate crop nutrient limitations in the field. We collected a large amount of field data to study the variations in yield, nutrient uptake and nutrient stoichiometry of peanut (Arachis hypogaeaL.) in Southeast China (SEC), North-central China (NCC), and Northeast China (NEC), during 1993 to 2018. Peanut pod yield gradually increased from 1993 to 2018, with average yields of 4 148, 5 138, and 4 635 kg ha−1in SEC, NCC, and NEC, respectively. The nitrogen (N) internal efficiency (NIE, yield to N uptake ratio) was similar among the three regions, but phosphorus (P) IE (PIE, yield to P uptake ratio) changed from low to high among regions: NCC

Information is limited on the effect of intensified management on winter wheat (Triticum aestivumL.) nutrient uptake dynamics. Our goal was to evaluate the effect of agronomic practices on wheat yield and uptake of N, P, K, and S by evaluating the (a) nutrient utilization and uptake at varying yield levels, (b) variation in nutrient concentration as function of biomass, and (c) plant nutritional status. The genotype ‘Everest’ was grown under standard (SM) and intensive (IM) management. Treatments (i.e., N, Cl, and S fertilizers, fungicide, plant density, and growth regulator) were individually added to the SM or removed from the IM controls. The IM control increased yield by as much as 0.9 Mg ha−1and uptake of N, K, and S by 37, 30, and 60%, respectively, relative to the SM control, with no changes in P uptake. Fungicide was the main treatment limiting yield and nutrient uptake, and its removal from the IM control reduced yield by 1 Mg ha−1and nutrient uptake in high disease‐pressure seasons. Across all treatments and nutrients, 20% of the uptake at maturity was accumulated by stem elongation, 50% at flag leaf, and 70% at heading. The IM control maintained higher shoot nutrient concentration relative to the SM control during the season, increasing nutrition indices for N and S, and indicating possible luxury uptake under IM. Intensification strategies may increase nutrient demand but it does not seem to affect the overall timing and rate of uptake during the season. Intensive management increased wheat yield and uptake of N, K, and S.Management practices had no effect on P uptake.Changes in N and S uptake due to management were more due to changes in nutrient concentration.Changes in P and K uptake due to management resulted more from changes in biomass.Intensification strategies did not affect timing and rate of nutrient uptake by wheat.

Soils and climate influence soil nutrient supply, crop nutrient demand, and potential yield. We collected soybean [Glycine max(L.) Merr.] data of seed yield and nutrient uptake from field students (n= 657) in Northeast China (2001–2017) to study spatial distribution of attainable yield, yield response to fertilizer, and crop nutrient requirements at regional scale. The study area was divided in four weather regions based on daily average temperature and cumulative precipitation during the soybean growth season. Average attainable yield was 3.2, 3.1, 2.9, and 2.6 Mg ha–1in warm‐wet, warm‐dry, cold‐wet, and cold‐dry regions, respectively. Soybean yield response to N, P, and K fertilizers and requirement of these nutrients presented the following order for each region from high to low: cold‐wet > warm‐dry > cold‐dry > warm‐wet regions, with exception for K requirement. In overall, spatial correlation in yield response to fertilizer and nutrient requirement ranked in the following order: P > K > N across all the climate regions. Based on the variation in attainable yield, balanced fertilization should be pursued to more sustainable higher soybean yields in the warm‐dry and cold‐wet regions with favorable weather conditions; while for the cold‐dry region, fertilization needs to address the topic of improving P use efficiency, as future research prioritizes from both environmental and productivity standpoints.

During a century of maize (Zea maysL.) breeding, yield genetic gain was largely determined by increased reproductive resilience under stress and establishment of sink size (number of grains per unit area). Considering grains as competing sinks for C and N assimilates, understanding changes in the C and N economy can provide insights to define selection criteria towards a sustained yield improvement. A cognitive framework to define such criteria may consist in connecting the water‐soluble carbohydrates (WSC) and N dynamics in stem and leaves with the reproductive sink strength during post‐flowering. The objectives of this study were to advance such framework by (a) quantifying grain N demand and remobilization capacity in two hybrids as affected by N availability, and (b) formalizing how the interplay between N and WSC remobilization influence grain growth. Single cross hybrids 3394 and P1197 (released in 1991 and 2014) were evaluated to represent keystone phases of germplasm development (conventional and molecular breeding eras). P1197 outyielded 3394 consistently under high N supply, and its better N utilization efficiency was reflected through a lower grain N concentration. Under high N, the ability to maintain a greater leaf area during late grain‐filling for P1197 resulted in a reduced leaf N remobilization. Although yield was not limited by C supply, 3394 exposed greater remobilization of WSC during late grain‐filling. This study contributes to advance the development of a relevant C to N framework to further analyze drivers of genetic yield gain and assist in selection strategies in maize.

Cowpea [Vigna unguiculata(L.) Walp.] cultivars with high yield potential and suitable plant architecture for mechanized harvesting have recently shown a growing interest to be cultivated as a second crop in fall–winter season of southeastern Brazil. The agronomic optimal plant density (AOPD) is one of the main management factors defining attainable yield. However, in the scientific literature for semiupright cowpea cultivars grown in fall–winter season in southeastern Brazil the AOPD is not yet clearly defined. A 3‐yr study was conducted to evaluate the effect of five plant densities (100,000–500,000 plants ha−1) on growth, seed yield and its components for two cowpea cultivars (BRS Guariba and BRS Novaera) in Botucatu, southeastern Brazil. The AOPD for cowpea increased as the attainable yield improved. The increase in plant density promoted increases in leaf area index (LAI) and the position (measured in height) of the first pod insertion for cowpea plants, but negatively affected per‐plant scale surviving rate, leaf area, aboveground dry matter (DM), and yield resulting from intraspecific competition. Maximum cowpea seed yields were achieved with AOPD ranging from 216,630 to 290,537 plants ha−1(8–24% greater than under the lowest plant density). On average, 259,000 plants ha−1was estimated for the AOPD to achieve maximum seed yield of semiupright cowpea grown as a second crop in fall–winter season in southeastern Brazil.

It is widely recognized that planting soybean [Glycine max(L.) Merr.] early is critical to maximizing yield, but the influence of changing management factors when soybean planting is delayed is not well understood. The objectives of this research were to (a) identify management decisions that increase seed yield in either early‐ or late‐planted soybean scenarios, and (b) estimate the maximum break‐even price of each management factor identified to influence soybean seed yield in early‐ or late‐planted soybean. Producer data on seed yield and management decisions were collected from 5682 fields planted with soybean during 2014−2016 and grouped into 10 technology extrapolation domains (TEDs) based on growing environment. A subsample of 1512 fields was classified into early‐ and late‐planted categories using terciles. Conditional inference trees were created for each TED to evaluate the effect of management decisions within the two planting date timeframes on seed yield. Management strategies that maximized yield and associated maximum break‐even prices varied across TEDs and planting date. For early‐planted fields, higher yields were associated with artificial drainage, insecticide seed treatment, and lower seeding rates. For late‐planted fields, herbicide application timing and tillage intensity were related to higher yields. There was no individual management decision that consistently increased seed yield across all TEDs.

Soybean [Glycine max(L.) Merr.] seeding rate research across North America is typically conducted in small geo‐political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment.

Yield estimations are of great interest to support interventions from governmental policies and to increase global food security. This study presents a novel model to perform in‐season corn yield predictions at the US‐county level, providing robust results under different weather and yield levels. The objectives of this study were to: (i) evaluate the performance of a random forest classification to identify corn fields using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and weather variables (temperature, precipitation, and vapor pressure deficit, VPD); (ii) evaluate the contribution of weather variables when forecasting corn yield via remote sensing data, and perform a sensitivity analysis to explore the model performance in different dates; and (iii) develop a model pipeline for performing in‐season corn yield predictions at county‐scale. Main outcomes from this study were: (i) high accuracy (87% on average) for corn field classification achieved in late August, (ii) corn yield forecasts with a mean absolute error (MAE) of 0.89 Mg ha−1, (iii) weather variables (VPD and temperature) highly influenced the model performance, and (iv) model performance decreased when predictions were performed early in the season (mid‐July), with MAE increasing from 0.87–1.36 Mg ha−1when forecast timing changed from day of year 232–192. This research portrays the benefits of integrating statistical techniques and remote sensing to field survey data in order to perform more reliable in‐season corn yield forecasts.

Plant breeders face the challenge of genotype × environment interaction (G × E) in comprehensively breeding for expanded geographic regions. An improved understanding of G × E sensitivity of traits and the environmental features that effectively discriminate among genotypes will enable more efficient breeding efforts. In this study of 31 maize (Zea maysL.) inbreds grown in 36 environments that are part of the Genomes to Fields Initiative, we measured 14 traits, including flowering date, height, and yield components (i.e., ear and kernel dimensions) to (i) identify traits that are the most sensitive indicators of G × E; (ii) determine how geographic location and weather factors influence environments’ discriminability of inbreds; and (iii) detect patterns of stability in better and worse discriminating environments. Genotype × environment interaction explained between 9.0–20.4% of the phenotypic variance with greater effects in the yield‐component traits. Discriminability of environments varied by trait. Midwest locations (where 26 of the 31 inbreds were developed) were among the most discriminating environments for more traits, while environments in the West and East tended to be less discriminating. Weather factors during silking were significantly different between the most and least discriminating environments more often than average weather across the season or during the period from planting to silking. Stability of genotypes varied by trait, and performance was usually not correlated with stability. The dissection of complex traits, such as yield into component traits, appears to be a useful approach to understand how environmental factors differentially affect phenotype.

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Soybean seed yield response to plant density is dependent on yield environment.Low yield environments required higher plant densities than high yield environments.Plant density mainly affected per‐plant seed number.No differences in plant survival were observed among yield environments. Inconsistent soybean [Glycine max(L.) Merr.] seed yield response to plant density has been previously reported. Moreover, recent economic and productive circumstances have caused interest in within‐field variation of the agronomic optimal plant density (AOPD) for soybean. Thus, the objectives of this study were to: (i) determine the AOPD by yield environments (YE) and (ii) study variations in yield components (seed number and weight) related to the changes in seed yield response to plant density for soybean in North America. During 2013 and 2014, a total of 78 yield‐to‐plant density responses were evaluated in different regions of the United States and Canada. A soybean database evaluating multiple seeding rates ranging from 170,000 to 670,000 seeds ha−1was collected, including final number of plants, seed yield, and its components (seed number and weight). The data was classified in YEs: low (LYE, <4 Mg ha−1), medium (MYE, 4–4.3 Mg ha−1), and high (HYE, >4.3 Mg ha−1). The main outcomes were: (i) AOPD increased by 24% from HYE to LYE, (ii) per‐plant yield increased due to a decrease in plant density: HYE > MYE > LYE, and (iii) per‐plant yield was mainly driven by seed number across plant densities within a YE, but both yield components influenced per‐plant yield across YEs. This study presents the first attempt to investigate the seed yield‐to‐plant density relationship via the understanding of plant establishment and yield components and by exploring the influence of weather variables defining soybean YEs.

Relative to full‐season soybeans, the double‐crop soybean yield gap is delayed and full‐season soybean yields improved.As wheat yield improved, double‐crop soybean has lower probability of presenting greater yields than wheat.Previous wheat yield, and soybean planting date and maturity groups influenced the attainable yield for double‐crop soybeans. Soybean (Glycine MaxL.) planted after wheat (Triticum aestivumL.) harvest in same season (double cropped [DC]) has the potential to increase productivity and sustainability. The objectives of this synthesis review were to (i) quantify attainable yield for DC soybean benchmarking against full‐season (FS) soybean; (ii) determine and build probabilistic response models on the effect of previous wheat productivity on DC soybean yields; and (iii) detect and rank factors influencing DC soybean yields via a decision inference tree analysis. A global database on DC soybean studies collected from 1976 to 2017 was divided into three data sets: (i) FS and DC soybean (n =141 data points); (ii) wheat and DC soybean (n =463); and (iii) production factors and DC soybean (n =547). Analysis showed that the yield gap between FS and DC soybeans increased from ‐31 to 1160 kg ha−1as FS yield improved from 1500 to 3000 kg ha−1. Even though the proportion of the variation accounted for wheat yields in the DC soybean/wheat yield ratio was low (R2= 0.15), the probability of soybean yield being equal to wheat yield was 0, 20, 30, and 55% for wheat yields of ≥6, ≥4 and <6, ≥2 and <4, and <2 Mg ha−1. Inference tree analysis indicated that the major factor impacting success of the DC system was wheat yield followed by soybean planting date and maturity group.

Critical S dilution curve was established for maize: S concentration = 2.13 × biomass−0.23.The S nutrition index successfully diagnosed S status at early crop stages (∼V6).Sulfur concentration and chlorophyll meter reading, both in the upper leaf, were good predictors of the S nutrition index. Sulfur (S) deficiency can severely limit maize (Zea maysL.) yield. This deficiency could be predicted by quantifying the S concentration (SC) in the shoot biomass (BM) and by calculating the S nutrition index (SNI = observed SC/critical SC). However, as shoot SCdetermination is laborious, alternative S diagnostic methods should be developed in maize. Thus, the objectives of our study were to: (i) determine and validate a critical S dilution curve, (ii) quantify the critical SNI, and (iii) explore the use of SCand chlorophyll meter reading (CMR), both determined in the uppermost developed leaf at vegetative growth stages, to predict SNI and for diagnosing S status in maize. Six field studies evaluating fertilizer S rates were executed, collecting maize shoot and uppermost developed leaf samples and determining Sc, CMR, yield, and SNI parameters. The main outcomes from our study were: (i) a maize critical S dilution curve was fitted (SC= 2.13BM−0.23) and validated with an independent dataset; (ii) a critical SNI threshold of 0.79 adequately diagnosed S status at ∼V6stage; (iii) at V6, SNI was linearly related to SC(R2= 0.65) and CMR (R2= 0.85). As a result, a threshold value of 2.0 g S kg−1for SC,47.5 units for CMR, and 0.94 units for relative CMR can be used to successfully diagnose S deficiencies at early vegetative (∼V6) maize stages. Future studies testing S diagnostic tools for maize should be performed in environments with severe S deficiency and nutrient co‐limitations.