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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. Core Ideas: 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. [ABSTRACT FROM AUTHOR]

Corn (Zea mays L.)–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. Core Ideas: 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. [ABSTRACT FROM AUTHOR]

Monitoring the availability of phosphorus (P) in soil under continuous cropping facilitates finding deficiency in crops and contributes to improving crop growth and nutrient management models. Soil P availability for crops is usually estimated by soil test P (STP), such as Bray‐1. This is widely used in the Americas. The relationship between the decrease of STP Bray‐1 and cumulative removal of P was evaluated in non‐P‐fertilized areas in long‐term studies. This removal was the sum of annual P removal over the study period as P exported in grains/crop outside the soil. The objectives were to: (a) quantify changes in STP as a function of cumulative P removal, (b) assess the relationship between relative decrease rate of STP and soil variables as well as annual removal of P by crops, and (c) develop a model to predict decrease of STP Bray‐1. Exponential decay functions were used to describe annual cumulative removal of P and STP from soil over time for 12 long‐term studies where no addition of P fertilizer was carried out. Changes in the relative rate of decrease of STP, relative to the initial STP Bray‐1 value at the onset of the experiment, were predicted by the ratio of soil organic matter to clay and silt and the average annual P removal by exponential decay (R2adj = 0.64; RMSE = 3.2 mg kg−1). We propose this predictive model as suitable to provide estimates of the relative decrease rate of STP by Bray‐1 and thereby improve management of P for optimizing crop yield. Highlights: STP Bray‐1 decrease and cumulative P removal were related by exponential decay functions.Relative decrease rate of STP Bray‐1 was related to SOM/(clay+silt) ratio and annual P removal.A predictive model of the relative decrease rate of STP Bray‐1 was fitted and validate.Our model is a useful tool to help predict soil P availability and nutrient management. [ABSTRACT FROM AUTHOR]

Windbreaks were originally promoted across the U.S. Great Plains to reduce wind erosion. A review paper published nearly 30 years ago showed yield increases for a variety of crops associated with windbreaks. However, with the widespread use of no-till cropping systems and advanced crop genetics, the question is "Do windbreaks still provide a yield benefit?" This study compared data from protected and unprotected fields over multiple years across Kansas and Nebraska looking at relative soybean (Glycine max L.) and winter wheat (Triticum aestivum L.) yield differences. Farmer's pre-existing georeferenced data, generated by automated combine yield monitors, were analyzed with ArcGIS 10.3.1 to visualize windbreak interaction with crop yield. Statistics were conducted to determine if the yield in protected areas of the field was significantly different from the yield in unprotected areas. Also, yield loss was estimated from the windbreak footprint to assess if yield increases were enough to compensate for the area taken out of crop production. Results showed: soybeans (57 crop/years) presented the most positive response to windbreak effect with significant yield increases 46% of the time, with a 16% (283 kg ha−1) average yield increase. Wheat (44 crop/years) yield increases were significant 30% of the time, with a 10% (319 kg ha−1) average yield increase. Narrow windbreaks (1–2 tree rows, average width of 13 m) and those on the north edge of fields resulted in yield increases that compensated for the footprint of the windbreak more often (71%) than wider windbreaks on the south edges of fields (38%). [ABSTRACT FROM AUTHOR]

Objectives: The main purpose of this publication is to help users (students, researchers, farmers, advisors, etc.) of weather data with agronomic purposes (e.g. crop yield forecast) to retrieve and process gridded weather data from different Application Programming Interfaces (API client) sources using R software. Data description: This publication consists of a code-tutorial developed in R that is part of the data-curation process from numerous research projects carried out by the Ciampitti's Lab, Department of Agronomy, Kansas State University. We make use of three weather databases for which specific libraries were developed in R language: (i) DAYMET (Thornton et al. in https://daymet.ornl.gov/, 2019; https://github.com/bluegreen-labs/daymetr), (ii) NASA-POWER (Sparks in J Open Source Softw 3:1035, 2018; https://github.com/ropensci/nasapower), and (iii) Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (Funk et al. in Sci Data 2:150066, 2015; https://github.com/ropensci/chirps). The databases offer different weather variables, and vary in terms of spatio-temporal coverage and resolution. The tutorial shows and explain how to retrieve weather data from multiple locations at once using latitude and longitude coordinates. Additionally, it offers the possibility to create relevant variables and summaries that are of agronomic interest such as Shannon Diversity Index (SDI) of precipitation, abundant and well distributed rainfall (AWDR), growing degree days (GDD), crop heat units (CHU), extreme precipitation (EPE) and temperature events (ETE), reference evapotranspiration (ET0), among others. [ABSTRACT FROM AUTHOR]

Camelina (Camelina sativa L. Crantz) is an alternative oilseed crop with potential for fallow replacement in dryland cereal-based crop production systems in the semiarid Great Plains. Th e interaction between genotype and environment was investigated on camelina seed yield, oil content, and fatty acid composition across two locations in the U.S. Great Plains. Treatments were three spring camelina genotypes (cultivars Blaine Creek, Pronghorn, and Shoshone), three growing seasons (2013, 2014, and 2015) and two locations (at Hays, KS, and Moccasin, MT). Results showed camelina grown at Hays yielded 54% less than that at Moccasin. Blaine Creek yielded 17 and 42% more than Pronghorn and Shoshone at Hays but yields were not different among genotypes at Moccasin. Oil content ranged from 262 g kg-1 at Hays to 359 g kg-1 at Moccasin. The proportion of polyunsaturated fatty acids (PUFAs) ranged from 51% at Hays to 55% at Moccasin, whereas monounsaturated fatty acid (MUFA) and saturated fatty acid (SFA) contents were greater at Hays. The linolenic acid content ranged from 26% when Pronghorn was planted at Hays to 35% when planted at Moccasin. In general, the variations in seed yield and fatty acid profile corresponded well with growing season precipitation and temperatures at each environment. [ABSTRACT FROM AUTHOR]

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions. [ABSTRACT FROM AUTHOR]

Identifying an optimal plant density is a critical management decision for corn (Zea mays L.) production. The main objectives of this study were to: (i) investigate the grain yield responses to plant density (yield-density relationship), (ii) identify best fitted yield-density response curves, and (iii) explore genotype (G) x environment (E) interaction effect on yield-density response models. Analysis was conducted on meta-data (124,374 observations) gathered from 22 US states and 2 Canadian provinces, diverse sites (E), for years from 2000-2014 on multiple hybrids (G). Yield data were further grouped into four yield environments (low [LY], <7 Mg ha-1; medium [MY], 7-10 Mg ha-1; high [HY], 10-13 Mg ha-1; and very high [VHY], >13 Mg ha-1 yielding groups). Primary outcomes from this analysis were: (1) strong G x E interaction; (2) a quadratic model best fitted yield-density relationship; (3) four contrasting yield-density responses identified as dominant in each yield productivity environment, i.e., a declining, a constant, an increasing, and ever-increasing type; (4) the yield productivity environment varied for the different corn comparative relative maturity (CRM) groups, i.e., the LY environment for long-maturing hybrids matched with a MY or HY environment for short maturing hybrids; and (5) maximum yielding plant density (MYPD) was lower but maximum yield was greater for long- versus short-maturing hybrids. In summary, optimal plant density should be decided based on detailed G x E analysis of production conditions that include factors such as CRM, yield productivity environment (weather-soil x management practices), and site information. [ABSTRACT FROM AUTHOR]

Unraveling the complexity underpinning nitrogen (N) use efficiency (NUE) can be physiologically approached via examining grain N sources and N internal efficiency (NIE) (yield to plant N content ratio). The main objective of this original research paper is to document and understand sorghum NUE and physiological mechanisms related to grain N dynamics. The study of different grain N sources, herein defined as the reproductive- stage shoot N remobilization (Remobilized N), reproductive-stage whole-plant N content (Reproductive N), and vegetative-stage whole-plant N content (Vegetative N), was pursued with the goal of synthesizing scientific literature for sorghum [Sorghum bicolor (L.) Moench] crop. A detailed literature review was performed and summarized on sorghum NUE (13 studies; >250 means) with three Eras, defined by the year of the study, named as Old Era (1965-1980); Transient Era (1981-2000); and New Era (2001-2014). The most remarkable outcomes from this synthesis were: (1) overall historical (1965-2014) cumulative yield gain was >0.5 Mg ha-1 (yields >7 Mg ha-1); (2) NIE did not change across the same time period; (3) grain N concentration (grain %N) accounted for a large proportion (63%) of the variation in NIE; (4) NIE increased as grain %N diminished, regardless of the Eras; (5) Remobilized N was strongly (>R² 0.6) and positively associated with Vegetative N, presenting a unique slope across Eras; and (6) a trade-off was documented for the Remobilized N and Reproductive N (with large variation,

Sorghum [Sorghum bicolor (L.) Moench] yield formation is severely affected by high temperature stress during reproductive stages. This study pursues to (i) identify the growth stage(s) most sensitive to high temperature stress during reproductive development, (ii) determine threshold temperature and duration of high temperature stress that decreases floret fertility and individual grain weight, and (iii) quantify impact of high daytime temperature during floret development, flowering and grain filling on reproductive traits and grain yield under field conditions. Periods between 10 and 5 d before anthesis; and between 5 d before- and 5 d after-anthesis were most sensitive to high temperatures causing maximum decreases in floret fertility. Mean daily temperatures >25°C quadratically decreased floret fertility (reaching 0% at 37°C) when imposed at the start of panicle emergence. Temperatures ranging from 25 to 37°C quadratically decreased individual grain weight when imposed at the start of grain filling. Both floret fertility and individual grain weights decreased quadratically with increasing duration (0-35 d or 49 d during floret development or grain filling stage, respectively) of high temperature stress. In field conditions, imposition of temperature stress (using heat tents) during floret development or grain filling stage also decreased floret fertility, individual grain weight, and grain weight per panicle. [ABSTRACT FROM AUTHOR]

Global food security must address the dual challenges of closing yield gaps (i.e., actual vs. potential yield) while improving environmental sustainability. Nutrient balance is essential for achieving global food security. Historical (in distinct "Eras" from late 1800s to 2012) and geographical (in United States vs. remainder of World) changes in maize (Zea mays L.) grain yields and plant nutrient content (N, P, and K) were characterized from studies (>150) with known plant densities. At the community scale, greater yield to nutrient content ratios (physiological efficiency, PE) were documented for United States vs. World. The U.S. historical trend displayed increasing gains for community-scale yield and nutrient uptake, except for a recent decline attributed to weather. At the individual-plant scale, geographic PE differences over time were primarily explained by changes in yield, and secondarily by nutrient content changes. Despite wide variation, high-yield maize in both geographies was associated with balanced N/P (5:1) and N/K (1:1) ratios. More scope exists for maize nutrient PE gains in developing regions. Achieving balanced nutrition in optimally integrated soil-crop management cropping systems will facilitate simultaneous realization of high-yield and bio-fortification goals in maize improvement efforts. [ABSTRACT FROM AUTHOR]

In maize (Zea mays L.), low N use efficiency (NUE) is observed when a high proportion of the fertilizer N is not recovered by the crop. Combining different management practices can improve maize NUE. The objectives were to assess the impacts of integrated N management practices on grain yield (GY), plant N uptake (PNU), and NUE (and its components, N recovery efficiency [NRE] and N internal efficiency [NIE]) from three maize site-years (Indiana). The factors evaluated were: (i) N rate (0, 90, and 180 kg N ha-1), (ii) nitrapyrin (presence or absence), and (iii) N timing (at planting or V6 stage). Grain yields for the field research study ranged from 6.3 to 12.1 Mg ha-1. The main results indicated: (i) PNU increased with N rates and with delayed N timing, (ii) the use of nitrapyrin increased NUE (~17%), primarily via improving NRE (~25%), and (iii) NUE increased by delaying the timing of N application. In addition to the field experiments, a meta-analysis studying the effect of nitrapyrin on maize GY, PNU, and NUE was performed. Highlights from the meta-analysis were: (i) positive impacts of nitrapyrin application on both GY and PNU, but (ii) those impacts were neither reflected in gains for NUE nor for NRE and NIE. The positive impacts of nitrapyrin on NUE (and components) found in the field research study were not consistent with the outcomes documented in the meta-analysis. This emphasizes that the potential maize NUE benefits of nitrapyrin addition to spring N applications are not always realized. [ABSTRACT FROM AUTHOR]

Abstract: Over the past 3 decades, the study of various mechanisms involved in maize grain yield (GY) formation and its relationship with nitrogen (N) uptake dynamics has been increasingly acknowledged in the scientific literature. However, few studies have combined investigations of GY response to N fertilizer with detailed physiologically based analyses of plant N dynamics such as N uptake quantities, timing, and (or) partitioning – and the complex interactions of those with specific genotypes (G), management practices (M), and (or) production environments (E). Limited reporting of both N and yield dynamics at plant-component, individual-plant, and community levels has contributed to a considerable knowledge gap as to whether the physiological mechanisms that govern maize plant N dynamics and their relationship with GY formation have changed with time. We therefore undertook a comprehensive review to discern trends in physiological aspects of maize response to changing plant densities and fertilizer N rates (M components) under the umbrella of evolving G×E interactions. We reviewed 100 published and unpublished papers based on field experiments which consistently reported total plant N uptake at maturity and maize GY (frequently among other physiological variables). Our analyses were limited nearly exclusively to experiments involving hybrid (as distinct from inbred) response to M input levels where plant density data was available. Dissection of the complex interactions among years, plant densities and N rates began with division of treatment mean data (close to ∼3000 individual points) into two time periods defined by year(s) of the original research: (i) studies from 1940 to 1990 – “Old Era” and, (ii) studies from 1991 to 2011 – “New Era”. For the Old Era, maize GY averaged 7.2Mgha−1 at a mean plant density of 5.6plm−2 with a total plant N uptake of 152kgNha−1, a grain harvest index (HI) of 48% and N harvest index (NHI) of 63%. For the New Era, maize GY averaged 9.0Mgha−1 at a mean plant density of 7.1plm−2, total plant N uptake of 170kgNha−1, a grain HI of 50% and a NHI of 64%. The most striking findings in terms of overall GY and plant N uptake were: (1) on a per-unit-area basis, both potential GY and NIE (GY/N uptake) increased from Old to New Era at comparable N uptake levels, and (2) on a per-plant basis, total plant N uptake at maturity had not changed between Eras despite increased plant density in the New Era genotypes. Other important findings in terms of plant growth and component partitioning responses to N were (i) a consistently strong dependency between dry matter and N allocation to the ear organ in both Eras; (ii) higher total plant biomass (BM) accumulation and N uptake, on an absolute basis, during the post-silking period with New Era genotypes accompanied by relatively smaller changes in HI and NHI; (iii) a strong correlation between plant N uptake at silking time and per-plant GY and its components in both Eras; (iv) New Era (56.0kg GYgrainkg−1 N) was primarily associated with reduced grain %N, and to a minor degree with NHI gains; and (v) New Era genotypes showed higher tolerance to N deficiency stress (higher GY when no N fertilizer was applied), and larger GY response per unit of N applied, relative to Old Era hybrids. This improved understanding of the physiological factors underlying progress in maize yield response to N over time, within the context of changing G×E×M factors, serves to help guide maize programs focused on achieving further improvements in N use efficiency. [Copyright &y& Elsevier]

Abstract: Nitrogen (N) use efficiency (NUE), defined as grain produced per unit of fertilizer N applied, is difficult to predict for specific maize (Zea mays L.) genotypes and environments because of possible significant interactions between different management practices (e.g., plant density and N fertilization rate or timing). The main research objective of this study was to utilize a quantitative framework to better understand the physiological mechanisms that govern N dynamics in maize plants at varying plant densities and N rates. Paired near-isogenic hybrids [i.e., with/without transgenic corn rootworm (Diabrotica sp.) resistance] were grown at two locations to investigate the individual and interacting effects of plant density (low—54,000; medium—79,000; and high—104,000plha−1) and sidedress N fertilization rate (low—0; medium—165; and high—330kgNha−1) on maize NUE and associated physiological responses. Total aboveground biomass (per unit area basis) was fractionated and both dry matter and N uptake were measured at four developmental stages (V14, R1, R3 and R6). Both plant density and N rate affected growth parameters and grain yield in this study, but hybrid effects were negligible. As expected, total aboveground biomass and N content were highly correlated at the V14 stage. However, biomass gain was not the only factor driving vegetative N uptake, for although N-fertilized maize exhibited higher shoot N concentrations than N-unfertilized maize, the former and latter had similar total aboveground biomass at V14. At the R1 stage, both plant density and N rate strongly impacted the ratio of total aboveground N content to green leaf area index (LAI), with the ratio declining with increases in plant density and decreases in N rate. Higher plant densities substantially increased pre-silking N uptake, but had relatively minor impact on post-silking N uptake for hybrids at both locations. Treatment differences for grain yield were more strongly associated with differences in R6 total biomass than in harvest index (HI) (for which values never exceeded 0.54). Total aboveground biomass accumulated between R1 and R6 rose with increasing plant density and N rate, a phenomenon that was positively associated with greater crop growth rate (CGR) and nitrogen uptake rate (NUR) during the critical period bracketing silking. Average NUE was similar at both locations. Higher plant densities increased NUE for both medium and high N rates, but only when plant density positively influenced both the N recovery efficiency (NRE) and N internal efficiency (NIE) of maize plants. Thus plant density-driven increases in N uptake by shoot and/or ear components were not enough, by themselves, to increase NUE. [Copyright &y& Elsevier]

Equidistant plant arrangements have shown positive impacts, in the United States over the last century, on soybean (Glycine max L.) yield and seed quality by reducing intraspecific plant competition and enhancing early canopy cover. This study aims to (i) assess the effects of equidistant versus non‐equidistant plant arrangements on soybean yield and seed quality across different regions in the United States; (ii) explore the effect of modified soybean plant canopy resulting from different plant arrangements on yield and seed quality. In 2021 and 2022, 13 trials were conducted in Kansas, Mississippi, and South Dakota. The treatments consisted of three equidistant plant arrangements of 13 cm × 13 cm, 15 cm × 15 cm, and 20 cm × 20 cm (620,000, 430,000, 242,363 seeds ha−1, respectively), and a non‐equidistant with 38 cm of row space (321,236 seeds ha−1). Soybean yield and yield components were collected, and canopy coverage (%) was monitored throughout the growing season. Overall, there is no evidence of any advantage of equidistant over non‐equidistant arrangements in soybeans. Yield differences related to spatial arrangement were observed in only three trials: the non‐equidistant increased yield compared to the 20 cm × 20 cm equidistant arrangement (lowest density) in two trials, while the 13 × 13 and 15 × 15 patterns (highest density) showed higher yield compared to the 20 cm × 20 cm arrangement in one trial. Furthermore, seed quality remained constant across spatial arrangements in the 13 trials. In contrast to prior research, our study found no correlation between canopy coverage development and yield improvement. The tested equidistant arrangement while promising did not provide substantial evidence of yield improvement relative to the non‐equidistant. [ABSTRACT FROM AUTHOR]

• We studied how nitrogen internal efficiency (NIE) is regulated by prolificacy in maize crops. • Crops expressed high prolificacy values at low density with high N supply. • NIE variations for prolific plants/crops were mainly determined by nitrogen harvest index. • Higher yields, total N uptakes and NIEs were recorded for prolific plants. • Prolificacy stabilized crops yields and NIE in response to environmental improvements. In maize (Zea mays L.) crop, "defensive practices" such as low plant densities and limited nitrogen (N) fertilization have begun to be progressively adopted in limited-production regions of the world. At low plant density, the expression of prolificacy (i.e., more than one fertile ear per plant) can stabilize maize production. Considering N as the main limiting resource, yield can be defined as a function of i) total N uptake and the efficiency to produce yield from total N uptake, i.e., N internal efficiency (NIE) or ii) total N uptake, N harvest index (NHI) and grain N concentration. The objective of this work was to describe changes in NIE and their related components regulated by prolificacy at both canopy- and plant- scales. Field studies were carried out in Buenos Aires, Argentina, during 2015–2016 and 2016–2017 seasons. Treatments were combinations of two N supplies (N-: 0 and N+: 200 kg N ha−1), two plant densities (4 and 8 plants m−2) and five maize hybrids released during the last four decades. High N supply positively impacted yield per unit area (ca. 45 %) by increasing kernel number (ca. 33 %), kernel weight (ca. 18 %) and harvest index (ca. 12 %). At low plant density, high N supply expressed the highest prolificacy (mean 1.65 ears plant−1). The older hybrids displayed the highest prolificacy (mean 1.30 ears plant−1) and the lowest yield per unit area (mean 792 g m−2), but showed the greatest grain N concentration (mean 15.1 g kg−1). However, recent hybrids resulted in medium prolificacy (mean 1.11 ears plant−1) with the highest yield per unit area (mean 910 g m−2), but with the lowest grain N concentration (mean 13.6 g kg−1). The different pattern of grain N concentration among hybrids coupled with similar NHI (mean 0.80) led to the lowest NIE of the most prolific hybrids (mean 55 g yield g N−1) relative to the less prolific ones (mean 60 g yield g N−1). Variations in NIE among genotypes and plant densities were negatively influenced by grain N concentration under N- and under N+ only when prolificacy = 1. Under N+ with prolificacy > 1, NIE variations were positively determined by NHI. For all genotypes, higher yield per plant (mean 30 %) and total N uptake (mean 25 %) were recorded for the prolific plants, reflected in the greater absolute NIE (mean 5 %). For the oldest hybrid differences between prolific and non-prolific plants in NIE (mean 46.8 vs 41.8 g yield g N-1) were greater than those recorded for the other genotypes due to the higher impact of prolificacy on yield per plant (mean 51 %) than on total N uptake (mean 26 %). Over time, improvements in NIE primarily occurred from greater yield of the apical ear. Overall, the expression of prolificacy appears to be an adequate strategy to stabilize maize production in response to improvements of environmental conditions, specially addressed by low plant density and high N supply. [ABSTRACT FROM AUTHOR]

Due to the asymptotic nature of the crop yield response curve to fertilizer N supply, the nitrogen use efficiency (NUE, yield per unit of fertilizer applied) of crops declines as the crop N nutrition becomes less limiting. Therefore, it is difficult to directly compare the NUE of crops according to genotype-by-environment-by-management interactions in the absence of any indication of crop N status. The determination of the nitrogen nutrition index (NNI) allows the estimation of crop N status independently of the N fertilizer application rate. Moreover, the theory of N dilution in crops indicates clearly that crop N uptake is coregulated by (i) soil N availability and (ii) plant growth rate capacity. Thus, according to genotype-by-environment-by-management interactions leading to variation in potential plant growth capacity, N demand for a given soil N supply condition would be different; consequently, the NUE of the crop would be dissimilar. We demonstrate that NUE depends on the crop potential growth rate and N status defined by the crop NNI. Thus, providing proper context to NUE changes needs to be achieved by considering comparisons with similar crop mass and NNI to avoid any misinterpretation. The latter needs to be considered not only when analyzing genotype-by-environment-by-management interactions for NUE but for other resource use efficiency inputs such as water use efficiency (colimitation N–water) under field conditions. [ABSTRACT FROM AUTHOR]

Legume cover crops are often considered a valuable alternative for supplying nitrogen (N) to cropping systems. However, certain pathways of N released from their residues to successor crops remain unclear. Aiming to assess N contribution and residual N from hairy vetch (Vicia villosa Roth.) cover crop in succeeding crops, we examined i) soil N availability and mineralizable-N, ii) crop yields, and iii) plant N sourcing within the soybean [ Glycine max (L.) Merrill]-wheat (Triticum aestivum L.) cropping sequence in the southeastern Argentine Pampas. Three field experiments were conducted over three seasons: E1 (2018/19), E2 (2019/20), and E3 (2020/2021) in the southeast of Buenos Aires province. The crop sequence studied was hairy vetch/soybean-wheat. Factors investigated were hairy vetch inclusion and bare fallow, and wheat N fertilizer (150 and 0 kg N ha−1). Hairy vetch aboveground dry matter and its N concentration were measured at hairy vetch termination and six times thereafter (residues) during the succeeding soybean, using the litter bag method. Soybean biomass, N concentration, and the relative abundance of ureides (RAU) as an indicator of N fraction derived from biological nitrogen fixation (BNF), were examined. Soil NO 3 --N and anaerobically incubated N (Nan) were sampled during the soybean season and at wheat sowing. At physiological maturity, soybean and wheat grain yields, along with grain N concentrations, were determined. Hairy vetch N released during soybean season ranged from 79 to 175 kg N ha−1. Soil NO 3 --N content at soybean sowing increased 5.5 kg ha−1 per Mg of hairy vetch dry matter, while Nan increased 3.6 mg kg−1 per Mg of hairy vetch dry matter. Soybean seed yields, seed N content, and N uptake showed no significant change with increased hairy vetch dry matter. Likewise, RAU at R5 decreased from 2.4 to 5.5 units (%) per Mg of hairy vetch dry matter. Concerning wheat, hairy vetch produced a slight increase in soil residual N at sowing. Only in one experiment (E1), hairy vetch dry matter led to improvements in wheat grain yield and N content. Our study outlined new insights into hairy vetch N contribution, primarily linked to increased soil N availability in the immediate soybean, the relatively higher soybean N uptake from soil compared to BNF, and the dilution of soil N contribution to wheat as second crop in the sequence. These findings support the recommendation of using hairy vetch as a predecessor for an immediate cereal or non-N fixing crops. • Nitrogen (N) dynamics were studied in a hairy vetch – soybean – wheat sequence. • Hairy vetch increased soil nitrate-N and mineralizable-N for the following soybean. • Increased hairy vetch N residues decreased soybean N fixation at the seed filling stage. • Hairy vetch neither affected subsequent soybean yields nor its total N uptake. • Hairy vetch marginally increased soil nitrate-N but did not affect mineralizable-N for wheat. [ABSTRACT FROM AUTHOR]

A comparative analysis of grain yield performance of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) under different environments constitutes a way to identify restrictions in yield formation between both crop species. However, most of the previous studies on these field crops have been carried out in Mediterranean environments. This study aims to compare the grain yield performance of wheat and barley across a wide range of temperate environmental conditions. Wheat and barley yield databases from the national network of cultivar evaluation of Argentina were assembled. The database included 191 wheat cultivars (with early-, mid- and late-flowering length cycle), 140 barley cultivars, and 44 combinations of sites and years of the Argentine wheat and barley belt region. Average heading date variation among environments and crops was no more than 10 days. Crop grain yield comparisons were carried out using a regression analysis, benchmarking within each site-year the grain yield of each cultivar against the average grain yield of all crop cultivars (i.e., environmental index). There were no differences in grain yield between wheat and barley for all data, but the behavior between crop species changed with the length of the wheat cycle (i.e., early-, mid-, or late-flowering cultivars). Barley portrayed a greater grain yield than early-flowering wheat under low-yielding environments (< 6060 kg ha−1), but this advantage vanished as the yield environment improved. Mid-flowering wheat showed a similar grain yield to barley across temperate environments. Lastly, late-flowering wheat outperformed barley mainly in high-yielding environments (> 6138 kg ha−1). Grain number m−2 was the main numerical component that explained variability in grain yield. The relative contribution of the yield numerical components differed between species, having barley lower grain number m−2 but a greater grain weight than wheat. The comparison of the grain yield performance between species represents a strategy to adjust the rotation system, being a critical factor in considering the variability in the crop growth cycle. • Yield performance offers valuable insights to select the best crop option into the right environment. • Wheat and barley have similar grain yield performance when they have similar cycle duration. • Barley outyielded early-flowering wheat (shorter season) in low-yielding environments (< 6 Mg ha−1). • Late-flowering wheat (longer season) outyielded barley in high-yielding environments (> 6 Mg ha−1). • Yield variations were governed by changes in grain number per unit area for both crop species. [ABSTRACT FROM AUTHOR]

Highlights • We developed an approach to evaluate influence of management on yield using both producer and experimental data. • We assessed influence of row spacing on US soybean yield. • Experimental data showed a consistent yield advantage of narrow versus wide row spacing. • However, data from producer fields indicate no yield difference between narrow and wide rows. • This complementary approach can help evaluate yield increase derived from a management practice. Abstract Narrowing row width in soybean fields leads to earlier canopy closure, which may increase capture of incoming solar radiation during critical crop stages for yield determination. Theoretically, this should enhance seed yield. However, in prior studies, the impact of narrowing row spacing on soybean yield has been inconsistent. To explore on a broader scale the potential factors underlying this inconsistency, we evaluated the yield difference between narrow (NR; ≈38 cm) and wide (WR; ≈76 cm) row spacing using two sources of yield and management information: (i) data collected from 4879 soybean production fields via a multi-year, multi-state survey of soybean producers in the North Central US region; and (ii) data extracted from 129 site-year experiments that quantified NR-WR yield difference. The producer fields were allocated to their respective climate-soil domains to enable analysis of the NR-WR yield difference within each domain. The experimental trial data originated from three US geographic regions: south, central, and north. Key crop developmental stages in each trial were estimated using a soybean crop simulation model to discern if changes in crop phenology or any weather variable occurring before versus after a specific crop stage modulated the magnitude of the NR-WR yield difference. Analysis of experimental trial data indicated that, while NR yields were overall higher than WR yields, the NR-WR yield difference varied by region: 540 (south), 104 (central), and 240 kg ha−1 (north); the respective NR yields were greater than WR yields in 92%, 68%, and 84% of the cases. In the north and south regions, the NR-WR yield difference increased when the crop cycle length decreased as a consequence of later sowing date, earlier cultivar maturity group, and/or higher temperature. The relatively smaller (and occasionally negative) NR-WR yield difference detected in the central region was likely the result of environmental conditions that favored canopy closure irrespective of row spacing. In contrast to the analysis of the experimental database, no consistent NR-WR yield differences were detected in the producer field database. We hypothesize that the apparent absence of a significant NR-WR effect in the producer dataset is likely associated with the background management used with narrow spacing, together with yield losses due to wheel damage and greater disease pressure. This complementary approach using both producer and experimental data can help evaluate if practices documented in experimental trials to enhance yield realize equivalent yield increases in producer fields and, if not, explore underlying causes for the discrepancy. [ABSTRACT FROM AUTHOR]

Field trials are commonly used to estimate the effects of different factors on crop yields. In the present study, we followed an alternative approach to identify factors that explain field-to-field yield variation, which consisted of farmer survey data, a spatial framework, and multiple statistical procedures. This approach was used to identify management factors with strongest association with on-farm soybean yield variation in the US North Central (NC) region. Field survey data, including yield and management information, were collected over two crop growing seasons (2014 and 2015) from rainfed and irrigated soybean fields (total of 3568 field-year observations). Fields were grouped into technology extrapolation domains (TEDs) that accounted for soil and climate variation and 9 TEDs were selected based on the number of fields needed to detect yield differences due to management as determined using power analysis. Average yield ranged from 2.5 to 5 Mg ha −1 across TEDs, with field yield distributions in half of the domains having a distributional peak that was close to maximum yields. Conditional inference trees analysis was chosen among 26 statistical procedures as the approach that best combines ability to detect and rank factors (and their interactions) with greatest influence on on-farm yield and relatively easy interpretation of results. Survey data from ca. 150 fields in each of the nine TEDs allowed us to identify key management factors influencing yields for an agricultural area that includes ca. 7 million ha sown with soybean. In five of the nine TEDs, highest yields were observed in early-sown fields. Other factors explaining on-farm yield variation were maturity group, and in-season foliar fungicide and/or insecticide application, but, in some cases, their influence on yield depended upon sowing date and water regime. While the approach proposed here cannot establish cause-effect relationships conclusively, it can certainly provide a focus to replicated field experiments in relation to which management factors to investigate. We believe that future agronomic studies based on farmer survey data can greatly benefit from ex-ante identification of most important TEDs (relative to crop area and production) as well as determination of minimum number of farmer survey data that needs to be collected from each of them based on expected yield differences and variability. The approach is generic enough to be applied in other crop producing regions as long as farmer data and associated climate and soil databases are available. [ABSTRACT FROM AUTHOR]

Crop intensification is a key aspect for achieving global food security and exploring options for optimizing land use and environmental resources. Following this rationale, a maize (Zea mays L.) – soybean (Glycine max L.) double cropping was tested to intensify current agricultural systems in the Southern region of the United States (US). The aims of this study were to i) assess the feasibility of cultivating maize-soybean double crop in the Southern US, ii) explore the best scenario of maize-soybean crops integrating field data with APSIM crop growth model via calculation of the land equivalent ratio (LER), and iii) evaluate the risk of crop failure for each crop combination. For this case study, one year field research and in-silico experiments using 30-years of weather data were analyzed to evaluate the different lengths of growth cycle for both crops for yield, LER, and percentage of success. The feasibility of the maize-soybean sequence was determined by increasing yield gain for the soybean crop while maintaining similar yield for maize crop. Simulated LER values ranged from 1.1 to 1.3 with the maize hybrid 110 comparative relative maturity followed by a soybean maturity group 3.0 as the best combination for maximum LER. Lastly, long-term simulations confirmed the low risk associated with maize-soybean double crop sequences across sowing dates. Expanding the implementation of this farming system could produce additional benefits not only on productivity and land use efficiency but in overall economic return for farmers in the Southern US region. [Display omitted] • Maize-soybean double cropping is a feasible production system in the Southern US. • Maximum land equivalent ratio was achieved with long-season maize with intermediate to long soybean maturity group. • Maize yield contribution to LER is a key factor for the overall maize-soybean system. • Soybean risk of not reaching maturity across sowing dates and maturities was low (<25%). [ABSTRACT FROM AUTHOR]

• Soybean yield at municipality-level was forecasted using satellite and weather data. • LSTM neural networks outperformed conventional machine learning algorithms in soybean yield prediction. • The model accuracy decreased as we anticipated earlier dates of the predictions. • Soybean yield can be forecasted with MAE of 0.42 Mg ha−1 ~70 days before harvesting. Soybean yield predictions in Brazil are of great interest for market behavior, to drive governmental policies and to increase global food security. In Brazil soybean yield data generally demand various revisions through the following months after harvest suggesting that there is space for improving the accuracy and the time of yield predictions. This study presents a novel model to perform in-season ("near real-time") soybean yield forecasts in southern Brazil using Long-Short Term Memory (LSTM), Neural Networks, satellite imagery and weather data. The objectives of this study were to: (i) compare the performance of three different algorithms (multivariate OLS linear regression, random forest and LSTM neural networks) for forecasting soybean yield using NDVI, EVI, land surface temperature and precipitation as independent variables, and (ii) evaluate how early (during the soybean growing season) this method is able to forecast yield with reasonable accuracy. Satellite and weather data were masked using a non-crop-specific layer with field boundaries obtained from the Rural Environment Registry that is mandatory for all farmers in Brazil. Main outcomes from this study were: (i) soybean yield forecasts at municipality-scale with a mean absolute error (MAE) of 0.24 Mg ha−1 at DOY 64 (march 5) (ii) a superior performance of the LSTM neural networks relative to the other algorithms for all the forecast dates except DOY 16 where multivariate OLS linear regression provided the best performance, and (iii) model performance (e.g., MAE) for yield forecast decreased when predictions were performed earlier in the season, with MAE increasing from 0.24 Mg ha−1 to 0.42 Mg ha−1 (last values from OLS regression) when forecast timing changed from DOY 64 (March 5) to DOY 16 (January 6). This research portrays the benefits of integrating statistical techniques, remote sensing, weather to field survey data in order to perform more reliable in-season soybean yield forecasts. [ABSTRACT FROM AUTHOR]