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The United Nations declared 2023 as the International Year of Millets to highlight the value and importance of millets, which are cereals grown in arid and semi-arid regions with low inputs and high resilience to climate change. Millets contribute to the Sustainable Development Goals, particularly in terms of food security, healthy diets, economic growth, responsible consumption, and climate action. Pearl millet is one of the most widely grown millets and plays a crucial role in achieving self-sufficiency and food security in dryland areas. It is a climate-resilient crop that is gluten-free and rich in essential micronutrients. This special issue of Crop Science covers various aspects of millet research, including crop improvement, phenotyping, crop physiology, management, nutritional quality, and grain composition. The goal is to promote global awareness of millets and their potential for diversifying and intensifying agricultural systems. Future research should focus on integrating disciplines, improving pre-breeding and phenotyping efforts, and expanding the use of millets for different purposes. Funding for this special issue was provided by the Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification at Kansas State University. [Extracted from the article]

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]

Many crop species, particularly those of tropical origin, are chilling sensitive, so improved chilling tolerance can enhance production of these crops in temperate regions. For the cereal crop sorghum (Sorghum bicolor L.), early planting and chilling tolerance have been investigated for >50 years, but the potential value or tradeoffs of this genotype × management change have not been formally evaluated with modeling. To assess the potential of early planted chilling‐tolerant grain sorghum in the central US sorghum belt, we conducted CERES‐Sorghum simulations and characterized scenarios under which this change would be expected to enhance (or diminish) drought escape, water capture, and yield. We conducted crop growth modeling for full‐ and short‐season hybrids under rainfed systems that were simulated to be planted in very early (April), early (May 15), and normal (June 15) planting dates over 1986–2015 in four locations in Kansas representative of the central US sorghum belt. Simulations indicated that very early planting will generally lead to lower initial soil moisture, longer growing periods, and higher evapotranspiration. Very early planting is expected to extend the growing period by 20% for short‐ or full‐season hybrids, reduce evaporation during fallow periods, and increase plant transpiration in the two‐thirds of years with the highest precipitation (mean > 428 mm), leading to 11% and 7% increase grain yield for short‐ and full‐season hybrids, respectively. Thus, in this major sorghum growing region, very early and early planting could reduce risks of terminal droughts, extend seasons, and increase rotation options, suggesting that further development of chilling‐tolerant hybrids is warranted. [ABSTRACT FROM AUTHOR]

Soil compaction is one of the most relevant crop yield‐limiting factors in no‐till (NT) farming systems in southern Brazil. This study aimed to identify strategies to mitigate soil compaction and increase crop productivity at low, medium and high yield environments within a field. Treatments evaluated were: control (without intervention), PG (phosphogypsum), MC (mechanical chiselling), PCC (polyculture of cover crops), MC + PG, PCC + PG, MC + PCC and MC + PCC + PG. Soil physical and chemical attributes (soil macroporosity, bulk density, penetration resistance) and soybean yield (Glycine max L.) and black oat (Avena strigosa Schreb) biomass production were evaluated. In the low and medium yield environments, MC + PCC + PG promoted the highest soybean yields: 5,455 kg ha−1 and 5,534 kg ha−1, respectively. In the high yield environment, PCC + PG promoted the highest soybean yield (5,579 kg ha−1), whereas MC decreased yields relative to the control. Black oat biomass production responded to the treatments similarly to soybean yields. Overall, integrating two or three decompaction strategies improved soil physical attributes in a greater proportion relative to single strategies and to the control, enhancing both soybean and black oat performances. Selection of the right decompaction strategies for each yield environment might help increase productivity under NT and optimize the use of time, labour, fuel and other resources. Highlights: Soil compaction limits crop yields under no‐tillage farming in southern Brazil.Low, medium and high yield environments were delineated with precision agriculture tools.Single or integrated mechanical, chemical and biological soil decompaction strategies were tested.Soil decompaction strategies to increase soybean yields are specific to yield environments. [ABSTRACT FROM AUTHOR]

BACKGROUND: Site‐specific weed management (SSWM) demands higher resolution data for mapping weeds in fields, but the success of this tool relies on the efficiency of optical sensors to discriminate weeds relative to other targets (soils and residues) before cash crop establishment. The objectives of this study were to (i) evaluate the accuracy of spectral bands to differentiate weeds (target) and other non‐targets, (ii) access vegetation indices (VIs) to assist in the discrimination process, and (iii) evaluate the accuracy of the thresholds to distinguish weeds relative to non‐targets for each VI using training and validation data sets. RESULTS: The main outcomes of this study for effectively distinguishing weeds from other non‐targets are (i) training and validation data exhibited similar spectral curves, (ii) red and near‐infrared spectral bands presented greater accuracy relative to the other bands, and (iii) the tested VIs increased the discrimination accuracy related to single bands, with an overall accuracy above 95% and a kappa above 0.93. CONCLUSION: This study provided a novel approach to distinguish weeds from other non‐targets utilizing a ground‐level sensor before cash crop planting based on field spectral data. However, the limitations of this study are related to the spatial resolution to distinguish weeds that might be closer to the one this study presented, and also related to the soil and crop residues conditions at the time of collecting the readings. Overall the results presented contribute to an improved understanding of spectral signatures from different targets (weeds, soils, and residues) before planting time supporting SSWM. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Early fertilizer nitrogen (N) application on cover crops or their residues during the off-season is a practice adopted in Brazil subtropical conditions under no-tillage corn (Zea mays L.) systems. However, the effect of early N application on yield, plant N content, and N recovery efficiency (NRE) for corn is not yet well documented. Five fertilizer N timings in an oat-corn system were evaluated in two studies utilizing an isotopic-labeled N determination, 15N isotope. The N fertilization timings were: (i) oat tillering, (ii) 15 days before corn planting time, over the oat residues, (iii) at corn planting time, (iv) in-season at the three-leaf growth stage (V3), and (v) in-season split application at V3 and six-leaf (V6) growth stages. Based on the statistical analysis, the N fertilization timings were separated into three groups: 1) N-OATS, designated to N applied at oat; 2) N-PLANT, referred to pre-plant and planting N applications; and 3) N-CORN, designated to in-season corn N applications. Corn yield was not affected by the N fertilization timing. However, the N-CORN N fertilization timings enhanced NRE by 17% and 35% and final N recovery system (plant plus soil) by 16% and 24% all relative to N-OATS and N-PLANT groups, respectively. Overall, N-OATS resulted in the largest N derived from fertilizer (NDFF) amount in the deeper soil layer, in overall a delta of 10 kg N ha-1 relative to the rest of the groups. Notwithstanding corn yield was not affected, early N fertilization under subtropical conditions is not a viable option since NRE was diminished and the non-recovery N increased relative to the in-season N applications. [ABSTRACT FROM AUTHOR]

Improved plant N utilization and partitioning is critical for future improvements in maize (Zea mays L.) grain yield (GY). The overall research objective was to gain understanding of the physiological mechanisms underpinning biomass (BM), N uptake partitioning, and GY processes during the reproductive period for two maize hybrids grown at varying plant density (PD) (low is 54,000 plant ha-1, medium is 79,000 plants ha-1, and high is 104,000 plants ha-1) and N inputs (low is 0 kg N ha-1, medium is 112 kg N ha-1, and high is 224 kg N ha-1) over four site- years. At the community level, maize GY was maximized in both genotypes at the medium PD and highest N rate. At maturity, grain harvest index improved as the whole-plant N uptake increased following a linear-plateau model and, for N allocation, both grain and shoot N concentrations increased similarly as BM increased. Around flowering (±15 d), dry mass and N partitioning rates were unmodified by treatment factors. Treatment factors only marginally influenced potential kernel number near flowering. Allometric analyses confirmed a lack of treatment impact on whole-plant N uptake and N remobilization coefficients. Greater reproductive- stage N uptake was associated with superior ear strength (kernel number and weight) and late shoot N remobilization, but GY was also positively related to vegetative-stage N uptake. Future research should identify genotypic variation for overcoming the documented N uptake trade-off mechanisms (vegetative-stage N uptake vs. shoot N remobilization) as related to the maize GY improvement process. [ABSTRACT FROM AUTHOR]

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]

For farmers, policymakers, and government agencies, it is critical to accurately define agricultural crop phenology and its spatial-temporal variability. At the moment, two approaches are utilized to report crop phenology. On one hand, land surface phenology provides information about the overall trend, whereas weekly reports from USDA-NASS provide information about the development of particular crops at the regional level. High-cadence earth observations might help to improve the accuracy of these estimations and bring more precise crop phenology classifications closer to what farmers demand. The second component of the proposed solution requires the use of robust classifiers (e.g., random forest, RF) capable of successfully managing large data sets. To evaluate this solution, this study compared the output of a RF classifier model using weather, two different satellite sources (Planet Fusion; PF and Sentinel-2; S-2), and ground truth data to improve maize (Zea mays L.) crop phenology classification using two regions of Kansas (Southwest and Central) as a testbed during the 2017 growing season. Our findings suggests that high temporal resolution (PF) data can significantly improve crop classification metrics (f1-score = 0.94) relative to S-2 (f1-score = 0.86). Additionally, a decline in the f1-score between 0.74 and 0.60 was obtained when we assessed the ability of S-2 to extend the temporal forecast for crop phenology. This research highlights the critical nature of very high temporal resolution (daily) earth observation data for crop monitoring and decision making in agriculture. [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]

The utilization of crop nitrogen (N) status as an in-season diagnosis tool for predicting N needs to maximize grain yield (GY) is a well-established concept in agronomy. However, a cross-species comparison including the characterization of yield components, grain number (GN) and grain weight (GW), to understand the physiological basis behind the GY-crop N status relationship is still missing. The main goal of this study was to perform a cross-species comparison for maize (Zea mays L.), rice (Oryza sativa L.), and wheat (Triticum aestivum L.) of the relationship between crop N status around anthesis as a GY and GY components prediction diagnosis method. A systematic literature search was carried out for these major field crops, the final dataset (comprising 629 observations) consisted of 15 publications including information on i) shoot biomass and plant N concentration or N nutrition index (NNI) values at anthesis, ii) GY, and iii) GN and/or GW. An analysis was conducted to assess the sensitivity (slope of the linear models) of GY, GN, and GW to changes in crop NNI status at anthesis. Notably, the crop N status at anthesis demonstrated a strong relationship between both GY (R2 between 0.66 and 0.93) and GN (R2 between 0.58 and 0.94) across all crops, with a slightly weaker relationship with GW (R2 between 0.30 and 0.83). Considerable uncertainty was observed on the GY and GN sensitivity (S) to N deficiency across all crops. Maize showed the greatest sensitivity of GY to NNI (S = 964 g m−2), with lowest sensitivity for wheat crop (S = 496 g m−2). Regarding GN, rice showed the greatest sensitivity to NNI (S = 23859 GN m−2), whereas maize was less sensitive (S = 2673 GN m−2). While maize exhibited a positive association between NNI and GW (although with considerable uncertainty), this relationship was less evident for rice and wheat crops. Our findings demonstrated that crop N status at anthesis is a better predictor of GY and GN than GW in maize, rice, and wheat. Maize showed the greatest range in observed values for relative GW relative to NNI, highlighting the impact of crop N status on GW determination. These findings contribute to improving the understanding of the importance of achieving adequate crop N status at anthesis as key aspect for yield formation, with implications for both breeding programs and the optimization of on-farm crop N management. • Crop N status at anthesis showed a strong relationship with grain number. • Maize showed the greatest sensitivity of grain yield to NNI at anthesis. • Rice showed the greatest sensitivity of grain number to NNI at anthesis. • Maize showed the greatest responses of grain weight to crop N status at anthesis. [ABSTRACT FROM AUTHOR]

In the temperate-humid region of Argentina, nitrogen (N) fertilisers in maize (Zea mays L.) are mainly applied around sowing, whereas N-splitting is rare and occurs during early vegetative stages. Splitting and late N fertilisation, even up to silking (R 1), effects on yield have been recently studied. However, to the extent of our knowledge, these studies have mainly focused on the effect of these strategies on maize yield, but less on the crop N economy. The aims were to study the mechanisms explaining the effect of i) splitting N and ii) additional N application at R 1 on yield, post-flowering N uptake, kernel weight, N uptake efficiency, and the relationship of these variables with the N nutrition index (NNI). Seven experiments were carried out throughout the humid temperate region of Argentina, evaluating seven treatments: a control without N fertilisation, N rate entirely applied at sowing or split between sowing, eight leaf (V 8), and R 1 , an additional N rate at R 1 , and an N sufficiency treatment. The scenario with split N application had no adverse effect on either yield or N uptake at maturity (P > 0.05), with less pre-flowering N uptake compensated with more post-flowering N uptake in late N applications. In turn, increases in both yield and kernel weight were related to increases in post-flowering N uptake. Positive yield responses to N-splitting (plateauing at 450 kg ha-1) were obtained with yield responses to N greater than 3107 kg ha-1. Likewise, yield responses to additional N at R 1 (plateauing at 1107 kg ha-1) were evident with yield responses greater than 2943 kg ha-1. The NNI at R 1 accounted for variations in post-flowering N uptake and yield, as well as yield responses to additional N at R 1 and their impact on kernel weight. Results show that N-splitting and late N fertilisation are promising strategies that, combined with crop N status monitoring, could lead to improvements in both maize yield and N economy. • Split and late N fertilisations were evaluated for maize in the humid-temperate region of Argentina. • Grain yield (GY) responses to N-splitting and to additional N at R 1 were recorded. • GY responses to N-splitting and to additional N at R 1 were higher for the most responsive sites. • N-splitting did not reduce GY and increased N uptake efficiency. • GY response to additional N at R 1 was negatively associated with crop N status at R 1. [ABSTRACT FROM AUTHOR]

Crop nitrogen (N) fertilization diagnoses via the N nutrition index (NNI)-yield relationship have been tested for several crop species, but a cross-species comparison of that relationship has not been performed yet. This study aimed to perform a cross-species comparison of the relationship between NNI and yield with emphasis on the yield sensitivity to N deficiency, slope of the models. Additionally, we conducted an evaluation to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops. Based on a recently published global dataset to parametrize critical dilution curves , we calculated integrated NNI, instantaneous NNI, relative yield, and relative shoot biomass for annual ryegrass, tall fescue, maize, potato, rice, and wheat. We obtained 238 observations to fit integrated NNI-relative yield linear mixed-effects models and 1606 observations to fit instantaneous NNI-relative yield models. Subsequently, we performed a sensitivity analysis to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops (maize, rice, and wheat). Our results show that there was low inter-species variation of sensitivity to N deficiency, i.e., the slope of the relationship between relative yield and integrated NNI. For grain crops, instantaneous NNI around anthesis demonstrated a better predictive capability for relative yield, outperforming other vegetative stages. This finding contributed to improving the understanding of the association between relative yield and NNI with implications for breeding programs, nutrient management practices, and crop modelling. Most importantly, this study is a contribution to improving the N nutrition diagnosis for several crop species, by using an integral, comparative approach. • The relationship between relative yield and integrated NNI was similar across species. • The slope of relative yield-integrated NNI shown limited variation across species. • The relative yield-integrated NNI slopes of maize and potato were the highest. • For grain crops, NNI around anthesis was the strongest predictor of relative yield. [ABSTRACT FROM AUTHOR]

[Display omitted] • PLSR models demonstrated a high predictive accuracy to estimate stem WSC. • Errors of prediction were associated with the number of samples of the calibration. • Spectral regions between 1100 and 1480 nm were critical for WSC determination. • NIRS spectroscopy is a reliable technique for WSC determination. Near infrared spectroscopy (NIRS) was evaluated as a rapid and non-destructive method for determining the concentration of water-soluble carbohydrates (WSC) in stem fractions for winter canola (Brassica napus L.), maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) crops. For each crop at different growth stages, stem WSC concentration was determined using NIRS, and benchmarked against the anthrone reagent method, chemical lab analysis. Partial least squares regression was implemented to associate the WSC predicted via NIRS relative to those obtained by laboratory analysis. Spectral regions between 1100 and 1480 nm were critical for WSC determination. The predictive models resulted in coefficient of determinations of 0.93, 0.94, and 0.95, and a Root Mean Square Error of prediction of 10, 20, and 17 for winter-canola, maize and sorghum crops, respectively. The NIRS spectroscopy is a reliable method for WSC determination in stem tissues on these major field crops. [ABSTRACT FROM AUTHOR]

Canola (Brassica napus L.) is a major oilseed crop for edible and industrial uses. Climate change, characterized by extreme heat and drought conditions, can affect canola seed yield and quality (protein and oil), restricting grower capacity to keep pace with growing demand. However, estimating the influence of future climate on such complex traits is challenging due to biophysical interactions and crop adaptations to abiotic stresses. Current scientific literature reports contrasting impacts on the effect of heat and drought for canola productivity, mainly linked to changes in magnitude and duration of the stress. To better understand how drought and heat affect canola seed yield and quality (mainly protein and oil), a meta-analysis was executed to compile a total of 39 papers (1794 observations). The aims of this research were to i) quantify the impact of different timing and duration of heat and drought stresses on canola seed yield, oil yield, and seed quality (oil and protein concentration), and ii) study the effect of short-term stresses on seed quality, yield and its components (seed number and seed weight). This review study highlighted the magnitude and direction of overall and different stress impacts on canola; and some of the main limitations in the current scientific literature. Briefly, the main outcomes of this review highlighted that i) early heat stress(before end of flowering) had the largest impact on canola seed yield, as a short stress, ii) drought caused greatest yield impact when imposed for a long duration during flowering or a shorter duration during pod setting, and iii) short stresses had large impacts on seed oil concentration mainly during pod setting. More studies with standard stress levels and detailed information of abiotic stresses on reproductive growth stages are needed. This meta-analysis provides critical information for crop management, guiding development of cultivars linked to tolerance to abiotic stresses for breeding programs, and the future direction of canola adoption and production. • Contrasting heat and drought impacts on canola/rapeseed were reported in the literature. • This meta-analysis dissected abiotic stress timing and duration. • Highest negative impacts on yield were observed with stresses imposed before the end of flowering. • Seed oil and protein concentration were affected following abiotic short stresses during pod setting. [ABSTRACT FROM AUTHOR]

Crop growth models can be useful tools to evaluate potential scenarios, yet the limited field data is an obstacle for providing significant insights at the local level. This certainly applies to several regions in Africa, where crop models built regional scenarios based on low resolution field data, leading to unsupported agricultural interventions on smallholders' systems. This review provides a synthesis analysis of crop modeling efforts in Africa using Agricultural Production Systems Simulator (APSIM) studies as a case-study. This research aims to highlight the value of standardized protocols to collect, store and deploy field data, and highlights the critical issue of limited data accessibility of published manuscripts and unavailability of a data sharing platform.Investment in local-level data collection and data sharing platforms is critical to guarantee the advancement of science and to provide reliable assessments to address complex challenges of food, nutrition, and climate security in Africa. [Display omitted] • Limited field data represents a challenge for crop models to provide useful insights. • This study showcases the need for investments on open data sources. • A systematic review was executed to collect crop modeling studies within Africa. • There is a need of standardization for field data collection and its accessibility. • Investment in local-level data is critical to provide reliable food assessments. [ABSTRACT FROM AUTHOR]

The determination of critical nitrogen (N) dilution curves (CNDCs) has been the subject of intensive study for the last decades due to its relevance to diagnosing crop N status. However, to date, minimum steps, data requirements and robust science-based guidelines to estimate CNDCs based on experimental data have not been formalized in the literature to ensure the reliability of an established nutrient dilution curve. In this study, we conducted a systematic review of the literature on CNDCs, described the main characteristics of the datasets used to establish CNDCs in the past, and finally identified a set of criteria specifying the minimum characteristics that a dataset must satisfy to be used to establish an accurate CNDC. Published CNDC studies showed large heterogeneity in the number of experiments (from 1 to 35), fertilizer N rates (from 2 to 7), and sampling times (from 2 to 16) used to fit the CNDCs. Given that, we quantified the sensitivity of the CNDC parameters to five dataset characteristics (number of experiments, N rates, and sampling times, attainment of W max plateau, and precision of the studies) using a Bayesian statistical model on a case-study estimation for maize. We found that the number of experiments is the main factor affecting the uncertainty of the fitted CNDC. Bootstrapping analyses showed that accurate CNDCs can be fitted with experiments having at least three N rates. Increasing the number of sampling times was more effective than using more than three N rates for reducing the uncertainty in the estimation of the parameters for the CNDCs. We also showed that more reliable CNDCs can be obtained when weighting each data by its precision. We concluded our analysis with formal recommendations of the minimum steps and data requirements to fit CNDC. The criteria established here should serve as a guide for the establishment of reliable dilution curves for N and other nutrients in the agricultural and biological sciences. • Number of experiments has strong impact on the uncertainty of the critical N dilution curve (CNDC). • Accurate CNDCs can be fit with experiments having at least three N rates. • More sampling times reduce more uncertainty than more N rates. • Young-stage biomass samplings are critical for the estimation of key CNDC parameters. • Within-study variances are useful to weigh studies by their precisions during the fitting process. [ABSTRACT FROM AUTHOR]

Nitrogen (N) metabolism is a major research target for increasing productivity in crop plants. In maize (Zea mays L.), yield gain over the last few decades has been associated with increased N absorption and utilization efficiency (i.e. grain biomass per unit of N absorbed). However, a dynamical framework is still needed to unravel the role of internal processes such as uptake, allocation, and translocation of N in these adaptations. This study aimed to 1) characterize how genetic enhancement in N efficiency conceals changes in allocation and translocation of N, and 2) quantify internal fluxes behind grain N sources in two historical genotypes under high and low N supply. The genotypes 3394 and P1197, landmark hybrids representing key eras of genetic improvement (1990s and 2010s), were grown under high and low N supply in a two-year field study. Using stable isotope 15N labelling, post-silking nitrogen fluxes were modeled through Bayesian estimation by considering the external N (exogenous-N) and the pre-existing N (endogenous-N) supply across plant organs. Regardless of N availability, P1197 exhibited greater exogenous-N accumulated in leaves and cob-husks. This response was translated to a larger amount of N mobilized to grains (as endogenous-N) during grain-filling in this genotype. Furthermore, the enhanced N supply to leaves in P1197 was associated with increased post-silking carbon accumulation. The overall findings suggest that increased N utilization efficiency over time in maize genotypes was associated with an increased allocation of N to leaves and subsequent translocation to the grains. • Bayesian modelling and 15N analysis were combined to estimate N allocation in maize. • Modern maize plants evidenced an improved post-silking N partitioning to leaves. • Greater N allocation to leaves resulted in an increased post-silking carbon fixation. • Internal translocation to the grains was improved due to larger supply from leaves. [ABSTRACT FROM AUTHOR]