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Shallow groundwater has a great influence on crop growth. However, few studies have investigated the impact of different groundwater depth on crop growth. This paper mainly discusses the impact of the groundwater depth on summer maize growth and the groundwater contributions to summer maize evapotranspiration and yield via the AquaCrop crop growth model. Numerical simulation results showed that summer maize growth and soil water in the Shijin irrigation district were highly related to the groundwater depth. With the depletion of the groundwater, the crop coefficient, crop actual transpiration, crop yield and crop water-use efficiency tended to increase and then decrease. Considering the groundwater contribution, maize yield and water-use efficiency, the suitable groundwater table depth for summer maize growth in the Shijin irrigation district was 1.5 m, and the groundwater contribution was 58.8 % in this case. Thus, discussing the impact of groundwater on crop growth is highly pertinent, as it is important for arranging irrigation schedules. [ABSTRACT FROM AUTHOR]

This paper presents estimates of the effects of changing climate on crop yields for grain corn and soybeans in Ontario, Canada, for 1959–2013. We were able to use a database that is more comprehensive with respect to explanatory variables than some previous efforts had available. Our model includes climate variables, prices, land quality, groundwater level, CO2 concentration, and a time trend. Our results indicate that trends in temperature and precipitation during our study period have not yet resulted in appreciable threats to crop yields in the region. [ABSTRACT FROM AUTHOR]

The application of drip irrigation has been paid more and more attention, but there was lack of systematic comparative analysis between drip irrigation and conventional border irrigation method for maize, currently. A 7-year field study from 2015 to 2021 evaluated the effects of drip irrigation (DI, 540 mm) or conventional border irrigation method (BI, 720 mm) on maize growth, water use efficiency (WUE) as well as profitability. The results showed the plant height, leaf area index, yield, WUE and economic benefit of maize with DI had significantly higher than BI. The dry matter translocation, the dry matter transfer efficiency and contribution of dry matter translocation to grain with DI showed significant increase of 27.44%, 13.97% and 7.85% compared to BI, respectively. In comparison to conventional border irrigation, the yield of drip irrigation increased by 14.39%, as well as WUE and irrigation water use efficiency (IWUE) increased by 53.77% and 57.89%. The net return and economic benefit of drip irrigation was 1998.87 and 756.58 USD$ hm−1 higher than that of BI. Drip irrigation increased net return and benefit/cost ratio by 60.90% and 22.88% compared with BI. These results demonstrate that the drip irrigation can effectively improve the growth, yield, WUE and economic benefit of maize in northwest China. Therefore, drip irrigation can be used for maize cultivation to increase crop yield and WUE in northwest China, which has cut down on irrigation water about 180 mm. [ABSTRACT FROM AUTHOR]

Accurate estimation of crop yield predictions is of great importance for food security under the impact of climate change. We propose a data-driven crop model that combines the knowledge advantage of process-based modeling and the computational advantage of data-driven modeling. The proposed model tracks the daily biomass accumulation process during the maize growing season and uses daily produced biomass to estimate the final grain yield. Computational studies using crop yield, field location, genotype and corresponding environmental data were conducted in the US Corn Belt region from 1981 to 2020. The results suggest that the proposed model can achieve an accurate prediction performance with a 7.16% relative root-mean-square-error of average yield in 2020 and provide scientifically explainable results. The model also demonstrates its ability to detect and separate interactions between genotypic parameters and environmental variables. Additionally, this study demonstrates the potential value of the proposed model in helping farmers achieve higher yields by optimizing seed selection. A data-driven and process-based model incorporates machine learning to predict maize yield from available historical data over both temporal and spatial dimensions using explainable parameters without the need for experimental calibration. [ABSTRACT FROM AUTHOR]

The impact of extreme heat on crop yields is an increasingly pressing issue given anthropogenic climate warming. However, some of the physical mechanisms involved in these impacts remain unclear, impeding adaptation-relevant insight and reliable projections of future climate impacts on crops. Here, using a multiple regression model based on observational data, we show that while extreme dry heat steeply reduced U.S. corn and soy yields, humid heat extremes had insignificant impacts and even boosted yields in some areas, despite having comparably high dry-bulb temperatures as their dry heat counterparts. This result suggests that conflating dry and humid heat extremes may lead to underestimated crop yield sensitivities to extreme dry heat. Rainfall tends to precede humid but not dry heat extremes, suggesting that multivariate weather sequences play a role in these crop responses. Our results provide evidence that extreme heat in recent years primarily affected yields by inducing moisture stress, and that the conflation of humid and dry heat extremes may lead to inaccuracy in projecting crop yield responses to warming and changing humidity. [ABSTRACT FROM AUTHOR]

A letter is presented which discusses the progress made in updating knowledge on the current diversity of maize landraces and their implications in relation to maize production and use in Mexico. It enumerates the federal agencies that provide the funding. It cites the lack of commercial production of transgenic maize in Mexico. The issue of whether the introduction of transgenic maize will offer solutions to current problems for Mexican agriculture.

Field experiments were conducted to evaluate eight different integrated crop management (ICM) modules for 5 years in a maize-wheat rotation (MWR); wherein, ICM1&2-ˈbusiness-as-usualˈ (conventional flatbed maize and wheat, ICM3&4-conventional raised bed (CTRB) maize and wheat without residues, ICM5&6-conservation agriculture (CA)-based zero-till (ZT) flatbed maize and wheat with the residues, and ICM7&8- CA-based ZT raised bed maize and wheat with the residues. Results indicated that the ICM7&8 produced significantly (p < 0.05) the highest maize grain yield (5 years av.) which was 7.8–21.3% greater than the ICM1-6. However, across years, the ICM5-8 gave a statistically similar wheat grain yield and was 8.4–11.5% greater than the ICM1-4. Similarly, the CA-based residue retained ICM5-8 modules had given 9.5–14.3% (5 years av.) greater system yields in terms of maize grain equivalents (MGEY) over the residue removed CT-based ICM1&4. System water productivity (SWP) was the highest with ICM5-8, being 10.3–17.8% higher than the ICM1-4. Nevertheless, the highest water use (TWU) was recorded in the CT flatbed (ICM1&2), ~ 7% more than the raised bed and ZT planted crops with or without the residues (ICM4-8). Furthermore, the ICM1-4 had produced 9.54% greater variable production costs compared to the ICM5-8, whereas, the ICM5-8 gave 24.3–27.4% additional returns than the ICM1-4. Also, different ICM modules caused significant (p < 0.05) impacts on the soil properties, such as organic carbon (SOC), microbial biomass carbon (SMBC), dehydrogenase (SDH), alkaline phosphatase (SAP), and urease (URE) activities. In 0.0–0.15 m soil profile, residue retained CA-based (ICM5-8) modules registered a 7.1–14.3% greater SOC and 10.2–17.3% SMBC than the ICM1-4. The sustainable yield index (SYI) of MWR was 13.4–18.6% greater under the ICM7&8 compared to the ICM1-4. Hence, this study concludes that the adoption of the CA-based residue retained ICMs in the MWR could sustain the crop yields, enhance farm profits, save water and improve soil properties of the north-western plans of India. [ABSTRACT FROM AUTHOR]

Farmers focus on reducing the cost of production and aim to increase profit. The objective of this study was to quantify the reduction of pesticides applied to soybean (Glycine max (L.) Merrill) and maize (Zea mays L.) crops in several stages of the production cycle using a site-specific spraying application based on real-time sensors in the Brazilian Cerrado region. The sprayers were equipped with a precision spraying control system based on a real-time sensor. The spraying operations were performed not only for herbicide, but also for fungicide and insecticides applications. The maps recorded the percentage of the spray boom when the application was turned on (on/off spray system) with nozzle-to-nozzle control. The precision spraying system based on real-time sensors reduced the volume of pesticides (including herbicides, insecticides, and fungicides) applied to soybean and maize crops. There was a more significant reduction in the volume of pesticides applied post-emergence of the crops in the initial stages of soybean and maize when the crops had less leaf area or less foliage coverage between the rows. The cost reduction achieved using this technology was 2.3 times lower than the cost associated with pesticide application over the entire area using a conventional sprayer. Under the experimental conditions, there were no differences in the average crop yield, compared to the historical productivity of soybean and maize crops by applying this technology because the recommended doses were not affected and the site of application was limited to points where the presence of plants was present was detected. [ABSTRACT FROM AUTHOR]

Drought stress is one of the major abiotic stresses that limit growth, development and yield of maize crops. To better understand the responses of maize inbred lines with different levels of drought resistance and the molecular mechanism of exogenous glycine betaine (GB) in alleviating drought stress, the responses of two maize inbred lines to drought stress and to the stress-mitigating effects of exogenous GB were investigated. Seedling morphology, physiological and biochemical indexes, root cell morphology and root transcriptome expression profiles were compared between a drought-tolerant inbred line Chang 7-2 and drought-sensitive inbred line TS141. Plants of both lines were subjected to treatments of drought stress alone and drought stress with application of exogenous GB. The results showed that with the increase of drought treatment time, the growth and development of TS141 were inhibited, while those of Chang 7-2 were not significantly different from those of the control (no drought stress and GB). Compared with the corresponding data of the drought-stress group, every index measured from the two inbred lines indicated mitigating effects from exogenous GB, and TS141 produced stronger mitigating responses due to the GB. Transcriptome analysis showed that 562 differentially expressed genes (DEGs) were up-regulated and 824 DEGs were down-regulated in both inbred lines under drought stress. Due to the exogenous GB, 1061 DEGs were up-regulated and 424 DEGs were down-regulated in both lines. In addition, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify 10 DEGs screened from the different treatments. These results showed that the expression of 9 DEGs were basically consistent with their respective transcriptome expression profiles. The results of this study provide models of potential mechanisms of drought tolerance in maize as well as potential mechanisms of how exogenous GB may regulate drought tolerance. [ABSTRACT FROM AUTHOR]

The fifth assessment report (AR5) predicted that land temperatures would rise faster over Africa than other global averages while changes in rainfall are uncertain across Sub-Saharan Africa. These portend water availability challenges with direct impacts on agricultural production. Existing studies on yield vulnerability in Nigeria are mostly at a national scale, which is not adequate for local decision making. This study provides a spatially explicit model of Maize yield vulnerabilities across the growing areas (GA). Thereby, turning available data into actionable information to support development actions. Yield vulnerability index was constructed as a relationship among exposure, yield sensitivity and adaptive capacity. Exposure was computed as the ratio between long and short-term climatic factors. Yield sensitivities were expressed as the ratio between expected and actual yield. Adaptive capacity was captured using a combination of socio-economic proxies. The result shows that Maize yields were vulnerable to climate variability across most of the GAs. Exposure values indicate a very high level of climate variability with the northern region more exposed. Yield sensitivity ranges between ranges 0.47 and 0.95, and highest along the northern extremes, moderate sensitivities were observed across large tracts of the north-west, northeast, south-east and south–south geopolitical regions. Adaptive capacity is highly variable ranging between 0.27 and 1. Yield vulnerability ranges between 0.46 and 1.51. The general assumption of a north–south divide for yield vulnerability was invalidated. Vulnerability is more disparate beyond latitudinal differences. The model presented, creates a framework to support targeted response, and opportunity for building resilience to climate change impact for crop yield. [ABSTRACT FROM AUTHOR]

The low availability of high-quality meteorological data resulted in the development of synthetic meteorological data generated by satellite or data interpolation, which are available in grids with varying spatio-temporal resolution. Among these different data sources, NASA/POWER and DailyGridded databases have been applied for crop yield simulations. The objective of this study was to evaluate the performance of these two datasets, in different time scales (daily, 10-day, monthly, and annual), as input data for estimating potential (YP) and attainable (YA) maize yields, using the FAO Agroecological Zone crop simulation model (FAO-AEZ), properly calibrated and validated. For that, daily weather data from ten Brazilian locations were collected and compared to the data extracted from NASA/POWER and DailyGridded systems and later applied to estimate the potential and attainable maize yields. DailyGridded data showed a better performance than NASA/POWER for all weather variables and time scales, with confidence index (C) ranging from 0.52 to 0.99 for the former and from 0.09 and 0.99 for the latter. As a consequence of that, DailyGridded data was better than NASA/POWER to estimate maize yields with estimates close to those obtained with observed data, with a lower mean absolute errors (< 30 kg ha−1) and a higher confidence index (C = 0.99). [ABSTRACT FROM AUTHOR]

In this study we used meteorological parameters and predictive modelling interpreted by model explanation to develop stress metrics that indicate the presence of drought and heat stress at the specific environment. We started from the extreme temperature and precipitation indices, modified some of them and introduced additional drought indices relevant to the analysis. Based on maize's sensitivity to stress, the growing season was divided into four stages. The features were calculated throughout the growing season and split in two groups, one for the drought and the other for heat stress. Generated meteorological features were combined with soil features and fed to random forest regression model for the yield prediction. Model explanation gave us the contribution of features to yield decrease, from which we estimated total amount of stress at the environments, which represents new environmental index. Using this index we ranked the environments according to the level of stress. More than 2400 hybrids were tested across the environments where they were grown and based on the yield stability they were marked as either tolerant or susceptible to heat, drought or combined heat and drought stress. Presented methodology and results were produced within the Syngenta Crop Challenge 2019. [ABSTRACT FROM AUTHOR]

Water is a key driver of agricultural production, the scarcity of which the production is adversely affected; hence, it is critical for the agriculture system and global food security. Water use efficiency (WUE) can be an effective measure to reduce water demand against crop yield as it depends upon water consumption. Thus, the purpose of this research was aimed to estimate the effect of variation in sowing time (timely and late) and nitrogen (N) level on the evapotranspiration (ET) and WUE for maize crop under irrigated (2013 and 2014) and rainfed (2012 and 2014) conditions. Two evapotranspiration (ET) approaches, i.e., Penman–Monteith (PM) and soil water balance (SWB), were used to estimate the evapotranspiration; thereafter, evapotranspiration was partitioned into soil evaporation (E) and transpiration (T). The results clearly indicated that cumulative evapotranspiration was higher for both rainfed (5.44–10.25%; N60–N100) and irrigated maize (5.87–13.77%; N75–N125) in comparison with N0 nitrogen level. The delayed sowing dates gave on average a lower value (9.56%) and a higher value (15.68%) of ET for the rainfed and irrigated seasons, respectively, in comparison with timely sowing dates. Additionally, the WUE for maize grain yield was higher for both rainfed (251.12–346.06%; N60–N100) and irrigated maize (113.75–162.62%; N75–N125) in comparison with N0 nitrogen level. The study further disclosed that a sowing date combination with nitrogen levels could be an effective management strategy to increase the crop yield by minimizing the water losses. [ABSTRACT FROM AUTHOR]

Maize, the highest-yielding cereal crop worldwide, is particularly susceptible to drought during its 2- to 3-week flowering period. Many genetic engineering strategies for drought tolerance impinge on plant development, reduce maximum yield potential or do not translate from laboratory conditions to the field. We overexpressed a gene encoding a rice trehalose-6-phosphate phosphatase (TPP) in developing maize ears using a floral promoter. This reduced the concentration of trehalose-6-phosphate (T6P), a sugar signal that regulates growth and development, and increased the concentration of sucrose in ear spikelets. Overexpression of TPP increased both kernel set and harvest index. Field data at several sites and over multiple seasons showed that the engineered trait improved yields from 9% to 49% under non-drought or mild drought conditions, and from 31% to 123% under more severe drought conditions, relative to yields from nontransgenic controls. [ABSTRACT FROM AUTHOR]

Popcorn breeding programs in Brazil have emerged but despite some advances there is still a lack of material performance studies specially regard to seed quality, in different agricultural seasons. This research was carried out to estimate the popcorn combining ability from biparental crosses between eight tropical and one temperate lines in two agricultural seasons (2008 and 2009) with regard to eight traits related to seed quality, in addition to three agronomic traits related to popping expansion. Method 4, proposed by Griffing (Model 1), was used to verifying the importance of the dominance effect in the genetic control of the seed quality traits, favouring the identification of superior hybrids through seed tests in the laboratory with low cost. The lines from Zélia and CMS42 were promising in obtaining hybrids with superior seed quality and greater popping expansion. The hybrids Zélia × IAC-112, CMS42 × UEM M2-1, CMS43-1 × IAC-112 and UEM M2-2 × Zaeli expressed superior SCA for grain yield, popping expansion and at least two seed quality traits. These hybrids are materials of interest for cultivation in different planting seasons. [ABSTRACT FROM AUTHOR]

Oil content and grain yield in maize are negatively correlated, and so far the development of high-oil high-yielding hybrids has not been accomplished. Then a fully understand of the inheritance of the kernel oil content is necessary to implement a breeding program to improve both traits simultaneously. Conventional and molecular marker analyses of the design III were carried out from a reference population developed from two tropical inbred lines divergent for kernel oil content. The results showed that additive variance was quite larger than the dominance variance, and the heritability coefficient was very high. Sixteen QTL were mapped, they were not evenly distributed along the chromosomes, and accounted for 30.91% of the genetic variance. The average level of dominance computed from both conventional and QTL analysis was partial dominance. The overall results indicated that the additive effects were more important than the dominance effects, the latter were not unidirectional and then heterosis could not be exploited in crosses. Most of the favorable alleles of the QTL were in the high-oil parental inbred, which could be transferred to other inbreds via marker-assisted backcross selection. Our results coupled with reported information indicated that the development of high-oil hybrids with acceptable yields could be accomplished by using marker-assisted selection involving oil content, grain yield and its components. Finally, to exploit the xenia effect to increase even more the oil content, these hybrids should be used in the Top Cross procedure. [ABSTRACT FROM AUTHOR]

Grain yield is more likely to be compromised by poor seed germination and vigour in low phytic acid (LPA) and quality protein maize (QPM) than normal maize (Nm), especially when grown under stressful tropical environmental conditions. The objectives of this study were to determine the effect of stress conditions on seed germination and vigour traits (percentage germination, seedling dry weight, average root and shoot length and vigour index) and to determine the GCA effects of the parental lines and SCA effects of the crosses. A ten parent half diallel (LPA, Nm, QPM lines) was subjected to the standard germination (non-stress) and accelerated aging (AA) (stress) tests. All seed lots were at the same physiological age and produced under the same season and conditions. Genotypic and group differences were investigated. General combining ability (GCA) and specific combining ability (SCA) effects were significant ( P ≤ 0.001) for all traits under both stress and non-stress conditions indicating that both additive and non-additive gene effects were significant. Generally SCA effects were superior for all traits. The LPA lines displayed 61 % reduction in germination and 23-52 % reduction in vigour under stress conditions thereby underscoring challenges that are expected in deploying LPA maize in stress conditions. However, LPA line CM 31 exhibited large positive and mostly significant GCA effects, while two LPA × LPA crosses showed significant positive SCA effects. Results indicate breeding is required to improve both germination and vigour of the LPA lines to adapt them to tropical conditions that are generally stress-prone. [ABSTRACT FROM AUTHOR]

ificial selection (domestication and breeding) leaves a strong footprint in plant genomes. Second generation high throughput DNA sequencing technologies make it possible to sequence the gene complement of a plant genome within 3 to 5 months, and the costs of doing so are declining very quickly. This makes it practical to identify genomic regions that have undergone very strong selection. Available reference sequences of important crops such as rice, maize, and sorghum will promote the wide use of re-sequencing strategies in these crops. Marker/trait associations, especially haplotype (or haplotype block) association analyses, will help the precise mapping of important genomic regions and location of favored alleles or haplotypes for breeding. This mini-review examines a genomics approach to defining yield traits in wheat. [ABSTRACT FROM AUTHOR]

The application of biochar (biomass-derived black carbon) to soil has been shown to improve crop yields, but the reasons for this are often not clearly demonstrated. Here, we studied the effect of a single application of 0, 8 and 20 t ha−1 of biochar to a Colombian savanna Oxisol for 4 years (2003–2006), under a maize-soybean rotation. Soil sampling to 30 cm was carried out after maize harvest in all years but 2005, maize tissue samples were collected and crop biomass was measured at harvest. Maize grain yield did not significantly increase in the first year, but increases in the 20 t ha−1 plots over the control were 28, 30 and 140% for 2004, 2005 and 2006, respectively. The availability of nutrients such as Ca and Mg was greater with biochar, and crop tissue analyses showed that Ca and Mg were limiting in this system. Soil pH increased, and exchangeable acidity showed a decreasing trend with biochar application. We attribute the greater crop yield and nutrient uptake primarily to the 77–320% greater available Ca and Mg in soil where biochar was applied. [ABSTRACT FROM AUTHOR]

Denitrification is known as an important pathway for nitrate loss in agroecosystems. It is important to estimate denitrification fluxes to close field and watershed N mass balances, determine greenhouse gas emissions (N2O), and help constrain estimates of other major N fluxes (e.g., nitrate leaching, mineralization, nitrification). We compared predicted denitrification estimates for a typical corn and soybean agroecosystem on a tile drained Mollisol from five models (DAYCENT, SWAT, EPIC, DRAINMOD-N II and two versions of DNDC, 82a and 82h), after first calibrating each model to crop yields, water flux, and nitrate leaching. Known annual crop yields and daily flux values (water, nitrate-N) for 1993–2006 were provided, along with daily environmental variables (air temperature, precipitation) and soil characteristics. Measured denitrification fluxes were not available. Model output for 1997–2006 was then compared for a range of annual, monthly and daily fluxes. Each model was able to estimate corn and soybean yields accurately, and most did well in estimating riverine water and nitrate-N fluxes (1997–2006 mean measured nitrate-N loss 28 kg N ha−1 year−1, model range 21–28 kg N ha−1 year−1). Monthly patterns in observed riverine nitrate-N flux were generally reflected in model output ( r2 values ranged from 0.51 to 0.76). Nitrogen fluxes that did not have corresponding measurements were quite variable across the models, including 10-year average denitrification estimates, ranging from 3.8 to 21 kg N ha−1 year−1 and substantial variability in simulated soybean N2 fixation, N harvest, and the change in soil organic N pools. DNDC82a and DAYCENT gave comparatively low estimates of total denitrification flux (3.8 and 5.6 kg N ha−1 year−1, respectively) with similar patterns controlled primarily by moisture. DNDC82h predicted similar fluxes until 2003, when estimates were abruptly much greater. SWAT and DRAINMOD predicted larger denitrification fluxes (about 17–18 kg N ha−1 year−1) with monthly values that were similar. EPIC denitrification was intermediate between all models (11 kg N ha−1 year−1). Predicted daily fluxes during a high precipitation year (2002) varied considerably among models regardless of whether the models had comparable annual fluxes for the years. Some models predicted large denitrification fluxes for a few days, whereas others predicted large fluxes persisting for several weeks to months. Modeled denitrification fluxes were controlled mainly by soil moisture status and nitrate available to be denitrified, and the way denitrification in each model responded to moisture status greatly determined the flux. Because denitrification is dependent on the amount of nitrate available at any given time, modeled differences in other components of the N cycle (e.g., N2 fixation, N harvest, change in soil N storage) no doubt led to differences in predicted denitrification. Model comparisons suggest our ability to accurately predict denitrification fluxes (without known values) from the dominant agroecosystem in the midwestern Illinois is quite uncertain at this time. [ABSTRACT FROM AUTHOR]

The article discusses the collaboration of Mexico and United States to improve Mexican Corn during 1940-1950. It is said that Rockefeller Foundation program initially was committed to a type of improved open-pollinated corn while the Mexican program proceeded with a strict U.S.-style hybrid program. The priority of each group on yield is being clarified. Breeding priorities that precluded any collaboration between the two is also being cited.

Under low-input cropping systems, nitrogen (N) can be a limiting factor in plant growth and yield. Identifying genotypes that are more efficient at capturing limited N resources and the traits and mechanisms responsible for this ability is important. Root trait has a substantial influence on N acquisition from soils. Nevertheless, inconsistencies still exist as to the effect of low N on root length and its architecture in terms of lateral and axial roots. For maize, a crop utilizing heterosis, little is known about the relationship between parents and their crosses in the response of root architecture to N availability. Here 7 inbred maize lines and 21 of their crosses created by diallel mating were used to study the effect of N stress on root morphology as well as the relationship between the inbreds and their crosses. With large genotypic differences, low N generally suppresses shoot growth and increases the root to shoot ratio with or without increasing root biomass in maize. Maize plants responded to N deficiency by increasing total root length and altering root architecture by increasing the elongation of individual axial roots and enhancing lateral root growth, but with a reduction in the number of axial roots. Here, the inbreds showed weaker responses in root biomass and other root parameters than their crosses. Heterosis of root traits was significant at both N levels and was attributed to both the general combining ability (GCA) and special combining ability (SCA). Low N had substantial affects on the pattern of heterosis, GCA and SCA affects on root traits for each of the crosses suggesting that selection under N stress is necessary in generating low N-tolerant maize genotypes. [ABSTRACT FROM AUTHOR]

Inoculation with Azospirillum brasilense exerts beneficial effects on plant growth and crop yields. In this study, a comparative analysis of maize ( Zea mays) root inoculated or not inoculated with A. brasilense strains was performed in two soils. Colonization dynamics of the rhizobacteria were tracked in various root compartments using 16S rRNA-targeted probes and 4′,6′diamidino-2-phenylindole staining, and the structure of bacterial populations in the same samples was analyzed by denaturing gradient gel electrophoresis (DGGE) of polymerase chain reaction products of the 16S rRNA gene. Based on whole cell hybridization, a large fraction of the bacterial community was found to be active in both the rhizoplane–endorhizosphere and rhizosphere soil compartments, in both soil types. A DGGE fingerprint analysis revealed that plant inoculation with A. brasilense had no effect on the structural composition of the bacterial communities, which were also found to be very similar at the root tip and at zones of root branching. However, rhizobacterial populations were strongly influenced by plant age, and their complexity decreased in the rhizoplane–endorhizosphere in comparison to rhizosphere soil. A clone library generated from rhizosphere DNA revealed a highly diverse community of soil and rhizosphere bacteria, including an indigenous Azospirillum-like organism. A large proportion of these clones was only distantly related to known species. [ABSTRACT FROM AUTHOR]

Summary The crop growth model CERES-Maize is used to estimate the direct (through enhanced fertilisation effect of ambient CO 2) and indirect (through changed climate conditions) effects of increased concentration of atmospheric CO 2 on maize yields. The analysis is based on multi-year crop model simulations run with daily weather series obtained alternatively by a direct modification of observed weather series and by a stochastic weather generator. The crop model is run in two settings: stressed yields are simulated in water and nutrient limited conditions, potential yields in water and nutrient unlimited conditions. The climate change scenario was constructed using the output from the ECHAM3/T42 model (temperature), regression relationships between temperature and solar radiation, and an expert judgement (precipitation). Results: (i) After omitting the two most extreme misfits, the standard error between the observed and modelled yields is 11%. (ii) The direct effect of doubled CO 2: The stressed yields would increase by 36?41% in the present climate and by 61?66% in the 2???CO 2 climate. The potential yields would increase only by 9?10% as the improved water use efficiency does not apply. (iii) The indirect effect of doubled CO 2: The stressed yields would decrease by 27?29% (14?16%) at present (doubled) ambient CO 2 concentration. The increased temperature shortens the phenological phases and does not allow for the optimal development of the crop. The simultaneous decrease of precipitation and increase of temperature and solar radiation deepen the water stress, thereby reducing the yields. The reduction of the potential yields is significantly smaller as the effect of the increased water stress does not apply. (iv) If both direct and indirect effects of doubled CO 2 are considered, the stressed yields should increase by 17?18%, and the potential yields by 5?14%. (v) The decrease of the stressed yields due to the indirect effect may be reduced by applying earlier planting dates. [ABSTRACT FROM AUTHOR]

Maize (corn) is the dominant grain grown in the world. Total maize production in 2018 equaled 1.12 billion tons. Maize is used primarily as an animal feed in the production of eggs, dairy, pork and chicken. The US produces 32% of the world's maize followed by China at 22% and Brazil at 9% (https://apps.fas.usda.gov/psdonline/app/index.html#/app/home). Accurate national-scale corn yield prediction critically impacts mercantile markets through providing essential information about expected production prior to harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. We build a deep learning model to predict corn yields, specifically focusing on county-level prediction across 10 states of the Corn-Belt in the United States, and pre-harvest prediction with monthly updates from August. The results show promising predictive power relative to existing survey-based methods and set the foundation for a publicly available county yield prediction effort that complements existing public forecasts. [ABSTRACT FROM AUTHOR]