Maize Canopy Research Articles

Modeling Maize Canopy Morphology in Response to Increased Plant Density.

Increased plant density markedly affects canopy morphophysiological activities and crop productivity. This study aims to model maize canopy final morphology under increased interplant competition by revising a functional–structural plant model, i.e., ADEL-Maize. A 2-year field experiment was conducted at Mengcheng, Anhui Province, China, in 2016 and 2018. A randomized complete block design of five plant densities (PDs), i.e., 4.5, 6, 7.5, 9, and 15 plants m–2, with three replications was applied using a hybrid, i.e., Zhengdan 958. Canopy morphology at different PDs was measured with destructive samplings when maize canopy was fully expanded. The relationship of changes of organ morphology in relation to increased plant density was analyzed based on 2016 data. The ADEL-Maize was first calibrated for the hybrid at 4.5 plants m–2 and then revised by introducing relationships identified from 2016 data, followed by independent validation with 2018 field data. A heatmap visualization was shown to clearly illustrate the effects of increased plant density on final morphology of laminae, sheaths, and internodes. The logarithmic + linear equations were found to fit changes for the organ size versus increased plant density for phytomers excluding ear position or linear equations for the phytomer at ear position based on 2016 field data. The revision was then further tested independently by having achieved satisfactory agreements between the simulations and observations in canopy size under different PDs with 2018 field data. In conclusion, this study has characterized the relationship between canopy morphology and increased interplant competition for use in the ADEL-Maize and realized the simulations of final size of laminae, sheaths, and internodes, as affected by increased plant density, laying a foundation to test an ideotype for maize withstanding high interplant competition.

Effect of a growing maize canopy on solid-set sprinkler irrigation: kinetic energy dissipation and water partitioning

The energy of sprinkler irrigation drops splashing on the soil surface or the crop canopy depends on their size and velocity. The kinetic energy of sprinkler irrigation drops is a major factor degrading the soil surface. The effect of a growing maize canopy on sprinkler drop energy dissipation and water partitioning was analyzed. A field experiment was performed on a solid-set sprinkler irrigated plot cropped with maize. Measurements were performed at different locations and heights into the sprinkler layout. Tests were performed at five maize heights. At each maize height, drop population characteristics and pluviometry were measured above and below the maize canopy. Drop population was measured with an optical disdrometer. Catch-can devices were used to measure pluviometry. Solid-set sprinkler irrigation distributes water quite homogeneously, but drop characteristics largely differ between layout locations. The effect of the crop on drop characteristics intensified as the canopy height increased. The canopy cover reduced drop diameter more than it reduced drop velocity. As the canopy grew, its capacity to intercept and derive irrigation water to stemflow increased. The largest part of energy dissipation by the maize canopy took place in the top 1.5 m. A fully developed maize canopy reduced the drop kinetic energy reaching the soil to 13.6% of that reaching bare soil. As a management practice, in sensitive soils or in sprinkler systems applying water with high specific kinetic power, irrigation of bare soils should be avoided.

Improving the Spatial and Temporal Estimation of Maize Daytime Net Ecosystem Carbon Exchange Variation Based on Unmanned Aerial Vehicle Multispectral Remote Sensing

Accurate estimation of net ecosystem carbon exchange (NEE) is vital to regional carbon balance. Currently, NEE observations at the canopy scale are mainly based on the chamber method. However, the chamber method is labor intensive, time consuming, and measures only plot-scale NEE. It cannot reflect whole-field NEE with high spatial resolution. In this article, maize daytime NEE variations in four fields under different irrigation treatments in a semiarid area was measured using the chamber method, and the spectral reflectance in the maize canopy at noon was obtained using an unmanned aerial vehicle (UAV) multispectral system. We established a daytime NEE variation estimation model and up-scaled the level of NEE observations in maize canopy using UAV-based remote sensing. Twelve widely used vegetation indices were employed for NEE estimation. To obtain an optimal NEE variation estimation method, we compared the performance of several models, including simple linear regression, multiple stepwise regression, and four machine learning (ML) algorithms. Based on the comparison, the modified triangular vegetation index-2 is the best predictor for analyzing simple linear regression, with a coefficient of determination R 2 = 0.719. Compared with the simple linear regression, there is no substantial increase in the R 2 of NEE estimation based on multiple stepwise regression. However, the ML algorithms greatly improved R 2 values. In particular, the gradient boosting regression model exhibits the best performance ( R 2 = 0.856). This article demonstrates that high-resolution UAV multispectral remote sensing shows great potential in improving the spatial and temporal estimating of maize daytime NEE variations.

不同灌溉量夏玉米叶绿素含量的高光谱特征及其反演

植物叶绿素含量直接影响其光合作用，并与植物的光谱特征密切相关。以夏玉米为研究对象，采用人工控水方法研究了夏玉米七叶期不同灌溉量下冠层叶绿素含量特征及其与光谱特征之间的关系。结果表明：灌溉量越少，夏玉米叶片叶绿素含量越低，冠层光谱反射率越高，绿峰位置"红移"，而红边位置"蓝移"。叶绿素含量与光谱特征参数、植被光谱指数之间存在极显著相关关系，据此建立了冠层叶绿素含量高光谱估算模型，且基于植被指数模型较基于单一光谱特征参数模型模拟效果更好。研究结果可为夏玉米叶绿素含量的快速无损测定以及夏玉米干旱监测提供依据。;Plant chlorophyll content directly affects its photosynthesis and is closely related to the spectral characteristics of plants. In this study, summer maize was used as the research object, and the characteristics of chlorophyll content and its spectral characteristics under different water irrigations of summer maize at seven-leaf stage were studied by the water control method. The results showed that with the decrease of water irrigation, the chlorophyll content of the summer maize canopy leaves decreased and the spectral reflectance of the canopy increased. At the same time, the green peak position was red shifted, but the red edge position was blue shifted. There was a significant relationship of chlorophyll content with spectral characteristic parameters and vegetation spectral index. Based on these relationships, several hyperspectral inversion models of canopy chlorophyll content were established. The model validations indicated that canopy chlorophyll content was simulated better by vegetation index than single spectral characteristic parameter. The results provide the basis for rapid non-destructive determination of chlorophyll content in summer maize and drought monitoring of summer maize production.

Calibration and validation of APSIM–Maize, DSSAT CERES–Maize and AquaCrop models for Ethiopian tropical environments

Process-based crop models are popular tools to quantify the impact of changes due to climate or crop management. Accurate simulation of crop production for different agro-ecological conditions using an individual crop model remains challenging due to different sources of uncertainty. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are limited in tropical environments, including Ethiopia. Therefore, the aim of this study was to compare the performance of the outputs of three individual crop models and their ensemble mean. We calibrated three different crop models, namely, APSIM-maize, AquaCrop and DSSAT CERES-maize and evaluated them separately and in a multimodel ensemble approach using four maize varieties (BH546, BH661, Jibat and MH140) grown under rainfed conditions. Model input data were collected from field experiments conducted at three sites (Ambo, Bako and Melkassa) during the 2017/2018 crop growing season. The experiments were laid out in a randomised complete block design using a plot size of 10 m × 10 m. The crop models were calibrated using measured data from the Bako and evaluated with independent datasets from the Ambo and Melkassa. The calibration parameters used in each of the three crop models studied enabled accurate simulation of flowering, maturity, canopy cover (AquaCrop) and grain yield against measured data. Evaluation of the models indicated that APSIM-maize and DSSAT CERES-maize accurately simulated days to flowering and maturity with root mean square error (RMSE) values ranging from 1.73–4.09 and 1.66–5.36 days, respectively. However, the DSSAT CERES-maize model over-estimated the maturity period of late-maturing varieties at Ambo. The AquaCrop model accurately simulated maize canopy cover for all varieties studied with a RMSE of less than 10.8% and a high index of agreement (d) of 0.95. The simulated grain yield agreed fairly well with the measured data, with normalised RMSE ranging from 13–19%, 1–4% and 1–17% for APSIM, AquaCrop and DSSAT maize models, respectively. However, the APSIM model underestimated yield for all maize varieties at Ambo (RMSE of 1.14 t ha−1 and d-value of 0.50). The best performance was obtained when an ensemble of all models was considered, which reduced the RMSE values for grain yield to 0.35 t ha−1 at Ambo and 0.41 t ha−1 at Melkassa. Furthermore, the ensemble mean reduced the normalised RMSE by 8% while increasing the d-value to above 0.90 for both evaluation sites. On the other hand, the ensemble results were quite similar for grain yield simulated using the AquaCrop model. It is concluded that model ensembles reduced model uncertainty and improved simulation output accuracy compared to the outputs of individual models in tropical environments.

Effect of altered leaf angle on maize stalk lodging resistance

AbstractIncreasing light intensity in the mid and low canopy can improve maize (Zea mays L.) stalk lodging resistance. Field experiments involving altered leaf angle were designed to explore how leaf angle affects the light distribution within the canopy, stalk carbohydrate accumulation, stalk strength formation, and grain yield. When the leaf area was constant and the average leaf angle was decreased by ∼10° above the ear leaves and increased by ∼25° at the ear leaves and below, the photosynthetically active radiation (PAR) at the ear layer increased by 41–62%, and the interception of PAR (IPAR) below the ear increased by 107–229%. Average cellulose content of the third and fourth internodes increased by 13–21%, whereas lignin content increased by 9–30%. The rind penetration strength (RPS) of the third internode increased by 13–21%, whereas the crush strength (CS) of the fourth internode increased by 15–27%. These changes resulted in a reduction of 6–11% in the lodging percentage. The grain yield increased by 15–27% because of increases in kernel number and kernel weight. Therefore, under close planting, selecting a maize ideotype with upright leaves in the upper canopy and flat leaves in the lower canopy is conducive to rational radiation allocation in the maize canopy. This would facilitate structural carbohydrate accumulation and mechanical strength formation while improving lodging resistance and grain yield.

Water distributions of low-pressure sprinklers as affected by the maize canopy under a centre pivot irrigation system

Selection of a suitable low-pressure sprinkler can be an effective measure to reduce energy consumption, wind drift and evaporation losses. The crop canopy often redistributes the applied water during sprinkler irrigation. Therefore, we investigated the water distributions above and below the canopy as well as their differences in Heermann and Hein uniformity coefficient (CUH) under three commonly used sprinkler types (Nelson D3000, Nelson R3000, and Komet KPT) and two growth stages of summer maize (6-leaf stage and tasselling stage) to evaluate the effect of crop canopy on the water distribution of low-pressure sprinklers. The variations in the soil water content (SWC) and Heermann and Hein uniformity coefficient of soil water content (CUHS) were investigated further at 1 h before, 1 h after, and 24 h. Additionally, the effects of growth indices and sprinkler types on CUH and CUHS values were analysed. Both the water application depths and CUH values above the canopy were significantly higher than those below the canopy and the differences above and below the canopy increased significantly with the growth of maize. The CUHS after irrigation mainly depended on the initial SWC and CUHS values but was minimally correlated with the plant height, leaf area index, and sprinkler application uniformity. Although the CUH above the canopy (78.1%) of D3000 was significantly lower than R3000 and KPT sprinklers (both exceeding 90%), the CUHS of D3000 reached more than 93% after irrigation. This result indicated that the effect of sprinkler application uniformity was not important as predicted, and a good CUHS would be achieved after canopy interception and soil water redistribution irrespective of sprinkler types. This study provides an insight into the selection of best low-pressure sprinklers in centre pivot irrigation systems.

A new empirical equation to describe the vertical leaf distribution profile of maize

AbstractThe characteristic traits of maize (Zea mays L.) leaves affect light interception and photosynthesis. Measurement or estimation of individual leaf area has been described using discontinuous equations or bell-shaped functions. However, new maize hybrids show different canopy architecture, such as leaf angle in modern maize which is more upright and ear leaf and adjacent leaves which are longer than older hybrids. The original equations and their parameters, which have been used for older maize hybrids and grown at low plant densities, will not accurately represent modern hybrids. Therefore, the aim of this paper was to develop a new empirical equation that captures vertical leaf distribution. To characterize the vertical leaf profile, we conducted a field experiment in Jilin province, Northeast China from 2015 to 2018. Our new equation for the vertical distribution of leaf profile describes leaf length, width or leaf area as a function of leaf rank, using parameters for the maximum value for leaf length, width or area, the leaf rank at which the maximum value is obtained, and the width of the curve. It thus involves one parameter less than the previously used equations. By analysing the characteristics of this new equation, we identified four key leaf ranks (4, 8, 14 and 20) for which leaf parameter values need to be quantified in order to have a good estimation of leaf length, width and area. Together, the method of leaf area estimation proposed here adds versatility for use in modern maize hybrids and simplifies the field measurements by using the four key leaf ranks to estimate vertical leaf distribution in maize canopy instead of all leaf ranks.

Estimation of the vertically integrated leaf nitrogen content in maize using canopy hyperspectral red edge parameters

Real-time monitoring of leaf nitrogen (N) content by remote sensing can accurately diagnose crop nutrient status and facilitate precision N management. However, the methods used to estimate of vertically integrated leaf N content do not consider different cropping systems, in which the maize growth stages are not synchronized, resulting in decreased practical value of the results. The purpose of this study was to propose an optimized red-edge absorption area (OREA) index in which the prediction accuracy of vertically integrated leaf N content is improved within spring- and summer-sown maize canopies. The results showed that vertical distributions of N existed regardless of variations in the maize growth stages, that is, the leaf N density of the upper and middle layers was higher than that of the lower layers. These published vegetation indices (VIs) provided relatively good correlations with leaf N density at different layers across all of the datasets. When predicting leaf N density of each leaf layer, an optimal VI is generated, and inconsistent VIs will limit its practical application. To further overcome the drawbacks of the inconsistency of each VI when estimating the leaf N density at different layers, a new OREA index was proposed based on red-edge absorption area parameter. The OREA index showed the highest prediction accuracy with leaf N density for entire canopies (r2 = 0.811, RMSE = 0.374, RE = 13.17%) and canopies without top first leaves (r2 = 0.795, RMSE = 0.269, RE = 15.20%) compared with the other published VIs. It is concluded that the vertically integrated leaf N content under different field experiments can be accurately estimated by the OREA index.

A novel plant growth regulator improves the grain yield of high-density maize crops by reducing stalk lodging and promoting a compact plant type

• DHEAP is a novel plant growth regulator. • DHEAP improved maize lodging resistance. • DHEAP shaped a compact maize plant type. • DHEAP is helpful to increases summer maize planting density in NCP. Increasing the planting density (PD) of maize ( Zea mays L.) crops is the most efficient method for enhancing maize grain yield, but supra-optimum PD leads to an increase in lodged maize, which alters the maize canopy structure. In this study, N, N-Diethyl-2-hexanoyl oxygen radicals-ethyl amine (2-ethyl chloride) phosphonic acid salt (DHEAP, a single compound), a novel plant growth regulator, was sprayed at the seven-expanded-leaf stage under two different PDs (75,000, and 90,000 plants ha -1 ) using two maize hybrids, ZD958 (lodging-resistance) and XY335 (lodging-susceptible), to reduce maize lodging rates and improve the maize canopy structure under high PD. After DHEAP treatment, the maize lodging rate of ZD958 and XY335 decreased by 70.87% and 75.22%, respectively, through a change in the morphological structure (i.e., reduced plant and ear heights and increased mean tilt angle) and an improvement in the basal internode quality (i.e., increased diameter, dry weight per unit length, crushing strength, cellulose content, lignin content, and vascular area, and reduced length). In addition, DHEAP treatment significantly reduced gibberellin (GA 3 ) contents in maize basal internodes. Moreover, the increase in the mean tilt angle (MTA) above the maize ear leaf after DHEAP treatment improved the light penetration in the middle leaf strata, which increased the chlorophyll content (SPAD), net photosynthetic rate (P n ), and Rubisco enzyme activities. Finally, DHEAP treatment increased the grain yield of ZD958 and XY335 under 90,000 PD by 22.28% and 43.14%, respectively. Thus, it was concluded that the spraying of DHEAP effectively reduced maize lodging rates and improved the photosynthetic performance of the maize ear leaf, which increased maize grain yield under high PDs.

Effects of phytase Q9 on the yield and senescence characteristics of summer maize shaded in the field

Light shortage in the canopy of summer maize resulted from the decrease of solar radiation and the increase of planting density in Huanghuaihai region could reduce maize yield. In order to explore the effects of phytase Q9 on leaf senescence characteristics of summer maize, three sha-ding treatments with summer maize hybrid 'Denghai 605' (DH605) were conducted, including shading at flowering to maturity stage (S1), shading at ear stage (S2), and shading at whole growth stage (S3) with natural lighting in the field as control (CK). Chemical control reagent phytase Q9 was used to regulate the shading treatments (the original solution was diluted by 100 times) and the CK exogenously, namely shading at flowering to maturity stage-phytase Q9 (S1Q), shading at ear stage-phytase Q9 (S2Q), and shading at whole growth stage-phytase Q9 (S3Q), and natural lighting-phytase Q9 (CKQ), with spraying water at the same stage as the control. The results showed that leaf area index (LAI), soil-plant analysis development (SPAD) value and net photosynthetic rate of summer maize was significantly reduced by shading, which led to decreases of yield. The activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT) in ear-leaf decreased. The content of malondialdehyde (MDA) and free proline increased, and that of soluble protein decreased. The spraying phytase Q9 significantly increased LAI, SPAD and net photosynthetic rate in S3Q and S2Q. MDA and free proline content in S3Q, S2Q, and S1Q were significantly decreased, and soluble protein content and POD activity was significantly increased. SOD and CAT activities in S2Q and S3Q were significantly increased. The yield of S3Q, S2Q and S1Q were 19%, 8% and 7% higher than that of S3, S2 and S1 respectively. There was no significant difference between CKQ and CK. In conclusion, phytase Q9 could effectively alleviate the negative impact of low light on yield formation of summer maize, and increase grain yield by improving photosynthetic capacity of leaves and delaying leaf senescence.

Retrieving fPAR of maize canopy using artificial neural networks with airborne LiDAR and hyperspectral data

ABSTRACT Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) is important for maize growth and yield estimations. Light detection and ranging (LiDAR)-derived canopy vertical structural and hyperspectral image-derived vegetation spectral information are complementary for vegetation fPAR estimation. This study explores the potential of artificial neural networks (ANNs) with two types of data to estimate maize fPAR. First, 45 metrics were derived from LiDAR data and 13 from a hyperspectral image. Then, the ANNs and stepwise multiple linear regression (SMLR) methods were used to estimate the fPAR. Finally, model validity was assessed using in-situ data. Results showed that the ANNs performed better in fPAR inversion (R 2 = 0.910, adj. R 2 = 0.921, RMSE = 0.046, RRMSE = 0.056, where R 2 is the coefficient of determination, adj. R 2 the adjusted coefficient of determination, RMSE the root mean squared error, and RRMSE the relative root mean squared error) than SMLR (R 2 = 0.638, adj. R 2 = 0.609, RMSE = 0.077, RRMSE = 0.092) and SMLR with the natural logarithm of data (R 2 = 0.855, adj. R 2 = 0.825, RMSE = 0.067, RRMSE = 0.081). This study is helpful for guiding the accurate estimation of maize fPAR using remote sensing techniques.

A PHYSICAL INVERSION METHOD OF CANOPY FPAR FROM AIRBORNE LIDAR DATA AND GROUND MEASUREMENTS

Abstract. Fraction of absorbed Photosynthetically Active Radiation (FPAR) is one of the pivotal parameters in terrestrial ecosystem modelling and crop growth monitoring. Airborne LiDAR is an advanced active remote sensing technology which can acquire fine three-dimensional canopy structural information quickly and accurately. Although some previous studies have shown that LiDAR-derived metrics had strong relationships with canopy FPARs, these estimation models without physical meaning are hard to be extended to various vegetation canopies and different growth periods. This study proposed a physical FPAR inversion method based on airborne LiDAR data and field measurements. The method considered direct and diffuse radiations separately based on the SAIL model and energy budget balance principle. The canopy FPAR was inversed from the structural information provided by LiDAR point cloud data and the spectral information provided by ground measurements. The estimated FPAR was validated with the field-measured FPAR over 39 maize plots. Results showed that the proposed method had a good performance in estimating the total FPAR of maize canopy (R2 = 0.76, RMSE = 0.062, n = 39). This study provides the potential to estimate the total, direct, and diffuse FPARs of vegetation canopy from airborne LiDAR data.

Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery

In order to improve the diagnosis accuracy of chlorophyll content in maize canopy, the remote sensing image of maize canopy with multiple growth stages was acquired by using an unmanned aerial vehicle (UAV) equipped with a spectral camera. The dynamic influencing factors of the canopy multispectral images of maize were removed by using different image segmentation methods. The chlorophyll content of maize in the field was diagnosed. The crop canopy spectral reflectance, coverage, and texture information are combined to discuss the different segmentation methods. A full-grown maize canopy chlorophyll content diagnostic model was created on the basis of the different segmentation methods. Results showed that different segmentation methods have variations in the extraction of maize canopy parameters. The wavelet segmentation method demonstrated better advantages than threshold and ExG index segmentation methods. This method segments the soil background, reduces the texture complexity of the image, and achieves satisfactory results. The maize canopy multispectral band reflectance and vegetation index were extracted on the basis of the different segmentation methods. A partial least square regression algorithm was used to construct a full-grown maize canopy chlorophyll content diagnostic model. The result showed that the model accuracy was low when the image background was not removed (Rc2 (the determination coefficient of calibration set) = 0.5431, RMSEF (the root mean squared error of forecast) = 4.2184, MAE (the mean absolute error) = 3.24; Rv2 (the determination coefficient of validation set) = 0.5894, RMSEP (the root mean squared error of prediction) = 4.6947, and MAE = 3.36). The diagnostic accuracy of the chlorophyll content could be improved by extracting the maize canopy through the segmentation method, which was based on the wavelet segmentation method. The maize canopy chlorophyll content diagnostic model had the highest accuracy (Rc2 = 0.6638, RMSEF = 3.6211, MAE = 2.89; Rv2 = 0.6923, RMSEP = 3.9067, and MAE = 3.19). The research can provide a feasible method for crop growth and nutrition monitoring on the basis of the UAV platform and has a guiding significance for crop cultivation management.

SPRAY DEPOSITION ON MAIZE USING AN ELECTROSTATIC SPRAYER

ABSTRACT It is a well-known fact that the application of pesticides can be improved using an electrostatic spray due to the reduction of the application rate and increase in deposition on plant targets. However, little information exists on the use of such technology in maize crops. Thus, this study is aimed to evaluate the spray deposition on maize resulting from spray application using an electrostatic sprayer in combination with low application rates and different spray compositions. A field experiment was conducted in a randomized block having a 2×2×2 factorial design with two application rates (95 and 52 L ha-1), with or without the spray electrification, and with or without the synthetic adjuvant. The droplet electrification ability, interference of the electrical conductivity of the spray solution, losses of spray solution to the soil, and spray deposition on maize plants were evaluated for two cases of insecticide applications. The Faraday cage method was used to determine the spray electrification ability of the equipment. The spray deposition on plants and the losses to the soil were analyzed using a tracer (food coloring dye) added to the spray solution that was subsequently detected by spectrophotometry. It was seen that the electrostatic sprayer improved the spray application efficiency, which responded positively to the increase in the electrical conductivity of the spray solution. The deposition of the electrified spray solution on the upper third canopy of maize was found to be higher; the spray solution electrification also reduced the losses to the soil, regardless of the application rate.

Removing top leaves increases yield and nutrient uptake in maize plants

Intraspecific competition for light affects nutrient uptake of maize, especially during the seed filling phase (from the blistering-stage to physiological-maturity). Partial leaf removal only affects the top leaves and improves the light-environment, which could then enhance nutrient uptake during the seed filling phase. However, there is a shortage of quantitative information on the yield effects of such a management measure. A 3-year field trial was conducted to evaluate the impact of different leaf removal treatments (no removal of leaves (D0: control), removal of two leaves (D2), removal of four leaves (D4), and removal of six leaves (D6) from maize-canopy) on total dry matter accumulation, and nitrogen, phosphorus, and potassium uptake at the blistering-stage and physiological-maturity, plus seed number per plant, seed weight, and seed yield at physiological maturity. Compared to D0, at physiological-maturity, D2 significantly increased total dry matter accumulation (by 9%), and uptake of nitrogen (by 5%), phosphorus (by 10%), and potassium (by 4%); while excessive leaf removal treatments considerably reduced dry matter accumulation and nutrient uptake. Importantly, during the seed filling phase of maize, treatment D2 significantly enhanced the uptake of nitrogen, phosphorus, and potassium by 76%, 40%, and 65%, respectively, compared to control. Treatment D2 increased seed number per plant (by 6.4%, from 448 under D0 to 477 in D2) and seed weight (by 5.7%). Relative to control, maize in D2 had 12%, 14%, and 11%, higher seed-yields in 2017, 2018, and 2019, respectively, and it also improved the economic profit when taking into account labor costs. Graphical representation of changes in light transmittance, photosynthesis, nutrient uptake, carbohydrate, and dry matter accumulation in maize plants as affected by different leaf removal treatments. Treatment codes represent no defoliation (D0: control), removal of two leaves (D2), removal of four leaves (D4), and removal of six leaves (D6) from the top of maize canopy. Yellow and green arrows show the light environment and leaf area of maize plants. The black arrows represent the regulating directions of leaf removal treatments on maize growth and development in this paper. The graphical abstract clearly demonstrates the significant improvement of optimum leaf removal treatment (D2) as compared to control (D0). The red and blue arrows show the relevant increase and decrease of the mentioned components between the optimal leaf removal and control.

Dynamics of canopy-dwelling arthropods under different weed management options, including glyphosate, in conventional and genetically modified insect-resistant maize.

The use of genetically modified varieties tolerant to herbicides (HT varieties) and resistant to insects (Bt varieties) in combination with application of a broad-spectrum herbicide such as glyphosate could be an effective option for the simultaneous control of weeds and pests in maize. Nevertheless, the possible impact of these tools on nontarget arthropods still needs to be evaluated. In a field study in central Spain, potential changes in populations of canopy-dwelling arthropods in Bt maize under different weed management options, including glyphosate application, were investigated. Canopy-dwelling arthropods were sampled by visual inspection and yellow sticky traps. The Bt variety had no effect on any group of studied arthropods, except for the expected case of corn borers-the target pests of Bt maize. Regarding the effects of herbicide regimes, the only observed difference was a lower abundance of Cicadellidae and Mymaridae on yellow sticky traps in plots not treated with pre-emergence herbicides. This effect was especially pronounced in a treatment involving two glyphosate applications. The decrease in Cicadellidae and Mymaridae populations was associated with a higher density of weeds in plots, which may have hindered colonization of the crop by leafhoppers. These differences, however, were only significant in the last year of the study. The low likelihood of the use of glyphosate- and herbicide-tolerant varieties for weed control triggering important effects on the nontarget arthropod fauna of the maize canopy is discussed.

Design and assessment of new artificial reference surfaces for real time monitoring of crop water stress index in maize

The crop water stress index (CWSI) is usually used to normalize crop temperature for efficient irrigation management. For CWSI computation, the temperature of a non-transpiring crop (Tdry) and that of a crop transpiring at its potential rate (Twet) are required. Alternatively, the use of artificial reference surfaces (ARS) that mimic Tdry and Twet has been proposed. However, to date, little effort has been made on the development of such surfaces, and there is still much uncertainty concerning what is measured. The aim of this research was to design, develop and evaluate the feasibility of new artificial reference surfaces for real time CWSI computation in maize. Hemispherical cellulose paper-based surfaces were constructed using three shades of green. The paper-based hemispheric surfaces were placed in a 3D-printed plastic structure that allows water storage and the placement of an upward-looking infrared thermometer at the bottom of the ARS. An experimental array comprising 18 randomly located ARS was designed, of which 9 were wet ARS (3 replicates per colour) and 9 were dry ARS (3 replicates per colour). The prototypic ARS were able to accurately determine Tdry and Twet in maize canopies. The CWSI values determined using the ARS developed in this research were validated in a commercial maize breeding trial against leaf gas exchange variables. The shade of green that provided the best results in both cases was the RGB colour R32, G66, B34. While Tdry could be obtained directly from a dry ARS temperature, a solar radiation-based transformation is required to determine Twet from the wet ARS temperature.

Light reacclimatization of lower leaves in C<sub>4 </sub>maize canopies grown at two planting densities

C4 plants have high photosynthetic capacity but are inefficient under low light. In a canopy, lower leaves developed under high light are progressively shaded. To elucidate how lower leaves in a C4 canopy reacclimatize to low light, we investigated maize canopies differing in light environment grown at standard and low planting densities (SPD, LPD). Although upper leaves at SPD and both upper and lower leaves at LPD had light-response curves of photosynthesis of sun leaves, lower leaves at SPD had that of shade leaves. All leaves at both densities had anatomical framework of sun leaves, but the chloroplast content in mesophyll and bundle-sheath cells of lower leaves at SPD was greatly reduced to reacclimatize to low light. This study demonstrates that lower leaves at SPD reacclimatize to low light by adjusting their physiological and chloroplast traits while maintaining anatomical framework, whereas those at LPD behave as sun leaves.

Use of Fluorescence Sensing to Detect Nitrogen and Potassium Variability in Maize

Real-time fluoro-sensing is a promising crop sensing technology to support variable-rate nutrient management for precision agricultural practices. The objective of this study was to evaluate the potential of fluoro-sensing to detect the variability of nitrogen (N) and potassium (K) in the crop canopy at the early growth stages of maize (before the V6 crop growth stage). This study was conducted under greenhouse conditions in pots filled with silica sand, and maize plants were supplied with modified Hoagland’s solution with different rates of N and K. Sensor readings were collected using a Multiplex®3 fluorescence sensor and analyzed using ANOVA (analysis of variance) to test differences in crop response to nutrient rates. Regression analysis was used to assess the ability of fluorescence sensor-based indices to estimate N and K in the crop canopy. The results of this study indicate that all fluorescence indices under consideration enabled the detection of N variability in the maize canopy prior to the V2 crop growth stage. The NBI_B (nitrogen balance index blue) index enabled N uptake detection (R2 = 0.99) as early as the V2 crop growth stage. However, the fluorescence indices failed to identify K deficiency, as the maize plants with K treatments showed little to no variability of this nutrient at early crop growth stages as measured by plant tissue analysis. The results present a tremendous opportunity to assess N uptake at early growth stages of maize for precision nitrogen application. We recommend using fluorescence sensor-based NBI_B or NBI_R (Nitrogen balance index red) for early detection of nitrogen uptake in maize for precision nitrogen management.