Maize Density Research Articles

Above- and Below-Ground Interactions and Interspecific Relationships in Wheat/Maize Systems

Above- and below-ground interactions play a crucial role in achieving higher yields in intercropping systems. Nonetheless, it remains unclear how these interactions impact intercropping crop growth and regulate interspecific relationships. This study aimed to quantify the impact of above- and below-ground interactions on crop yield by determining the dynamics of dry matter accumulation, photosynthetically active radiation (PAR) transmittance, and leaf area index (LAI) in intercropped wheat and maize. Three below-ground intensities were set for an intercropping system: no root separation (CI: complete interaction below ground), 48 μm nylon mesh separation (PI: partial interaction below ground), and 0.12 mm plastic sheet separation (NI: no interaction below ground). Two densities were set for maize: low (45,000 plants hm−2) and high (52,500 plants hm−2). At the same time, corresponding monoculture treatments were established. The grain yields in the CI and PI treatments were, on average, 23.7% and 13.7% higher than those in the NI treatment at high and low maize densities, respectively. Additionally, the grain yield for high density was 12.3% higher than that of low density in the CI treatment. The dry matter accumulation of intercropped wheat under the CI and PI treatments was, on average, 9.1%, 14.5%, and 9.0% higher than that in the NI treatment at the flowering, filling, and maturity stages, respectively. The dry matter accumulation of intercropped maize at the blister, milk, and physiological maturity stages increased by 41.4%, 32.1%, and 27.8%, respectively, under the CI treatment compared to the NI treatment. The PAR transmittance and LAI of maize at the V6 stage were significantly increased by increasing the intensity of below-ground interactions. This study showed that complete below-ground interaction contributed to a significant increase in the competitiveness of intercropped wheat with respect to maize (Awm) under the high-density maize treatment, especially at the filling stage of wheat. Moreover, the CI treatment enhanced the recovery effects of maize (Rm) after wheat harvesting. Increasing the intensity of below-ground interactions can significantly enhance the Awm and Rm in intercropping systems, favoring the accumulation of crop dry matter mass and light energy utilization to increase system yields.

Histological and single-nucleus transcriptome analyses reveal the specialized functions of ligular sclerenchyma cells and key regulators of leaf angle in maize

Leaf angle (LA) is a crucial factor that affects planting density and yield in maize. However, the regulatory mechanisms underlying LA formation remain largely unknown. In this study, we performed a comparative histological analysis of the ligular region across various maize inbred lines and revealed that LA is significantly influenced by a two-step regulatory process involving initial cell elongation followed by subsequent lignification in the ligular adaxial sclerenchyma cells (SCs). Subsequently, we performed both bulk and single-nucleus RNA sequencing, generated a comprehensive transcriptomic atlas of the ligular region, and identified numerous genes enriched in the hypodermal cells that may influence their specialization into SCs. Furthermore, we functionally characterized two genes encoding atypical basic-helix–loop–helix (bHLH) transcription factors, bHLH30 and its homolog bHLH155, which are highly expressed in the elongated adaxial cells. Genetic analyses revealed that bHLH30 and bHLH155 positively regulate LA expansion, and molecular experiments demonstrated their ability to activate the transcription of genes involved in cell elongation and lignification of SCs. These findings highlight the specialized functions of ligular adaxial SCs in LA regulation by restricting further extension of ligular cells and enhancing mechanical strength. The transcriptomic atlas of the ligular region at single-nucleus resolution not only deepens our understanding of LA regulation but also enables identification of numerous potential targets for optimizing plant architecture in modern maize breeding.

Mapping Maize Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology

Maize is a globally important cereal and fodder crop. Accurate monitoring of maize planting densities is vital for informed decision-making by agricultural managers. Compared to traditional manual methods for collecting crop trait parameters, approaches using unmanned aerial vehicle (UAV) remote sensing can enhance the efficiency, minimize personnel costs and biases, and, more importantly, rapidly provide density maps of maize fields. This study involved the following steps: (1) Two UAV remote sensing-based methods were developed for monitoring maize planting densities. These methods are based on (a) ultrahigh-definition imagery combined with object detection (UHDI-OD) and (b) multispectral remote sensing combined with machine learning (Multi-ML) for the monitoring of maize planting densities. (2) The maize planting density measurements, UAV ultrahigh-definition imagery, and multispectral imagery collection were implemented at a maize breeding trial site. Experimental testing and validation were conducted using the proposed maize planting density monitoring methods. (3) An in-depth analysis of the applicability and limitations of both methods was conducted to explore the advantages and disadvantages of the two estimation models. The study revealed the following findings: (1) UHDI-OD can provide highly accurate estimation results for maize densities (R2 = 0.99, RMSE = 0.09 plants/m2). (2) Multi-ML provides accurate maize density estimation results by combining remote sensing vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features (R2 = 0.76, RMSE = 0.67 plants/m2). (3) UHDI-OD exhibits a high sensitivity to image resolution, making it unsuitable for use with UAV remote sensing images with pixel sizes greater than 2 cm. In contrast, Multi-ML is insensitive to image resolution and the model accuracy gradually decreases as the resolution decreases.

Impact of Different Doses of Herbicides on Weed Density and Weed Control Efficiency in Maize

A field experiment was conducted at Chandra Shekhar Azad University of Agriculture and Technology, Kanpur during Kharif season of 2021. To evaluate the effect of different doses of Mesotrione 2.27% w/w + Atrazine 22.7% w/w SC on growth and yield of maize (Zea mays L.). The experiment was laid down in the Randomized block design with ten treatments which was replicated three times. Experimental Field was infested with Echinochloa colona, Digitaria sanguinalis, Dactyloctenium aegyptium, Commelina benghalensis, Trianthema and Cyperus rotundus. Ready-mix herbicide, Mesotrione 2.27% w/w + Atrazine 22.7% w/w SC (Calaris Xtra) manufactured by Syngenta was used in the experiment. Among the herbicidal treatments, the application of Mesotrione 2.27% w/w + Atrazine 22.7% w/w SC @ 1.750 kg a.i. ha-1 recorded the lowest weed density and highest weed control efficiency (approx. 90%) at every growth stage of maize. However, among herbicidal treatments, grain yield of maize was maximum with the application of Mesotrione 2.27% w/w + Atrazine 22.7% w/w SC @ 1.0 kg a.i. ha-1(T4) which might be due do toxicity effect of herbicide.

WGCNA analysis of the effect of exogenous BR on leaf angle of maize mutant lpa1

Leaf angle, as one of the important agronomic traits of maize, can directly affect the planting density of maize, thereby affecting its yield. Here we used the ZmLPA1 gene mutant lpa1 to study maize leaf angle and found that the lpa1 leaf angle changed significantly under exogenous brassinosteroid (BR) treatment compared with WT (inbred line B73). Transcriptome sequencing of WT and lpa1 treated with different concentrations of exogenous BR showed that the differentially expressed genes were upregulated with auxin, cytokinin and brassinosteroid; Genes associated with abscisic acid are down-regulated. The differentially expressed genes in WT and lpa1 by weighted gene co-expression network analysis (WGCNA) yielded two gene modules associated with maize leaf angle change under exogenous BR treatment. The results provide a new theory for the regulation of maize leaf angle by lpa1 and exogenous BR.

ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field

The intelligent acquisition of phenotypic information on male tassels is critical for maize growth and yield assessment. In order to realize accurate detection and density assessment of maize male tassels in complex field environments, this study used a UAV to collect images of maize male tassels under different environmental factors in the experimental field and then constructed and formed the ESG-YOLO detection model based on the YOLOv7 model by using GELU as the activation function instead of the original SiLU and by adding a dual ECA attention mechanism and an SPD-Conv module. And then, through the model to identify and detect the male tassel, the model’s average accuracy reached a mean value (mAP) of 93.1%; compared with the YOLOv7 model, its average accuracy mean value (mAP) is 2.3 percentage points higher. Its low-resolution image and small object target detection is excellent, and it can be more intuitive and fast to obtain the maize male tassel density from automatic identification surveys. It provides an effective method for high-precision and high-efficiency identification of maize male tassel phenotypes in the field, and it has certain application value for maize growth potential, yield, and density assessment.

Effect of Leveling and Tillage Equipment on Soil Bulk Density and Yield of Maize

A field experiment was carried out to evaluate the effect of leveling and tillage equipment on the soil bulk density and the growth and production of maize. Three landscaping types of equipment, namely laser, pelvic, and grader equipment, and three tillage equipment, namely moldboard plow, chisel plow, and no-till farming system. Soil bulk density, practical productivity, plant height, weight of 100 seeds, number of seeds per ear, and the yield of maize were measured. The results can be summarized as follows: The interaction between the pelvic leveling equipment and the no-till farming system was superior in obtaining the lowest soil bulk density, while the interaction between the laser leveling equipment and chisel plow was superior in obtaining the highest plant height, the highest weight of 100 seeds stood 36.70 g, the highest number Seeds per ear stood 371.30 seeds per ear and the highest yield of the plant stood 6.36 tons ha-1 compared to the other interventions.

Coupling phosphate type and placement promotes maize growth and phosphorus uptake by altering root properties and rhizosphere processes

ContextNutrient distribution and phosphate fertilizers can impact maize growth, but how to match the type of phosphate fertilizer and the application method to enhance root-foraging capacity for phosphorus (P) is unclear. ObjectiveThis study was aimed at characterizing the effects of different phosphate fertilizers and placements on maize growth, grain yield, root properties and rhizosphere processes on calcareous soil. MethodsA soil column experiment with five different phosphate fertilizers [single superphosphate (SSP), monoammonium phosphate (MAP), diammonium phosphate (DAP), urea phosphate (UP) and ammonium polyphosphate (APP)] applied either uniformly or locally 10–20 cm deep in a greenhouse. In addition, a 2-year field experiment with five P fertilizer treatments (non-fertilization and broadcast MAP, DAP, UP or APP) was conducted. ResultsIn the soil column experiment, localized application of SSP, MAP, UP and APP significantly promoted the growth and P uptake of maize by modifying the root morphology. Localized application of each P fertilizer increased (by up to 74 %) the first-order lateral root density and first-order lateral root length of maize in the fertilizer-enriched layer, resulting in a significant increase in the efficiency of root P acquisition by maize. In the field experiment, the rhizosphere pH of maize supplied with MAP, UP or APP was reduced by 0.5–0.7 units in comparison to DAP in 2018. Compared with MAP and DAP, application of UP or APP in the field did not show significantly improve maize growth and P uptake. ConclusionsThese results show that optimizing P fertilizer types and placement modified root morphology and physiology, and consequently promoted maize growth and P accumulation, but further characterization of the localized application of different P fertilizers in the field is needed. SignificanceEngineering root properties and rhizosphere processes by manipulating fertilizer types and placement improves fertilizer-use efficiency and reduces environmental footprint of food production.

Potential crop yield gains under intensive soybean/maize intercropping in China

Abstract Aims Intensive soybean/maize intercropping, a specific form of intercropping, holds promise in addressing the challenges posed by increasing food demands, diminishing cropland areas, deteriorating soil quality, and escalating environmental pollution. Methods To evaluate the potential of this system, we conducted a national meta-analysis, quantifying its absolute yield gain (net effect, NE) and land use efficacy (land equivalent ratio, LER). We further investigated the underlying mechanisms by examining local climate, soil properties, and field management practices and then developed random forest (RF) models to assess the system's potential, incorporating current information on natural resources. Results In China, an average NE of 3.2 ± 0.1 Mg ha−1 and LER of 1.4 ± 0.02 were achieved by intensive soybean/maize intercropping. The variance of NE was significantly influenced by air temperature (10%), soybean delay days (8%), and maize plant density (9%). Similarly, the LER was strongly driven by soybean delay days (14%), sunshine hours (11%), and maize density (10%). Notably, this intensive intercropping system efficiently utilizes available resources, such as light, temperature (heat), accumulated temperature, and soil nutrients, particularly in regions characterized by low soil fertility and limited agricultural resources. Ultimately, the RF model estimated substantial overyielding of 2 800 kg per hectare, representing approximately 1.4 times the current soybean and maize production under China's monoculture. Conclusions The implementation of intensive soybean/maize intercropping is highly beneficial throughout China, especially in areas with limited agricultural resources. The Yangtze River Basin, in potentially, emerges as the most suitable region for adopting this intensive intercropping practice.

A multi-parameter control method for maize threshing based on machine learning algorithm optimisation

A complex field environment and differences in maize density cause feed rate fluctuations in the combine. Traditional threshing methods do not adjust multiple operating parameters based on the feed rate, resulting in low threshing performance and high power consumption. A multi-parameter control method for maize threshing based on the feed rate was proposed to overcome these problems. Random forest, support vector machine, and multiple linear regression machine learning algorithms were used to estimate the threshing performance indices for different feed rates. The algorithm (random forest) was used to construct a control model for the rotor speed, threshing gap, and top cover guide vane angle. The performance of the control system was compared with a threshing system with constant parameters to verify the control model's accuracy. The results demonstrated that the threshing unit with the control system outperformed the one without. The broken grain rate and the power consumption were 2.18% and 36.22% lower, respectively, whereas the unthreshed grain rate was not significantly different. Random forest was more suitable than the support vector machine and multiple linear regression models for establishing a multi-parameter control model for maize threshing. The adjustment of multiple threshing parameters significantly improved the operating performance for different feed rates.

Optimizing root system architecture to improve root anchorage strength and nitrogen absorption capacity under high plant density in maize

Optimizing root system architecture to improve root anchorage strength and nitrogen absorption capacity under high plant density in maize

Selection of Weeding Method and Plant Density in Maize (Zea mays L.) Based on Morphological and Physiological Characteristics

Aims: Maize is annual plant of the Poaceae, which, with a relatively short growth period and high yield, is ranked second in the world in terms of production after wheat. Choosing the most suitable different planting densities and weeding methods can have a favorable effect on increasing the yield of corn plants.
 Study Design: To investigate the traits and choose the most appropriate planting density and weeding method in maize (Zea mays L.) using graphic methods, a factorial experiment based on randomized complete block design was conducted in three replications.
 Place and Duration of Study: This study was conducted in Shahre-Rey region in 2020-2021.
 Methodology: The hybrid cultivated in the SC647 experiment was selected. Each test plot included four cultivation lines with a width of 75 cm and a length of 4 meters. The experimental land was fallow the year before maize cultivation, which was prepared by spring plowing and disk operations.
 Results: Based on the analysis of variance, the effect of planting density in all traits except the thousand seed weight trait and the effect of weeding stages in all traits except the trait number of rows in the cob showed a significant difference at the probability level of 0.01. Based on the means comparison of D3W3, D1W3 and D3W2 were selected as suitable treatments. Based on the correlation intensity map between the traits, the highest correlation intensity was estimated for the ear length trait with the plant height and the grain yield trait with the harvest index. The grouping diagram of the treatments formed four groups based on the first and second main components that justified 50.45% of the total variance of the data. Based on the polygon graph, treatments D2W3, D1W3, D3W2, D3W3, D1W1 and D1W2 were identified as the best treatments. Based on the cluster analysis diagram and heat map drawn on the experiment data, the treatments were divided into two main groups.
 Conclusion: In general, by reviewing all the analyzes performed on the experiment data, D3W3 (density of 85,000 plants per hectare and no weeding) and D3W2 (density of 85,000 plants and two stages of weeding 4 to 8 weeks after planting) can be used for maize cultivation.

Natural polymorphisms in ZmIRX15A affect water-use efficiency by modulating stomatal density in maize.

Stomatal density (SD) is closely related to crop drought resistance. Understanding the genetic basis for natural variation in SD may facilitate efforts to improve water-use efficiency. Here, we report a genome-wide association study for SD in maize seedlings, which identified 18 genetic variants that could be resolved to seven candidate genes. A 3-bp insertion variant (InDel1089) in the last exon of Zea mays (Zm) IRX15A (Irregular xylem 15A) had the most significant association with SD and modulated the translation of ZmIRX15A mRNA by affecting its secondary structure. Dysfunction of ZmIRX15A increased SD, leading to an increase in the transpiration rate and CO2 assimilation efficiency. ZmIRX15A encodes a xylan deposition enzyme and its disruption significantly decreased xylan abundance in secondary cell wall composition. Transcriptome analysis revealed a substantial alteration of the expression of genes involved in stomatal complex morphogenesis and drought response in the loss-of-function of ZmIRX15A mutant. Overall, our study provides important genetic insights into the natural variation of leaf SD in maize, and the identified loci or genes can serve as direct targets for enhancing drought resistance in molecular-assisted maize breeding.

Weed seedbank, above‐ground weed community and crop yields under conventional and conservation agriculture practices in maize–wheat–mungbean rotation

AbstractWeeds are the major biological barriers to achieving higher yields in agro‐ecosystem. We aimed to determine weed community (seedbank and aboveground) and crop yield responses to conventional tillage (CT) and different types of conservation agriculture (CA) practices with variable nitrogen (N) doses in a maize (Zea mays L.)–wheat (Triticum aestivum L.)–mungbean (Vigna radiata L.) rotation. After 8 years of implementation, CA practices including zero‐tilled (ZT) permanent narrow‐bed with crop residues and 100% required N (PNB + R + 100 N), and ZT permanent broad‐bed with residues and 100% required N (PBB + R + 100 N) had a lower weed seed density by 45% at 0–7.5 cm and 53% at 7.5–15 cm soil layers than CT. CT promoted dominance of Cyperus rotundus L., Cyperus esculentus L. and Dinebra retroflexa (Vahl) Panz. while, specific CA practices (PNB + R + 100 N and PBB + R + 100 N) promoted dominance of Digera arvensis Forsk. and Melilotus indicus (L.). Compared to PNB + R + 100 N and PBB + R + 100 N, CT seedbanks had 41–46% higher ecological dominance, which is an indicator that delineates dominance of few specific weed species. The PBB + R + 100 N also had a lower total above‐ground weed density in maize–wheat–mungbean rotation by 34% (mean of years) than CT. The PBB + R + 100 N had 31.1% higher system productivity over CT. Thus, PBB + R + 100 N could reduce the weed seed density in the soil and above‐ground weeds and increase crop yields for sustainable crop production in maize–wheat–mungbean rotations.

Effect of plant population density of black maize on plant growth, baby-corn yield, and biomass

The aim of the study was to determine the effect of black maize plant population density on plant growth, baby-corn yield, and biomass. The research was carried out from September to November 2021 on the plot of the Merang Mushroom Center and Integrated Farming of Lestari Makmur, Kepuhan Hamlet, Argorejo, Sedayu, Bantul, Yogyakarta with vertisol soil type and an altitude of 87.5 meters above sea level. The plant population density implemented in the number of plants per hole, namely 1, 2, 3, and 4 plants per hole, was tested using Completely Randomized Block Design with 3 replications. Each experimental unit measuring 3m × 3.5m with a plant spacing of 75cm × 25cm. Maintenance of experimental plants following the standard recommendations for corn cultivation. Cow manure 15 tons ha−1 was sown before planting, while NPK fertilizer of 300 kg ha was given 2 times on 2 and 4 weeks after planting, by making a whole, put fertilizer in, then covered with soil again. Detasseling was done before the tassels bloom. Baby corn was harvested up to maximum of 6 days after the ears appear. Biomass was harvested by cutting the plant stand after all baby-corn had been harvested. The observed variables were plant height (cm), number of leaves (strands), stem diameter (mm), days to tasseling (days after planting), fresh and dry weight of plant (g), weight and diameter of ears, baby corn yield ha−1, and biomass yield ha−1. Data collected were analized by analysis of variance and DMRT at α= 5%. The results showed that the population density of black maize had an effect on stem diameter, plant fresh weight, baby-corn yield, and biomass yield per hectare. The largest stem diameter at a population density of 1 plant/hole. The yield of baby-corn per hectare was highest at a density of 3 or 4 plants per hole, and the highest biomass yield per hectare was at a density of 4 plants per hole. The weight, length, and diameter of baby-corn did not differ at various population densities.

Alfalfa stand age at termination influences soil properties, root characteristics, and subsequent maize yield

Alfalfa (Medicago sativa L.)–maize (Zea mays L.) rotation is an important cropping practice to improve crop production and soil fertility, but the effects of the alfalfa stand age on the performances of the following maize remain unexplored. It is not clear how different alfalfa stand ages at termination influence soil properties, root characteristics, and yield of subsequent maize. The aim of this study was to evaluate how alfalfa influences soil characteristics, which in turn influence the root and aboveground growth and grain yield of the following maize crop. A field experiment from 2014 to 2019 was conducted at the Lishu Experimental Station, Jilin Province, China. The field treatments included six years of continuous maize (CM), six years of continuous alfalfa (CA), three years of alfalfa followed by two years of maize (3A2M), four years of alfalfa followed by two years of maize (4A2M), and five years of alfalfa followed by one year of maize (5A1M). Only the yield for the first-year maize after alfalfa in the 3A2M, 4A2M, and 5A1M treatments significantly increased by 8 %, 23 %, and 13 %, respectively. Root biomass (RB) of CA treatment gradually increased from 2014, reaching its maximum value of 5866 kg ha−1 in 2019, which was 35 % greater than the RB accumulation under CM in the 0–60 cm soil layer. Compared with the CM treatment, 3A2M and 4A2M treatments increased RB, while 3A2M, 4A2M, and 5A2M increased root length density (RLD), root surface area density (RSAD), and root volume density (RVD) of first-year maize after alfalfa. The 4A2M treatment also increased RLD, RSAD, and RVD in the 0–60 cm soil layer of second-year maize and reduced the soil bulk density (BD) in the 0–20 cm soil layer of first-year maize after alfalfa. Only 4A2M and 5A1M treatments significantly increased soil Olsen-P and soil organic carbon by first-year maize after alfalfa. The redundancy analysis results showed that the soil water content in the critical period and soil Olsen-P were the primary factors, which explained 33.8 % and 13.0 % of variation in maize root characteristics (P < 0.01), respectively. Structural equation modeling indicated that sowing alfalfa influenced the subsequent maize yield by influencing the soil Olsen-P, soil mineral nitrogen before sowing, and root characteristics. Therefore, keeping alfalfa for at least four years should be the recommended practice owing to its ability to improve the soil properties, root growth, and subsequent maize yield.

Grain yield and N uptake of maize in response to increased plant density under reduced water and nitrogen supply conditions

The development of modern agriculture requires the reduction of water and chemical N fertilizer inputs. Increasing the planting density can maintain higher yields, but also consumes more of these restrictive resources. However, whether an increased maize density can compensate for the negative effects of reduced water and N supply on grain yield and N uptake in the arid irrigated areas remains unknown. This study is part of a long-term positioning trial that started in 2016. A split-split plot field experiment of maize was implemented in the arid irrigated area of northwestern China in 2020 to 2021. The treatments included two irrigation levels: local conventional irrigation reduced by 20% (W1, 3,240 m3 ha–1) and local conventional irrigation (W2, 4,050 m3 ha–1); two N application rates: local conventional N reduced by 25% (N1, 270 kg ha–1) and local conventional N (360 kg ha–1); and three planting densities: local conventional density (D1, 75,000 plants ha–1), density increased by 30% (D2, 97,500 plants ha–1), and density increased by 60% (D3, 120,000 plants ha–1). Our results showed that the grain yield and aboveground N accumulation of maize were lower under the reduced water and N inputs, but increasing the maize density by 30% can compensate for the reductions of grain yield and aboveground N accumulation caused by the reduced water and N supply. When water was reduced while the N application rate remained unchanged, increasing the planting density by 30% enhanced grain yield by 13.9% and aboveground N accumulation by 15.3%. Under reduced water and N inputs, increasing the maize density by 30% enhanced N uptake efficiency and N partial factor productivity, and it also compensated for the N harvest index and N metabolic related enzyme activities. Compared with W2N2D1, the N uptake efficiency and N partial factor productivity increased by 28.6 and 17.6% under W1N1D2. W1N2D2 had 8.4% higher N uptake efficiency and 13.9% higher N partial factor productivity than W2N2D1. W1N2D2 improved urease activity and nitrate reductase activity by 5.4% at the R2 (blister) stage and 19.6% at the V6 (6th leaf) stage, and increased net income and the benefit:cost ratio by 22.1 and 16.7%, respectively. W1N1D2 and W1N2D2 reduced the nitrate nitrogen and ammoniacal nitrogen contents at the R6 stage in the 40–100 cm soil layer, compared with W2N2D1. In summary, increasing the planting density by 30% can compensate for the loss of grain yield and aboveground N accumulation under reduced water and N inputs. Meanwhile, increasing the maize density by 30% improved grain yield and aboveground N accumulation when water was reduced by 20% while the N application rate remained constant in arid irrigation areas.

Evaluating the Canopy Chlorophyll Density of Maize at the Whole Growth Stage Based on Multi-Scale UAV Image Feature Fusion and Machine Learning Methods

Maize is one of the main grain reserve crops, which directly affects the food security of the country. It is extremely important to evaluate the growth status of maize in a timely and accurate manner. Canopy Chlorophyll Density (CCD) is closely related to crop health status. A timely and accurate estimation of CCD is helpful for managers to take measures to avoid yield loss. Thus, many methods have been developed to estimate CCD with remote sensing data. However, the relationship between the CCD and the features used in these CCD estimation methods at different growth stages is unclear. In addition, the CCD was directly estimated from remote sensing data in most previous studies. If the CCD can be accurately estimated from the estimation results of Leaf Chlorophyll Density (LCD) and Leaf Area Index (LAI) remains to be explored. In this study, Random Forest (RF), Support Vector Machines (SVM), and Multivariable Linear Regression (MLR) were used to develop CCD, LCD, and LAI estimation models by integrating multiple features derived from unmanned aerial vehicle (UAV) multispectral images. Firstly, the performances of the RF, SVM, and MLR trained over spectral features (including vegetation indices and band reflectance; dataset I), texture features (dataset II), wavelet coefficient features (dataset III), and multiple features (dataset IV, including all the above datasets) were analyzed, respectively. Secondly, the CCDP was calculated from the estimated LCD and estimated LAI, and then the CCD was estimated based on multiple features and the CCDP was compared. The results show that the correlation between CCD and different features is significantly different at every growth stage. The RF model trained over dataset IV yielded the best performance for the estimation of LCD, LAI, and CCD (R2 values were 0.91, 0.97, and 0.97, and RMSE values were 6.59 μg/cm2, 0.35, and 24.85 μg/cm2). The CCD directly estimated from dataset IV is slightly closer to the ground truth CCD than the CCDP (R2 = 0.96, RMSE = 26.85 μg/cm2) calculated from LCD and LAI. The results indicated that the CCD of maize can be accurately estimated from multiple multispectral image features at the whole growth stage, and both CCD estimation strategies can be used to estimate the CCD accurately. This study provides a new reference for accurate CCD evaluation in precision agriculture.

Increasing Maize Production and Advancing Rational Water Allocation and Usage Based on the Optimal Planting Density and Irrigation Levels in Northwest China

Increasing water-use efficiency by optimizing planting density and irrigation regimes in maize is crucial for food security under limited water resources. In this study, six plant densities (6.0, 7.5, 9.0, 10.5, 12.0, and 13.5 × 104 plants ha−1) and three irrigation amounts (300, 450, and 600 mm) were assessed to analyze the effects of planting density and irrigation amount on the yield and yield components. We also explored the combination of maize production capacity and maximizing economic returns in a planting region. The results demonstrated that as planting density increased, grain yield first increased and then decreased. The optimum planting density was 9 × 104 plants ha−1 when the irrigation amount was 300 mm, and 10.5 × 104 plants ha−1 under both 450 mm and 600 mm irrigation amounts. The relationship between planting density, irrigation amount, planting area, production capacity, and economic return showed that a current production capacity with 6.75 × 104 plants ha−1, 600 mm, and 1.066 million ha, could be achieved with 10.5 × 104 plants ha−1, 344 mm, and 1.1 million ha. The water-use efficiency of irrigation was increased by 74%. Current returns could be achieved with 10.5 × 104 plants ha−1, 405 mm, and 1.1 million ha, or by 9.0 × 104 plants ha−1, 449 mm, and 1.1 million ha. These observations demonstrated that appropriately increasing the planting area and reducing the irrigation amount per hectare under an optimal planting density could achieve a greater economic return and water-use efficiency than either increasing the planting density or reducing the irrigation volume alone in North Xinjiang. We concluded that concurrent optimization of the maize planting density, irrigation amount and plant area will not only meet the demand of food security but also achieve a rational use of water resources and farmland.

The effects of plant density and nitrogen fertilization on maize yield and soil microbial communities in the black soil region of Northeast China

To obtain high maize yield (Zea mays L.), nitrogen (N) fertilizer is widely used across the world and has greatly altered soil microbial communities and influenced soil health. Increasing plant density is an effective strategy for increasing maize yield, while variations in soil microbial communities in response to plant density under high N levels have not been well-studied. In Northeast China, maize was grown at low (LD, 67,500 plants ha−1) and high (HD, 90,000 plants ha−1) densities, combined with three N application rates of 0, 200, and 400 kg N ha−1yr−1(N0, N200 and N400). Based on a six-year field experiment, key soil microbial characteristics and physicochemical properties of top soils (0–20 cm), as well as yield and vegetative parameters were examined. Compared with that of LD, the maize grain yield of HD increased 10.8 % across N application rates from 2012 to 2017 (P < 0.05), while no significant differences between N200 and N400 were observed. HD significantly increased microbial biomass carbon (MBC), microbial biomass N (MBN), and bacterial and fungal diversity at N400 (P < 0.05). Dominant bacterial phyla across all samples were Proteobacteria, Acidobacteria, Actinobacteria and Thaumarchaeota, and fungal phyla were Ascomycota, Basidiomycota and Zygomycota. Species composition of HD shared more similarity between N200 and N400 than that of LD. HD significantly increased the abundance of Nitrososphaera and reduced the abundance of Pseudomonas and Sphingobium. From LD to HD, ammonia-oxidizing archaea (AOA) gene abundance increased while nirK gene abundance decreased at all N application rates, and ammonia oxidizing bacteria (AOB) and nirS gene abundances decreased at N0 and N400 but increased at N200 (P < 0.05). Shoot biomass and N uptake were positively correlated with MBC and MBN but negatively correlated with microbial community diversity. HD directly increased root biomass and N uptake, then reduced soil N contents (NH4+-N, NO3–-N, and TN) and thus positively regulated soil microbial communities. Relative differences of diversity indexes and functional gene abundances between N application rates of HD were significantly lower than those of LD. Overall, our findings indicate that higher plant density of maize could mitigate the adverse effects of N fertilizer overuse on soil microbial communities, thus reconciling maize productivity and black soil health in Northeast China.