Optimum Plant Density Research Articles

Optimizing maize planting density based on soil organic matter to achieve synergistic improvements of yield, economic benefits, and resource use efficiency

With the development of the economy, the contradiction between population, resources, and the environment has become more and more prominent. How to make full use of limited cultivated land resources to increase food production while reducing damage to the environment is an important issue facing agricultural production. Maize plays an essential role in ensuring global food security. Furthermore, planting density is a key agronomic factor affecting maize yield. Although soil organic matter (SOM) is an important indicator of soil fertility. Whether there are different agronomic optimal planting densities of maize under varying SOM contents remains unknown. Furthermore, there is limited understanding on whether optimizing maize planting density based on SOM further improves grain yield and resource use efficiency. Therefore, this study investigates the influence of SOM and planting density on maize grain yield. We also determine the relationship between SOM and agronomic optimal planting density (AOPD) and compare the grain yield, economic benefits, and resource use efficiency of sowing under uniform conventional planting density (SUD) versus optimized planting density based on SOM (SOD). The results showed that AOPD and its corresponding yield increased linearly with the increase in SOM. Compared with SUD, the yield of the two experimental sites under SOD increased by 2.3 % and 5.5 %, respectively, and the economic benefits increased by 0.5 % and 4.9 %, respectively. The average energy use efficiency, energy mass productivity, and energy economic productivity of the two experimental sites under SOD were all higher than those of SUD. These results demonstrate that it is theoretically feasible to optimize maize planting density based on the spatial heterogeneity of SOM. SOD is a potentially sustainable maize production method that can fully utilize the resources of cultivated land to increase grain yield and economic benefits.

Leaf shape, planting density, and nitrogen application affect soybean yield by changing direct and diffuse light distribution in the canopy

When attempting to maximize the crop yield from field-grown soybean (Glycine max (L.) Merr.) by means of improving the light conditions for photosynthesis in the canopy, it is crucial to find the optimal planting density and nitrogen application rate. The soybean plants that were the subject of our experiment were cultivated in N-dense mutual pairs, and included two cultivars with different leaf shapes; one cultivar sported ovate leaves (O-type) and the other lanceolate leaves (L-type). We analyzed the results quantitatively to determine the amount of spatial variation in light distribution and photosynthetic efficiency across the canopy, and to gauge the effect of the experimental parameters on the yield as well as the photosynthetic light and nitrogen use efficiency of the crop. Results indicate that the different leaf shapes were responsible for significant disparities between the photosynthetic utilization of direct and diffuse light. As the nitrogen fertilizer rate and the planting density increased, the soybean plants responded by adjusting leaf morphology in order to maximize the canopy apparent photosynthetic light use efficiency, which in turn affected the leaf nitrogen distribution in the canopy. Despite the fact that the light interception rate of the canopy of the L-type cultivar was lower than that of the canopy of the O-type cultivar, we found its canopy apparent photosynthetic nitrogen and light use efficiency were higher. It was interesting to note, however, that the nitrogen and light use efficiency contributions associated with exposure to diffuse light were greater for the latter than for the former.

Growth , yield and quality of medicinal plant Chia (Salvia hispanica) as influenced by planting methods and density

An experiment was conducted at Central Research Farm, Gayeshpur, Nadia under BCKV, West Bengal to assess the impact of plant density and planting methods on growth, yield and quality attributes of chia (Salvia hispanica L.) grown during winter (rabi) seasons of 2019–20 and 2020–21. Treatments were distributed in split-plot design, with 2 establishment methods (M1, direct sowing and M2, Transplanting) in main plots and 4 planting density (S1, 60 cm × 50 cm = 33,333 plants/ha; S2, 50 cm × 50 cm = 40,000 plants/ha; S3, 50 cm × 25 cm = 80,000 plants/ha; S4, 50 cm × 20 cm = 1,00,000 plants/ha) in sub-plots. Crop under direct sowing with closer spacing (50 cm × 20 cm) had the greater number of primary branch (15.7/plant), primary branch length (38.4 cm) and main inflorescence length (25.9 cm), thereby reflecting superiority of this treatment combination over others. The same treatment combination produced significantly the highest grain (671.7 kg/ha), stem (1109.4 kg/ha) and husk yield (375.2 kg/ha), which led to increase harvest index (31.2). The greatest economic response in terms of gross return (228.4 × 103 ₹/ha), net return (166.3 × 103 ₹/ha) and B: C ratio (3.67) was recorded in crop under the same treatment combination. Based on quadratic regression equation, the optimum plant density of chia was calculated as 71,428 plants/ha. Thus, direct seed sowing at a density of 71,428 plants/ha (spacing ~ 55 cm × 25 cm) can be done for chia cultivation during rabi season in West Bengal condition.

CPDOS: A Web-Based AI Platform to Optimize Crop Planting Density

Increasing crop yield is a significant objective in modern agriculture, with adjusted planting density and rational fertilization strategies standing out as the foremost approaches for attaining such a goal. Through the use of modern artificial intelligence techniques such as genetic algorithms and neural networks, the CPDOS (Crop Planting Density Optimization System), an online intelligent system that can automate the modeling, optimization, and analysis of the two models, was developed in the present study. The goal of the system is to optimize the planting density model and fertilizer application in combination with other computer system development techniques. The CPDOS comprises three main modules: yield density optimization module, optimal planting density range module, and fertilization and planting density optimization module. The three modules are complemented by two modules for data input and result visualization, culminating in the comprehensive process of optimizing planting density and fertilizer allocation through the CPDOS. The CPDOS was tested using potato, corn, and soybean data, and the results show that the optimization effects of planting density and fertilizer application were satisfactory. The CPDOS is an automated crop planting optimization system that integrates algorithms and models and is driven by artificial intelligence technology. The introduction of the CPDOS reduces the barriers to utilizing these algorithms and models, facilitating wider adoption of intelligently optimized planting technology. The platform’s launch will accelerate the swift advancement of this field.

Optimal planting density combined with phosphorus input promotes common buckwheat resource use efficiency and productivity to increase grain yield

Optimal planting density combined with phosphorus input promotes common buckwheat resource use efficiency and productivity to increase grain yield

Determinants of growth and carbon accumulation of common plantation tree species in the three northern regions, China: Responses to climate and management strategies

The Three-North (Northwest, North and Northeast) Shelter Forests Program (TNSFP) in China has effectively promoted vegetation growth and carbon sink in the temperate semi-humid and semi-arid regions. To compare the afforestation benefits of commonly used tree species in the area and explore the effect of environment on growth and carbon accumulation in plantations, backpack LiDAR was used to acquire 3 dimensional lidar point clouds of forests from a total of 480 pure plantation patches consisting of Pinus sylvestris (P.s.), Pinus tabuliformis (P.t.), Populus spp. (Pop.), and Robinia pseudoacacia (R.p.). Then, diameter at breast height (DBH), forest height, canopy coverage, and aboveground carbon accumulation were calculated for each plantation patches, which ranged from 7.0 to 37.3 cm, 1.5–14.5 m, 10–99 % and 4.2–205.9 Mg/ha, respectively. Generalized linear mixed-effect models and ANOVA were applied to account for the environmental constraints on the variations of forest parameters. Results showed that precipitation had a stronger effect on all the above parameters of plantations than temperature, and P.t. was more sensitive to climate than other three species. With regard to forest management in Pop. plantations, thinning could improve afforestation efficiency because carbon accumulation would reduce after the age exceeds 30 years. In contrast, P.s. populations maintained a continuous increase in carbon accumulation at least before 40 years old, while the radial growth of canopy became saturated after 12 years of age. The optimal planting density for P.s. and Pop. are about 1000 trees/ha, beyond which the increase in carbon accumulation will slow down or change rate of canopy coverage will be insignificant. Within the TNSFP area, P.t. and R.p. plantations would be more suitable in southern regions, while P.s. and Pop. plantations grow better in the northeastern regions. Meanwhile, mountains along the “Hu Line” showed high potential for growth and carbon accumulation for all tree species examined.

Maize stover transfers from maize fields to banana-based agroforestry homegardens and the corresponding nutrient flows in central-northern Tanzania

Maize (Zea mays) stover produced in the drier lowland areas on the volcanic foot slopes of Mount Kilimanjaro serves as an important additional fodder for smallholder zero-grazed dairy cattle farming in the humid highland areas of the region. The obtained cattle manure is used to replenish nutrients and manage soil fertility in banana-based agroforestry homegardens of the highlands, which contributes significantly to sustaining crop production, thus food security and the livelihoods of the community. The study aims to provide a preliminary evaluation of the amounts of stover removed from smallholder maize-based fields in the lowlands and the subsequent nutrient flows to the banana-based agroforestry homegardens of the highlands. In 54 smallholder maize fields selected at random from 11 villages in the Hai and Siha districts of the Kilimanjaro region, we conducted an agronomic survey to record the types and amounts of fertilizer used, plant spacing at harvest, and maize stover biomass yield. Most of the surveyed maize-based fields received inadequate nutrient inputs, which were limited to mineral nitrogen alone if any. Of the surveyed fields, 16.7% received recommended amounts of 74 kg N ha–1 by the Ministry of Agriculture, 46.3% sub-optimal amounts and 37.0% did not receive fertilizer at all. Plant density was only 50% of the recommended population density. While this was only a preliminary study on nutrient flows between maize and banana-based agroecosystems, our findings demonstrated that potassium exports via harvested banana bunches exceeded the inputs in the homegardens. We conclude that (i) soil fertility management techniques, preferably integrated soil fertility management, suitable for small-scale maize production in the lowlands should be formulated while optimizing plant density to increase production, (ii) maize-based agroforestry farming system adapted to drier conditions to deal with windstorm and soil erosion problems in the lowlands is urgently needed, and (iii) wider and more detailed research on nutrient flows between maize and banana-based agroecosystems should be conducted to document their respective soil nutrient balances and the sustainability of such practice.

Analisis Pertumbuhan dan Produktivitas Kelapa Sawit pada Variasi Kerapatan Tanam

In oil palm growth, planting density has a sustainable impact on its productivity. To enhance the long-term growth and production yield of oil palm, it is crucial to consider the establishment of appropriate planting densities during the initial planting phase. This research explores the relationship between planting density and productivity, highlighting the importance of determining optimal plant densities for optimal yields. The study was conducted at the Muara Tawang Estate (MTNE) in Kapuas Hulu Regency, West Kalimantan Province. Two blocks of oil palm land with three different planting densities (130, 136, and 143 plants per hectare) were observed to understand the relationship between planting density and agronomic characteristics. Measurements were taken on plant height, frond length, petiole width, number of male and female flowers, and sex ratio. The analysis unveiled consistent trends in oil palm production, which varied based on planting density. Planting densities of 130 and 136 showed superior production trends compared to 143, with 136 demonstrating production increases ranging from 19.8% to 29.8% relative to density 143, alongside enhancements in plant height and frond length by 4.6% and 5.4%, respectively. Additionally, petiole thickness and width were notably lower in density 143, experiencing reductions of 8.0% and 11.0%, respectively, compared to density 136, while the sex ratio exhibited a similar pattern, declining by 34.0% in density 143 compared to 136. This highlights the importance of determining appropriate planting densities to enhance oil palm productivity.

A probabilistic framework for forecasting maize yield response to agricultural inputs with sub-seasonal climate predictions

Crop yield results from the complex interaction between genotype, management, and environment. While farmers have control over what genotype to plant and how to manage it, their decisions are often sub-optimal due to climate variability. Sub-seasonal climate predictions embrace the great potential to improve risk analysis and decision-making. However, adequate frameworks integrating future weather uncertainty to predict crop outcomes are lacking. Maize (Zea mays L.) yields are highly sensitive to weather anomalies, and very responsive to plant density (plants m−2). Thus, economic optimal plat density is conditional to the seasonal weather conditions and can be anticipated with seasonal prospects. The aims of this study were to (i) design a model that describes the yield-to-plant density relationship (herein termed as yield–density) as a function of weather variables, and provides probabilistic forecasts for the economic optimum plant density (EOPD), and (ii) analyze the model predictive performance and sources of uncertainty. We present a novel approach to enable decision-making in agriculture using sub-seasonal climate predictions and Bayesian modeling. This model may inform crop management recommendations and accounts for various sources of uncertainty. A Bayesian hierarchical shrinkage model was fitted to the response of maize yield–density trials performed during the 2010–2019 period across seven states in the United States, identifying the relative importance of key weather, crop, and soil variables. Tercile forecasts of precipitation and temperature from the International Research Institute were used to forecast EOPD before the start of the season. The variables with the greatest influence on the yield–density relationship were weather anomalies, especially those variables indicating months with above-normal temperatures. Improvements on climate forecasting may also improve forecasts on yield responses to management, as we found reduced bias and error (by a factor >10), and greater precision (e.g. R2 increased from 0.26 to 0.32) for cases where weather forecasts matched observations. This study may contribute to the development of decision-support tools that can trigger discussions between farmers and consultants about management strategies and their associated risks.

Enhancing Rice Yield and Nitrogen Utilization Efficiency through Optimal Planting Density and Reduced Nitrogen Rates

Rice yields can only be achieved by selecting a high-quality population. Nitrogen rates and transplanting density play a significant role in determining population quality and yield. Field experiments were conducted in Heshan District, Hunan Province, China, to explore suitable nitrogen rates and appropriate transplanting densities for rice production in 2021 and 2022. In this study, three levels of nitrogen, e.g., N1, N2, and N3 (0, 150, and 210) kg ha−1, and three transplanting densities, e.g., B1, B2, and B3 (1, 2, and 3) seedlings per hill were used to study their individual or combined effects on rice (Taiyou 390) population quality, yield, and yield components, nitrogen-related enzyme activities, and nitrogen absorption and utilization efficiency. The results showed that N2B3 had the highest yield, with an average yield of 9.30 t ha−1 in two years, which was 3.7~49.6% higher than other treatments. This increase was attributed to higher dry matter accumulation (1538.22 g m−2) and effective panicle number (435.2 × 104 ha−1), influenced by increased nitrate reductase activity at the booting stage and glutamine synthase at the heading stage, along with maintenance of higher SPAD value and leaf area index. Nitrogen rates and transplanting densities significantly affected nitrogen use efficiency, with the contribution rate of nitrogen fertilizer to yield decreasing as nitrogen rates increased. However, N2B3 improved nitrogen use efficiency and stabilized rice yield by reducing nitrogen fertilizer application. This study suggested that N2B3 treatment could enhance rice yield by improving plant nitrogen use efficiency under low nitrogen supplementation.

Economic efficiency of seedling method when growing St. John’s wort (Hypericum perforatum L.) under drip irrigation

One of the most popular medicinal plants is St. John’s wort (Hypericum perforatum L.). The main source of obtaining its raw materials is collecting in natural groups. Quite often, the amount of St. John’s wort collected does not satisfy the demand in the domestic market of the country, besides, there are cases that it does not contain a sufficient amount of biologically active substances. Research in recent years shows the high prospects of using medicines made on the basis of St. John’s wort as a powerful antiviral agent, especially in the treatment of COVID19. Therefore, industrial cultivation of this crop to increase the raw material base is relevant. Research on improving the technology of growing St. John’s wort was carried out during 2019–2020 at the Research Station of Medicinal Plants of the IAEM of the National Academy of Sciences. The purpose of the research was to determine the effect of plant planting density on the productivity of St. John’s wort under drip irrigation and perform calculations to determine the optimal plant planting density from an economic point of view. Studies have shown that with an increase in the density of planting plants, the yield of the crop also increased. The growth of the harvest of raw material — dry grass, correspondingly increased the costs of its production. The sum of all expenses in the first year of cultivation ranged from UAH 192757/ha — in the variant with a density of 42 thousand plants/ha, to UAH 277289/ha — in the variant with the highest studied St. John’s wort planting density — 167 thousand plants/ha. From an economic point of view, the most profitable in the first year is the cultivation of St. John’s wort with a planting density of 42–56 thousand plants/ha, a further increase in the planting density of plants reduces the profit, while the profit was UAH 79043–85404/ha, the cost of the raw materials received 63803–73747 UAH/t, with a production profitability of 41.0–41.1%. Analysis of the structure of costs for growing and primary processing of raw materials revealed the factors that most significantly affect the cost of the obtained products. When growing St. John’s wort, the most expensive item was the payment of labor, as in the production process, manual labor was used to care for the crops, maintain the irrigation system, dry the received raw materials, and other operations. Labor costs amounted to UAH 71550–77300/ha. In the second year of St. John’s wort vegetation, the cost of growing the crop decreased compared to the first year of vegetation, and the yield of dry raw materials increased, as a result, the economic indicators were significantly higher. The amount of costs was from UAH 152810 to UAH 160364/ha, depending on the density of planting. The profit was from 175690 to 196036 hryvnias/ha, the profitability of production was from 115.0 to 122.2%. The cost of one ton of dry raw materials ranged from UAH 40496 to UAH 41866. An essential item of expenses in the second year of growing St. John’s wort remains the payment of labor, with an increase in St. John’s wort productivity, it increased, in the version with a planting density of 42 thousand plants/ha with a crop yield of 3.5 t/ha of dry raw materials, it amounted to UAH 60950/ha. The highest labor costs of UAH 64955/ha were in the variant with a planting density of 83 thousand plants/ha, where the highest yield of dry raw materials was 3.96 t/ha. From an economic point of view, in the second year of vegetation, the most profitable is the cultivation of St. John’s wort with a density of 83 thousand plants/ha, for which the highest economic indicators were obtained. The high efficiency of the seedling method of growing St. John’s wort under drip irrigation conditions has been proven.

Influence of plant density on productivity and quality indicators of sunflower oilseeds

In the agricultural technology of sunflower cultivation, plant density in crops is one of the main conditions that determine its further productivity [5]. The optimal sowing density of sunflower can vary widely and depend on various factors, such as the varietal characteristics of plant, the soil-climatic zone of its cultivation, specific weather conditions and, above all, the moisture supply of plants, especially in the critical phases of sunflower development (flowering and grain filling) [ 8]. It has been proved that the optimal plant density in sunflower crops is such that not only normal growth processes and development of each plant are ensured, but the maximum possible productivity per unit of sown area is also formed. In this regard, in 2018–2020, in the flat zone on the leached chernozems of LLC «Zarya» of the Shovgenovsky district of the Republic of Adygea, studies were carried out (according to B.A. Dospekhov «Methodology of field experience») in order to study the reaction of new promising sunflower hybrids (Gorstar, Natali, Iren, selection of Federal State Budgetary Scientific Institution «FSC «VNIIMK named after V.S. Pustovoit», Krasnodar) to change the number of plants per hectare. As a result of the research, the degree of influence of the studied factor on the formation of productive qualities of the studied sunflower hybrids has been established– the diameter of the anthode, the number of oil seeds in it, the mass of achenes from one anthode, the weight of 1000 achenes, the fulfillment of achenes, seed yield, total collection of oil from one hectare area). The studies have shown that the maximum seed yield has been achieved in the hybrids Gorstar and Iren, formed on the second variant (50 thousand pieces/ha). In the Natalie hybrid, the maximum yield was recorded in the variant with a plant density of 60 thousand pieces/ha. From this, it should be noted that with the option of 50 thousand pieces/ha, the Natali hybrid turned out to be 0.62–0.61 t/ha less productive than the Gorstar hybrid and 0.52–0.56 t/ha than the Irene hybrid.The studies were carried out on the equipment of the «Expert» Common Use Center of FSBEI HE «MSTU».

A non-replicated experimental design in faba bean (Vicia faba L.) variety testing – Embracing plant density variation as a strength

Plant density is one of several factors that can affect crop yield. In conventional variety trials, it is generally assumed that the plant density is optimal and similar across all varieties, but in practice, field emergence and plant density may vary among varieties. In this study, we want to investigate an alternative experimental design that may help to reduce the part of the genotype x environment interactions caused by heterogeneity in plant density among varieties. We investigate the use of a non-replicated quantitative design adapted towards regression of yield on plant density by intentionally inducing variation in plant density on the cost of repetitions, and compare it to a conventional ANOVA-focused block design. Based on two field experiments and a simulation study, we show that the estimated yield accuracy is comparable between the two designs under the condition that plant density is similar across varieties. If plant density varies, the quantitative design provides the opportunity for crop yield adjustment. Furthermore, the quantitative design allows estimation of the yield potential, the optimal plant density, and the sensitivity to low plant densities for each variety. Across experiments, varieties, and years, the yield potential was 2.18 t/ha - 7.82 t/ha, the economically optimal net yield was 2.75 t/ha - 6.62 t/ha, and the economically optimal plant density was 39.8–67.7 plants m−2. The study demonstrates that the quantitative design provides added value compared to ordinary qualitative designs, by offering additional information such as the yield potential, optimal plant density,flowering and sensitivity measures.

Response of Grain Yield to Planting Density and Maize Hybrid Selection in High Latitude China—A Multisource Data Analysis

Identifying the relationships between the yield of rainfed maize and planting densities as well as the hybrids used is crucial for ensuring the sustainable development of the grain industry in high latitude China. In this study, we collected 108 grain yield date points from our multiyear (2017–2020) field experiments and combined 213 data points collected from 21 published papers to appraise the impact of planting density and hybrids on maize yield. It was found that grain yield forms a curvilinear relationship with plant density as it increased from 22,500 to 112,500 plants ha−1. The optimum plant density (OPD) was determined to be 72500 plants ha−1, with a maximum maize grain yield of 10.56 Mg ha−1. The interannual variability in grain yields among hybrids with different planting densities was mainly due to the differences in dry matter (DM), especially post-silking. Grain yields increased significantly with a rise in the proportion of post-silking DM to DM at maturity. In addition, both the collected literature and our field experiments showed that the OPD was positively correlated with solar radiation accumulated during the maize growth period and with each hybrid’s year of release. This study suggests that increasing plant density and selecting new hybrids with suitable growth periods are effective approaches for increasing grain yield in high latitude China.

China can be self-sufficient in maize production by 2030 with optimal crop management

Population growth and economic development in China has increased the demand for food and animal feed, raising questions regarding China’s future maize production self-sufficiency. Here, we address this challenge by combining data-driven projections with a machine learning method on data from 402 stations, with data from 87 field experiments across China. Current maize yield would be roughly doubled with the implementation of optimal planting density and management. In the 2030 s, we estimate a 52% yield improvement through dense planting and soil improvement under a high-end climate forcing Shared Socio-Economic Pathway (SSP585), compared with a historical climate trend. Based on our results, yield gains from soil improvement outweigh the adverse effects of climate change. This implies that China can be self-sufficient in maize by using current cropping areas. Our results challenge the view of yield stagnation in most global areas and provide an example of how food security can be achieved with optimal crop-soil management under future climate change scenarios.

Optimal planting density: effects on harvest time and yield

Optimal planting density: effects on harvest time and yield

Effect of Spacing, Nutrition and Their Interaction on Growth and Yield of Nerium (Nerium oleander L.) cv. Pink Single

Background: Commercial cultivation of a crop needs improved agro techniques, such as optimum plant density, application of a required quantity of manures and fertilizers, etc. As such, for nerium cultivation no specific recommendations are available on spacing and nutrition requirements and majority of growers are practicing varied spacing with different nutritional levels. Optimum spacing helps not only in obtaining increased production of better quality but also in the proper utilization of land and other inputs. Excess use of fertilizers may result in wastage of resources apart from damage to plant, soil and environment, whereas, undernutrition leads to low production and poor-quality flowers. The current study aimed to standardize optimum plant spacing and nutrition for commercial production of Nerium. Methods: This field investigation was started in 2018 and carried out up to 2019. The planting materials were planted at the College of Horticulture-Bengaluru with three levels of spacing combined with four levels of nutrition using a factorial randomized complete block design. Result: The effect of spacing, nutrition and interaction studies of Nerium genotype ‘Pink Single’ for various growth and yield parameters showed significant differences for the growth and yield parameters studied. Among the interaction of different levels of spacing and nutrition, yield/hectare was higher (14.43 t) in S2F1 (1.2 m × 1.2 m, 5 kg FYM/plant + 90:180:180 g NPK/plant).

Combination of suitable planting density and nitrogen rate for high yield maize and their source-sink relationship in Northwest China.

Increasing crop yield per unit area by increasing planting density is essential to ensure food security. However, the optimal combination of planting density and N application for high-yielding maize and its source-sink characteristics need to be more clearly understood. A two-year field experiment was conducted combining three planting densities (D1: 70,000 plants ha-1 ；D2: 100,000 plants ha-1 ；D3: 130,000 plants ha-1 ) and three nitrogen rates (N1: 150 kg N hm-2 , N2: 350 kg N hm-2 , N3: 450 kg N hm-2 ). The results showed that increasing planting density significantly increased leaf area index and grain yield, but negatively affected ear traits. The Richards model was used to fit the dynamic changes of dry matter accumulation of maize under different treatments, and the fitting results were good. Increasing planting density increased population yield while limiting the development of individual plants, bringing the period of rapid dry matter accumulation to an early end and accelerating leaf senescence. Appropriate N rate could prolong the period of rapid accumulation of dry matter in maize, increase the 100-kernel weight. Increasing planting density enhanced post-silking dry matter accumulation to a lesser extent, and the source-sink relationship of the maize population gradually developed from sink limitation to source limitation with increasing planting density. The decrease in yield due to the insufficient source strength to meet the sink demand at too high densities was the reason that limited further improvement of the optimal planting density. Appropriate N rate facilitated the realization of yield potential at high density. This article is protected by copyright. All rights reserved.

Long-term assessments of cotton fiber quality in response to plant population density: Reconciling fiber quality and its temporal stability

Regulation of plant population density is crucial for optimizing cotton fiber quality. However, the relationship between plant density and fiber quality stability under contrasting climatic conditions remains unclear, and the compromise between fiber quality and temporal stability is unknown. In this study, based on a long-term field experiment with various plant densities (1.5–10.5 plants m–2) conducted from 2008 to 2021, cotton fiber quality variability, including fiber length, fiber strength, elongation, uniformity index, and micronaire, and their temporal stability, were evaluated. We determined the optimal plant density for cotton cultivation and elucidated the contributions of plant density and climatic conditions to the variability of cotton fiber quality and its temporal stability in China’s Yellow River Valley. The results revealed that cotton maintained its upper-intermediate quality throughout the study period, with fiber length, strength, elongation, uniformity index, and micronaire values ranging between 28.04 and 30.03 mm, 26.15–30.05 cN tex–1, 5.90–6.83%, 83.85–85.44%, and 4.24–5.14, respectively. An increase in plant density improved fiber quality but impaired temporal stability (P < 0.01), and slight increases in stability at lower plant densities resulted in substantially decreased probabilities of years with major declines in fiber quality class. Plant density and climatic conditions regulated the quality traits (23.5% and 69.3% of the explained variance, respectively), including fiber length, fiber strength, and micronaire, while climate was the most important factor (75.9% of the explained variance) in determining their temporal stability. Photosynthetically active radiation and maximum and mean temperature exhibited significant positive effects on fiber quality, whereas mean diurnal temperature range had the opposite effect. A plant density of 3.3–5.1 plants m–2 ensures the highest temporal stability of cotton fiber quality without declines in the fiber quality class. This study first provides insights into balancing cotton fiber quality and temporal stability through agronomic interventions, with implications for the quantitative prediction of future crop quality changes owing to climatic variation.

A Review on Plants and Plant/Microbial Systems in Reducing Exposure

Plants and plant-microbial compounds can be a viable means of remediating contaminated soils, in this review, two approaches to phytoremediation are discussed, the first approach how plants can promote the growth of degrading microorganisms in the soil rhizosphere, which can lead to enhanced degradation of chlorinated pesticides; the second approach focuses on the potential of plants to remove and accumulate metals from their environment, a unique test system, the Target Neighbor Method, is used to evaluate how plant density affects metal uptake, these studies could provide valuable information for optimizing plant density to improve metal removal and remediate metal-contaminated soils or to minimize toxic metal accumulation in crops and reduce human exposure.