ABSTRACT
Maize is a major staple crop widely used as food, animal feed, and raw materials in industrial production. High‐density planting is a major factor contributing to the continuous increase of ...maize yield. However, high planting density usually triggers a shade avoidance response and causes increased plant height and ear height, resulting in lodging and yield loss. Reduced plant height and ear height, more erect leaf angle, reduced tassel branch number, earlier flowering, and strong root system architecture are five key morphological traits required for maize adaption to high‐density planting. In this review, we summarize recent advances in deciphering the genetic and molecular mechanisms of maize involved in response to high‐density planting. We also discuss some strategies for breeding advanced maize cultivars with superior performance under high‐density planting conditions.
High‐density planting usually triggers a shade avoidance response and results in yield loss. This review summarizes recent advances in deciphering the genetic basis of five morphological traits (plant height/ear height, leaf angle, tassel branch number, flowering time, and root architecture) essential for breeding maize cultivars with tolerance to high‐density planting.
Matching of maize growth with solar radiation is of great importance for achieving high yield. We conducted experiments using different maize cultivars and planting densities under different solar ...radiations during grain filling to quantitatively analyze the relationships among these factors. We found that a decrease in solar radiation after silking caused a drop in maize grain yield and biomass, with lower solar radiation intensities leading to worse grain yields and biomass. Cultivar ZD958 was more sensitive to solar radiation changes than cultivar XY335; slight decreases in solar radiation (i.e., 15% shading) caused significant declines in ZD958 grain yield. When total solar radiation during grain filling was less than 486.9 MJ m
for XY335 and less than 510.9 MJ m
for ZD958, the two cultivars demonstrated high yields at lower planting density of 7.5 × 10
plants ha
; average yields were 13.36 and 11.09 Mg ha
, respectively. When radiation intensities were higher than 549.5 MJ m
for XY335 and higher than 605.8 MJ m
for ZD958, yields were higher at a higher planting density of 12 × 10
plants ha
, with average yields of 20.58 Mg ha
for XY335 and 19.65 Mg ha
for ZD958.
To assemble a data set of global crop planting and harvesting dates for 19 major crops, explore spatial relationships between planting date and climate for two of them, and compare our analysis with ...a review of the literature on factors that drive decisions on planting dates. Global. We digitized and georeferenced existing data on crop planting and harvesting dates from six sources. We then examined relationships between planting dates and temperature, precipitation and potential evapotranspiration using 30-year average climatologies from the Climatic Research Unit, University of East Anglia (CRU CL 2.0). We present global planting date patterns for maize, spring wheat and winter wheat (our full, publicly available data set contains planting and harvesting dates for 19 major crops). Maize planting in the northern mid-latitudes generally occurs in April and May. Daily average air temperatures are usually c. 12-17 °C at the time of maize planting in these regions, although soil moisture often determines planting date more directly than does temperature. Maize planting dates vary more widely in tropical regions. Spring wheat is usually planted at cooler temperatures than maize, between c. 8 and 14 °C in temperate regions. Winter wheat is generally planted in September and October in the northern mid-latitudes. In temperate regions, spatial patterns of maize and spring wheat planting dates can be predicted reasonably well by assuming a fixed temperature at planting. However, planting dates in lower latitudes and planting dates of winter wheat are more difficult to predict from climate alone. In part this is because planting dates may be chosen to ensure a favourable climate during a critical growth stage, such as flowering, rather than to ensure an optimal climate early in the crop's growth. The lack of predictability is also due to the pervasive influence of technological and socio-economic factors on planting dates.
Several initiatives have been proposed to mitigate forest loss and climate change through tree planting as well as maintaining and restoring forest ecosystems. These initiatives have both inspired ...and been inspired by global assessments of tree and forest attributes and their contributions to offset carbon dioxide (CO₂) emissions. Here we use data from more than 130,000 national forest inventory plots to describe the contribution of nearly 1.4 trillion trees on forestland in the conterminous United States to mitigate CO₂ emissions and the potential to enhance carbon sequestration capacity on productive forestland. Forests and harvested wood products uptake the equivalent of more than 14% of economy-wide CO₂ emissions in the United States annually, and there is potential to increase carbon sequestration capacity by ∼20% (−187.7 million metric tons MMT CO₂ ±9.1 MMT CO₂) per year by fully stocking all understocked productive forestland. However, there are challenges and opportunities to be considered with tree planting. We provide context and estimates from the United States to inform assessments of the potential contributions of forests in climate change mitigation associated with tree planting.
Summary
Improving yield is a primary mission for cotton (Gossypium hirsutum) breeders; development of cultivars with suitable architecture for high planting density (HPDA) can increase yield per unit ...area.
We characterized a natural cotton mutant, AiSheng98 (AS98), which exhibits shorter height, shorter branch length, and more acute branch angle than wild‐type.
A copy number variant at the HPDA locus on Chromosome D12 (HPDA‐D12), encoding a dehydration‐responsive element‐binding (DREB) transcription factor, GhDREB1B, strongly affects plant architecture in the AS98 mutant. We found an association between a tandem duplication of a c. 13.5 kb segment in HPDA‐D12 and elevated GhDREB1B expression resulting in the AS98 mutant phenotype. GhDREB1B overexpression confers a significant decrease in plant height and branch length, and reduced branch angle.
Our results suggest that fine‐tuning GhDREB1B expression may be a viable engineering strategy for modification of plant architecture favorable to high planting density in cotton.
Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) ...provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible high-throughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced) and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha). The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13–15 (it is a scale to identify the phenological development stage of a plant which is here the 3- to 5-leaves development stage) when the color of young leaves differs from older leaves. Close correlations up to R2 = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to optimize farm management and to support field experimentation for agronomic and breeding purposes.
Abstract
The current study was conducted in the greenhouse at the department of Horticulture and Gardens Engineering / College of Agriculture and Forestry / University of Mosul for the period from ...October 1
st
2021 until June 10
th
2022 on the series Hot Cakes of
Matthiola incana
L. The experiment was conducted using split-plot systems with a random complete block design. The planting factor was put in the main boards, which included the uncovered and covered planting. The factor of planting date was put in the secondary boards, which included three senga gnitnalp, namely 1/10, 15/10 and 1/11. The results show that the plants that were planted using the uncovered planting system gave the highest values in terms of the leaf area (169 cm
2
) and the number of flowerets (54.64) floweret. As for the covered planting, it was convenient to increase the diameter of the first floweret (33.54 mm) and the height of the plant (28.62 cm). it was also observed that in the late period of planting, which was on 1/11/2021, the leaf area improved (181.34 cm
2
) and leaf area (170 cm
2
plant
−1
).
PDF
