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43 results for “rice yield”
High resolution rice yield data of Jiangsu, China
<p>This dataset presents rice yields of Jiangsu Province, China during 2001-2020, the spatial resolution is 1 km. </p> <p>The dataset was generated using Random Forest Regression algorithm and multi-phase remote sensing data, the accuracy is <span>R2 = 0.65, RMSE = 388.79 kg/ha, and rRMSE = 4.48%.</span></p>
PML-V2 China staple crop (maize, wheat, rice) evapotranspiration, gross primary production and yield over 2003-2018
<p>This dataset provides the yearly evapotranspiration (ET), gross primary production (GPP) and yield of the three staple crops (i.e., maize, wheat and rice) of China from 2003 to 2018. ET and GPP are estimated by the Penman-Monteith-Leuning version 2 model (PML-V2 model), and crop yield is the product of GPP and harvest index. The units of ET, GPP and yield are mm year<sup>-1</sup>, g C m<sup>-2</sup> year<sup>-1</sup> and kg ha<sup>-1</sup>, respectively.</p> <p>The PML-V2 model is calibrated and validated against the observed ET and GPP at EC sites for each crop type. The PML_V2 model uses CMFD meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 crop evapotranspiration, gross primary production datasets.</p> <p>The data format is tiff, the spatiotemporal resolution is yearly and 0.0125°, and the time span is 2003-2018. The file name is "crop_variable_year.tif". For example, a file named Maize_ETsum_2003.tif corresponds to the total ET of maize in 2003.</p>
Multitemporal multispectral imagery for rice yield and phenology prediction
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Compound dry and wet extremes lead to an increased risk of rice yield loss
<p>The dataset consists of relevant results in the paper "Compound dry and wet extremes lead to an increased risk of rice yield loss" submitted to Geophysical Research Letters.</p>
A global meta-analysis of yield-scaled N2O emissions and its mitigation efforts for maize, wheat, and rice
<p>Maintaining or even increasing crop yields while reducing nitrous oxide (N<sub>2</sub>O) emissions is necessary to reconcile food security and climate change, while the metric of yield-scaled N<sub>2</sub>O emission (i.e., N<sub>2</sub>O emissions per unit of crop yield) is at present poorly understood. Here we conducted a global meta-analysis with more than 6000 observations to explore the variation patterns and controlling factors of yield-scaled N<sub>2</sub>O emissions for maize, wheat, and rice and associated potential mitigation options. Our results showed that the average yield-scaled N<sub>2</sub>O emissions across all available data followed the order wheat (322 g N Mg<sup>-1</sup>, with the 95% confidence interval (CI): 301-346) > maize (211 g N Mg<sup>-1</sup>, CI: 198-225) > rice (153 g N Mg<sup>-1</sup>, CI: 144-163). Yield-scaled N<sub>2</sub>O emissions for individual crops were generally higher in tropical or subtropical zones than in temperate zones, and also showed a trend towards lower intensities from low to high latitudes. This global variation was better explained by climatic and edaphic factors than by N fertilizer management, while their combined effect predicted more than 70% of the variance. Furthermore, our analysis showed a significant decrease in yield-scaled N<sub>2</sub>O emissions with increasing N use efficiency or in N<sub>2</sub>O emissions for production systems with cereal yields > 10 Mg ha<sup>-1</sup> (maize), 6.6 Mg ha<sup>-1</sup> (wheat) or 6.8 Mg ha<sup>-1</sup> (rice), respectively. This highlights that N use efficiency indicators can be used as valuable proxies for reconciling trade-offs between crop production and N<sub>2</sub>O mitigation. For all three major staple crops, reducing N fertilization by up to 30%, optimizing the timing and placement of fertilizer application or using enhanced-efficiency N fertilizers significantly reduced yield-scaled N<sub>2</sub>O emissions at similar or even higher cereal yields. Our data-driven assessment provides some key guidance for developing effective and targeted mitigation and adaptation strategies for the sustainable intensification of cereal production.</p>
Biochar and nitrogen fertilizer promote rice yield by altering soil enzyme activity and microbial community structure
<p><span>Biochar can significantly change soil properties and improve soil quality.</span> <span>However, the effects of long-term combined application of biochar (B) and nitrogen (N) fertilizer on relationships between soil enzyme activity, microbial community structure and crop yield are still obscure. We characterized these relationships in a long-term (8 years) field experiment with rice, two biochar rates of 0 and 13.5 t ha<sup>-1</sup> year<sup>-1</sup> (B0 and B) and two N fertilizer rates of 0 and 300 kg N ha<sup>-1</sup> year<sup>-1</sup> (N0 and N).</span><span> The repeated, long-term combined applications of biochar and N fertilizer significantly increased microbial biomass carbon and nitrogen (MBC and MBN), but biochar decreased the abundance of total bacteria, fungi, actinomycetes, Gram-positive and Gram-negative bacteria as well as the amount of total phospholipid fatty acids. </span><span>The activity of leucine aminopeptidase (LAP) </span><span>decreased significantly in the biochar-amended and N fertilized treatment, but</span><span> the LAP activity either remained unchanged or increased with biochar amendment at N0. The relative abundance of bacterial phylum <em>Chloroflexi</em> was increased in the combined biochar and N fertilizer treatment. The changes in soil organic matter and the activity of α-1,4-xylosidase were the major properties influencing soil bacterial community composition, whereas the structure of fungal community was governed by MBC, MBN and LAP activity. In addition, long-term biochar and N fertilizer applied together significantly increased rice yield (more than biochar and nitrogen fertilizer applied alone). Yield</span> <span>was significantly positively correlated with LAP activity, but significantly negatively correlated with the relative abundance of Chloroflexi. In conclusion, long-term biochar and nitrogen fertilizer applications increased rice yield, which was associated with altered soil microbial community and enhanced activity of some enzymes.</span></p>
AsiaRiceYield4km: Seasonal Rice Yield in Asia from 1995 to 2015
<p><strong><em>请注意,我们已更新版本,包含两个名为 Version1 和 Version2 的文件!</em></strong></p> <p>第一个文件名为<em><strong>Version1.zip</strong></em>,是<strong><em>之前的版本</em></strong>,其中包含 126 个压缩到单个文件夹中的 .tif 文件。</p> <p><em>该文件支持地球系统科学数据中发表的题为“ AsiaRiceYield4km:1995 年至 2015 年亚洲季节性水稻产量</em>”(https://doi.org/10.5194/essd-15-791-2023)的出版物。</p> <p>以下是<strong><em>Version1.zip</em></strong>的描述:</p> <p><strong><em>吴华清</em></strong>和<strong><em> 张静</em></strong>对本文的贡献相同,为共同第一作者。</p> <p>该数据集提供了1995年至2015年亚洲主要稻米生产国4公里网格季节性稻米产量。</p> <p>*** 数据文件为“.tif”格式</p> <p>*** 时间分辨率:季节性</p> <p>*** 时间范围:1995-2015</p> <p>*** 像素大小:4 公里</p> <p>*** 投影信息:EPSG:4326</p> <p>本数据库使用的地图边界并不意味着我们对任何国家、领土、城市或地区或其当局的法律地位,或对其边界或边界的划定发表任何意见。</p> <p>至于名为<em><strong>Version2.zip</strong></em>的文件,则是<em><strong>更新版本</strong></em>,将<strong><em>东南亚的</em></strong>研究期<strong><em>从 1991 年延长至 2021 年,使用相同的产量估算方法。本次更新由</em></strong><strong><em>程飞</em></strong>进行。 </p> <p>以下是<strong><em>Version 2.zip</em></strong>的描述:</p> <p>该数据集提供了1991年至2021年东南亚主要稻米生产国0.05°网格季节性稻米产量。</p> <p>*** 数据文件为“.nc”格式</p> <p>*** 时间分辨率:季节性</p> <p>*** 时间范围:1991-2021</p> <p>*** 像素大小:0.05°</p> <p>*** 投影信息:EPSG:4326</p>
A global meta-analysis of yield-scaled N2O emissions and its mitigation efforts for maize, wheat, and rice
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Biochar and nitrogen fertilizer promote rice yield by altering soil enzyme activity and microbial community structure
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Data from: Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice
To ensure food security in the face of population growth, decreasing water and land for agriculture, and increasing climate variability, crop yields must increase faster than the current rates. Increased yields will require implementing novel approaches in genetic discovery and breeding. Here we demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits. We find that detecting quantitative trait loci (QTL) with HTP phenotyping is as accurate and effective as traditional labor-intensive measures of flowering time, height, biomass, grain yield, and harvest index. Genetic mapping in this population, derived from a cross of an modern cultivar (IR64) with a landrace (Aswina), identified four alleles with negative effect on grain yield that are fixed in IR64, demonstrating the potential for HTP of large populations as a strategy for the second green revolution.
Rice yield and nutrient balance in southwest China
<p>The optimal application of nutrients, such as nitrogen and phosphorus, to the soil is crucial for achieving high crop yields with minimal environmental impact. However, the effect of spatio-temporal changes in soil nutrient supply on crop yield is poorly understood in China. Here, we present a framework that combines environmental data, fertilizer field experiments, and machine learning to estimate the rice yield responses to different nutrient conditions and overall farmland nutrient sustainability in southwest China from 2009 to 2019. This dataset contains data on the spatial distribution of rice yield forecasts and farmland nutrient balances in Southwest China in 2009 and 2019.</p>
Data from: Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice
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Rice yield and nutrient balance in southwest China
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Data from: Evaluation of drip irrigation system for water productivity and yield of rice
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Effect of planting density on growth characteristics and grain yield increase in successive cultivations of two rice (Oryza sativa L.) cultivars
<p><span>Rice (<i>Oryza sativa</i> L.) re-cultivation plays an important role in increasing land productivity and efficiency in crop rotation. Planting density (PD) is an important agronomic factor to achieving maximum grain yield (GY). The current study aimed to determine the best PD for the first cultivation and re-cultivation of rice. The experiment was conducted as a split plot based on the randomized complete block design with three replications at Ghaemshahr University, Mazandaran (northern Iran) from 2013 to 2014. Treatments consisted of cultivar, i.e. Tarom Hashemi and Koohsar, at two levels as the main factor and PDs at the three levels of 16, 25, and 33.3 hills m<sup>-2</sup> with planting spaces of 25 × 25, 20 × 20, and 30 × 10 cm<sup>2</sup>, respectively, as the sub factor. The results of the current study showed that cultivar for the first cultivation, and year for the re-cultivation had a significant effect on GY. PD had a significant effect on GY in both first cultivation and re-cultivation. The GY of Hashemi in the first cultivation (36.1%) and re-cultivation (18.5%) was higher than that of Koohsar. GY in the first cultivation and re-cultivation had an increasing trend; PD increased up to 33.3 hills m<sup>-2</sup> as 19.9% and 21.4% in the first cultivation and re-cultivation, respectively, due to the increase in number of panicles m<sup>-2</sup> (30.1% and 30.6%, respectively). Hashemi is suitable for the first cultivation. It is noteworthy, also, that a density of 33.3 hills m<sup>-2</sup> is recommended for the first cultivation and re-cultivation.</span></p>
Louisiana Historic Rice Yields from 1895 to 2019
<p>This data set presents Louisiana rice yields in pounds per acre and kilograms per hectare from 1895 to 2019. This rice yield data is based on Louisiana rice statistics provided by the USDA National Agricultural Statistics Service (USDA-NASS, 2019) and Louisiana State University Agricultural Center (LSU AgCenter, 2019).</p>
Effect of planting density on growth characteristics and grain yield increase in successive cultivations of two rice (Oryza sativa L.) cultivars
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Louisiana Historic Rice Yields from 1895 to 2019
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Enhanced sucrose loading improves rice yield by increasing grain size
GEO Series GSE74204. Oryza sativa. 12 samples. Type: Expression profiling by array.
Epigenetic regulation of antagonistic receptors confers rice blast resistance with yield balance
GEO Series GSE83522. Oryza sativa. 1 samples. Type: Non-coding RNA profiling by high throughput sequencing.
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