We present high angular resolution (∼80 mas) ALMA continuum images of the SN 1987A system, together with CO J = 2 1, J = 6 5, and SiO J = 5 4 to J = 7 6 images, which clearly resolve the ejecta (dust ...continuum and molecules) and ring (synchrotron continuum) components. Dust in the ejecta is asymmetric and clumpy, and overall the dust fills the spatial void seen in H images, filling that region with material from heavier elements. The dust clumps generally fill the space where CO J = 6 5 is fainter, tentatively indicating that these dust clumps and CO are locationally and chemically linked. In these regions, carbonaceous dust grains might have formed after dissociation of CO. The dust grains would have cooled by radiation, and subsequent collisions of grains with gas would also cool the gas, suppressing the CO J = 6 5 intensity. The data show a dust peak spatially coincident with the molecular hole seen in previous ALMA CO J = 2 1 and SiO J = 5 4 images. That dust peak, combined with CO and SiO line spectra, suggests that the dust and gas could be at higher temperatures than the surrounding material, though higher density cannot be totally excluded. One of the possibilities is that a compact source provides additional heat at that location. Fits to the far-infrared-millimeter spectral energy distribution give ejecta dust temperatures of 18-23 K. We revise the ejecta dust mass to Mdust = 0.2-0.4 for carbon or silicate grains, or a maximum of <0.7 for a mixture of grain species, using the predicted nucleosynthesis yields as an upper limit.
Salt Tolerance Mechanisms of Plants van Zelm, Eva; Zhang, Yanxia; Testerink, Christa
Annual review of plant biology,
04/2020, Volume:
71, Issue:
1
Journal Article
Peer reviewed
Crop loss due to soil salinization is an increasing threat to agriculture worldwide. This review provides an overview of cellular and physiological mechanisms in plant responses to salt. We place ...cellular responses in a time- and tissue-dependent context in order to link them to observed phases in growth rate that occur in response to stress. Recent advances in phenotyping can now functionally or genetically link cellular signaling responses, ion transport, water management, and gene expression to growth, development, and survival. Halophytes, which are naturally salt-tolerant plants, are highlighted as success stories to learn from. We emphasize that (
a
) filling the major knowledge gaps in salt-induced signaling pathways, (
b
) increasing the spatial and temporal resolution of our knowledge of salt stress responses, (
c
) discovering and considering crop-specific responses, and (
d
) including halophytes in our comparative studies are all essential in order to take our approaches to increasing crop yields in saline soils to the next level.
•Thirty years of paddy crop related real data of the districts of Tamil Nadu State in India are collected for this work.•In this work we proposed a hybrid MLR-ANN model for better crop yield ...predictive accuracy.•Accurate prediction features and algorithm plays the major role.
Crop yield prediction is one of the challenging task in agricultural domain. Extensive research in agricultural domain has been carried out to predict better crop yield using the machine learning algorithm Artificial Neural Network (ANN) and statistical model Multiple Linear Regression (MLR). This article examines the intrinsic relationship between MLR and ANN. A hybrid MLR-ANN model has been proposed in this research work for efficient crop yield prediction. The proposed hybrid model is modeled to analyze the prediction accuracy when MLR intercept and coefficients were applied to initialize the ANN’s input layer weights and bias. Feed Forward Artificial Neural Network with Back Propagation training algorithm was used for predicting accurate paddy crop yield. In conventional ANN model, the weights and bias of input and hidden layer are initilized randomly. This hybrid MLR-ANN model, instead of random weights and bias initialization, the input layer weights and bias are initialized by using MLR’s coefficients and bias. The hybrid model prediction accuracy is compared with ANN, MLR, Support Vector Regression (SVR), k-Nearest Neighbour (KNN) and Random Forest (RF) models by using performance metrics. The computational time for both hybrid MLR-ANN and conventional ANN was calculated. The results show that the proposed hybrid MLR-ANN model gives better accuracy than the conventional models.
Seasonal agricultural production forecasting is essential for agricultural supply chain and economic prediction. However, to what extent seasonal climate prediction and remote sensing observations ...can improve crop yield forecasting at regional scale remains unknown. Using a statistical seasonal forecasting framework for U.S. county‐level maize yield, we demonstrated that (1) incorporating satellite‐based enhanced vegetation index (EVI) significantly improved the yield forecasting performance, compared with other climate‐only models using monthly air temperature (T), precipitation (P), and vapor pressure deficit (VPD). (2) The bias‐corrected climate prediction from the Coupled Forecast System model version 2 (CFSv2) showed better yield forecasting performance than the historical climate ensemble. (3) Using the “T + P + VPD + EVI” model with climate prediction from bias‐corrected climate prediction from CFSv2 outperformed the yield forecast in the World Agricultural Supply and Demand Estimates reports released by the United States Department of Agriculture, with root‐mean‐square error of 4.37 bushels per acre (2.79% of multiyear averaged yield) by early August.
Plain Language Summary
Given the significant advances in both seasonal climate prediction and satellite remote sensing, these data have not been fully used in crop yield forecasting at regional scale, and their benefits are to be quantified compared to survey‐based approaches. Here we evaluated the benefits of using seasonal climate prediction and satellite remote sensing data in forecasting U.S. maize yield at both national and county levels. To achieve this goal, we built a seasonal forecasting system for U.S. maize yield by bridging the most advanced seasonal climate prediction products from National Centers for Environmental Prediction (NCEP) with a statistical crop modeling framework. We found we could not achieve a better forecasting performance than the official survey‐based forecast from United States Department of Agriculture until we used both climate and remote sensing observations in our model. Compared with using historical climate information for the unknown future in each growing season, using climate prediction from NCEP gave better forecasting performance once we corrected the bias in the seasonal climate prediction products. Using our climate‐remote sensing combined model and bias‐corrected climate prediction from NCEP, we achieved a better forecasting performance than the United States Department of Agriculture forecast. Our system will be useful for the stakeholders in the agriculture industry and commodity market.
Key Points
Incorporating remote sensing observations significantly improves maize yield forecasting skill
Bias‐corrected seasonal climate prediction performs better than the historical climate ensemble in maize yield forecasting
The “climate + remote sensing” approach achieves high within‐season yield forecasting performance
The simultaneous improvement of grain quality and yield of cereal crops is a major challenge for modern agriculture. Here we show that a rice grain yield quantitative trait locus qLGY3 encodes a ...MADS-domain transcription factor OsMADS1, which acts as a key downstream effector of G-protein βγ dimers. The presence of an alternatively spliced protein OsMADS1
is shown to be associated with formation of long and slender grains, resulting in increases in both grain quality and yield potential of rice. The Gγ subunits GS3 and DEP1 interact directly with the conserved keratin-like domain of MADS transcription factors, function as cofactors to enhance OsMADS1 transcriptional activity and promote the co-operative transactivation of common target genes, thereby regulating grain size and shape. We also demonstrate that combining OsMADS1
allele with high-yield-associated dep1-1 and gs3 alleles represents an effective strategy for simultaneously improving both the productivity and end-use quality of rice.
•Evaluation of green manure application effect by meta-analysis.•Green manure can significantly improve soil nutrient content.•Green manures’ type affects succeeding crops yield.
The application of ...green manure is a traditional and valuable practice for agroecosystem management. In northern China, the effects of green manure on production of the region’s major crops have been extensively investigated, but the inconsistent conclusions that these case studies have yielded cannot provide effective guidance for practical local agricultural production. Here, we conducted a meta-analysis to generate a comprehensive evaluation of the effects of green manure on soil properties and crop yield in this region. Our results shown that green manure improves soil quality effectively, decreasing soil bulk density by ∼ 5.6 %, increasing microbial biomass carbon by 28 %, and improving the activities of soil enzymes by 14 % ∼ 39 %. Among the different types of green manure, legume green manure more markedly increased both nitrate and hydrolysable nitrogen, while non-legume green manure more markedly increased available potassium. Soil gravimetric water content was decreased under green manure treatment. Maize yield was significantly increased under green manure by 11 % on the whole, while effects of green manure on wheat and potato were inconsistent. In summary, the application of green manure in northern China can improve soil quality significantly, and proper green manure use can improve cash crop yields.
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