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44 results for “crop improvement”
Functional diversity of ground beetles improved aphid control but did not increase crop yields on European farms
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Data for: Biochar co-compost improves nitrogen retention and reduces carbon emissions in a winter wheat cropping system
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Data from: No‐till establishment improves the climate benefit of bioenergy crops on marginal grasslands
<p>Expanding biofuel production is expected to accelerate the conversion of unmanaged marginal lands to meet biomass feedstock needs. Greenhouse gas production during conversion jeopardizes ensuing climate benefits, but most research to date has focused only on conversion to annual crops and only following tillage. Here we report the global warming impact of converting USDA Conservation Reserve Program (CRP) grasslands to three types of bioenergy crops using no-till (NT) versus conventional tillage (CT). In three CRP fields planted to continuous corn, switchgrass, or restored prairie we established replicated NT and CT plots. For the two years following an initial soybean year in all fields, we found that, on average, NT conversion reduced nitrous oxide (N2O) emissions by 50% and carbon dioxide (CO2) emissions by 20% compared to CT conversion. Differences were higher in year 1 than in year 2 in the continuous corn field, and in the two perennial systems the differences disappeared after year 1. In all fields net CO2 emissions (as measured by eddy covariance) were positive for the first two years following CT establishment, but following NT establishment net CO2 emissions were close to zero or negative, indicating net C sequestration. Overall, NT improved the global warming impact of biofuel crop establishment following CRP conversion by over 20-fold compared to CT (-6.01 Mg CO2e ha-1 yr-1 for NT vs. -0.25 Mg CO2e ha-1 yr-1 for CT, on average). We also found that IPCC estimates of N2O emissions (as measured by static chambers) greatly underestimated actual emissions for converted fields regardless of tillage. Policies should encourage adoption of NT for converting<br> marginal grasslands to perennial bioenergy crops in order to reduce carbon debt and maximize climate benefits.</p>
Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems
<p><span>The potent greenhouse gas nitrous oxide (N</span><sub><span>2</span></sub><span>O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ~60% of the agricultural flux. Empirical models of N</span><sub><span>2</span></sub><span>O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited </span><span><span>geographic coverage;</span></span><span> process-based biogeochemical models are rarely able to predict daily to monthly emissions with > 20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured </span><span><span>soil</span></span><span> parameters. We used sub-daily N</span><sub><span>2</span></sub><span>O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper U.S. Midwest (~3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (~1100 daily fluxes), to train an ML model that explained 65-89% of daily flux variance with very few input variables –soil moisture, days after fertilization, soil texture, air temperature, soil carbon, precipitation, and N fertilizer rate. When applied to a long-term test site not used to train the model, the model explained 38% of the variation observed in weekly to biweekly manual chamber measurements from corn, and 51% upon coupling the ML model with a cropping systems model that predicted daily soil N availability. </span><span><span>This represents a 2-3 times improvement over conventional process-based models and with substantially fewer input requirements.</span></span><span> This coupled approach </span><span><span>offers promise</span></span><span> for better predictions of agricultural N</span><sub><span>2</span></sub><span>O emissions and thus more precise global models and more effective </span><span><span>agricultural mitigation interventions.</span></span></p>
Data from: The accumulation of deleterious mutations as a consequence of domestication and improvement in sunflowers and other Compositae crops
For populations to maintain optimal fitness, harmful mutations must be efficiently purged from the genome. Yet, under circumstances that diminish the effectiveness of natural selection, such as the process of plant and animal domestication, deleterious mutations are predicted to accumulate. Here, we compared the load of deleterious mutations in 21 accessions from natural populations and 19 domesticated accessions of the common sunflower using whole-transcriptome single nucleotide polymorphism data. Although we find that genetic diversity has been greatly reduced during domestication, the remaining mutations were disproportionally biased toward nonsynonymous substitutions. Bioinformatically predicted deleterious mutations affecting protein function were especially strongly over-represented. We also identify similar patterns in two other domesticated species of the sunflower family (globe artichoke and cardoon), indicating that this phenomenon is not due to idiosyncrasies of sunflower domestication or the sunflower genome. Finally, we provide unequivocal evidence that deleterious mutations accumulate in low recombining regions of the genome, due to the reduced efficacy of purifying selection. These results represent a conundrum for crop improvement efforts. Although the elimination of harmful mutations should be a long-term goal of plant and animal breeding programs, it will be difficult to weed them out because of limited recombination.
Livestock in diverse cropping systems improve weed management and sustain yields whilst reducing inputs
<p>Dataset used for the article: </p> <p>MacLaren, C.; Storkey, J.; Strauss, J.; Swanepoel, P.; and Dehnen-Schmutz, K. (2018). Livestock in diverse cropping systems improve weed management and sustain yields whilst reducing inputs. <em>Journal of Applied Ecology.</em></p> <p> </p>
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
<p>Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.</p>
Manure applications combined with chemical fertilizer improves crop yield and soil functionality
<p>The current farming system is highly reliant on synthetic fertilizers, which adversely affect soil quality, the environment, and crop production. Improving crop productivity on a sustainable basis is a challenging issue in the current agricultural system. To address this issue, we assumed that the combined use of manure and chemical fertilizers (CF) could improve rice grain yield and soil properties without the expense of the environment. Therefore, a two-year field experiment was conducted to explore optimal fertilizer management strategies using a combination of CF and organic fertilizer in the form of cattle manure (CM) or poultry manure (PM). Manure was added at two levels and soil microbial biomass production, enzyme activities, nutrient content, as well as grain yield of rice were measured. The study consisted of six treatments: no N fertilizer control (Neg-Con); 100% chemical fertilizer (Pos-Con); 60% CM + 40% CF (High-CM); 30% CM + 70% CF (Low-CM); 60% PM + 40% CF (High-PM), and 30% PM + 70% CF (Low-PM). Results showed that the addition of manure significantly increased soil enzymatic activities such as soil invertase, acid phosphatase, urease, catalase, ꞵ-glucosidase, and cellulase as compared to sole chemical fertilizer application. Similarly, the combined fertilizers application led to significant increases in soil microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), soil pH, soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), available phosphorous (AP) and rice yield. Average increases in soil MBC, MBN, SOC AN, and AP in the 0–20 cm soil depth were 62.2%, 54.5%, 29.2%, 17.4%, and 19.8%, respectively, across the years in the High-CM treatment compared with the Pos-Con. In addition, the linear regression analysis showed that soil enzymatic activities were highly positively correlated with soil MBC and MBN. The PCA and linear regression analyses showed that the increased soil enzyme activities and microbial biomass production played a key role in the higher grain yield of rice. Overall, the results of this study demonstrate that the combined use of synthetic fertilizer and organic fertilizer in paddy fields could be beneficial for the farmers in southern China by improving soil functionality and yield of rice on a sustainable basis.</p>
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
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Data from: The accumulation of deleterious mutations as a consequence of domestication and improvement in sunflowers and other Compositae crops
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Manure applications combined with chemical fertilizer improves crop yield and soil functionality
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Data from: Machine learning improves predictions of agricultural nitrous oxide (N2O) emissions from intensively managed cropping systems
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Data from: No‐till establishment improves the climate benefit of bioenergy crops on marginal grasslands
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Multiple cropping alone does not improve year-round food security among smallholders in rural India
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Green Biostimulant from Hydrolysis of Kappaphycus alvarezii Seaweed for Growth and Crop Yield Improvement of Hybrid Maize
<p>In this project, a seaweed extract-based biostimulants that are rich in oligocarrageenans (OCs) was introduced. It was produced via direct hydrolysis of <em>Kappaphycus averazii</em> biomass in ascorbic acid. Two hydrolysates products, contains OCs with distinct molecular weight varied by different reaction time, were foliar sprayed on maize field at different dosages; and the optimal treatment gave significant increases in the plant nutrient uptakes, plant height (by ~20.6%), and grain yield (by ~21%) over the control. We consider the possibility of hydrolysates usage as an effective biostimulant to reduce fertilizers in agriculture. </p> <p>The project was granted by The Tay Nguyen 3 Program which is a national Science and Technology program for Tay Nguyen socio – economic development (2011 – 2015)</p> <p>(<a href="http://www.vast.ac.vn/en/science-and-technology-research-projects?start=400?option=com_detai&view=detai&id=1078">http://www.vast.ac.vn/en/science-and-technology-research-projects?start=400?option=com_detai&view=detai&id=1078</a>)</p>
Data from: Exploitation of interspecific diversity for monocot crop improvement
In many cultivated crop species there is limited genetic variation available for the development of new higher yielding varieties adapted to climate change and sustainable farming practises. The distant relatives of crop species provide a vast and largely untapped reservoir of genetic variation for a wide range of agronomically important traits that can be exploited by breeders for crop improvement. In this paper, in what we believe to be the largest introgression programme undertaken in the monocots, we describe the transfer of the entire genome of Festuca pratensis into Lolium perenne in overlapping chromosome segments. The L. perenne/F. pratensis introgressions were identified and characterised via 131 simple sequence repeats and 1612 SNPs anchored to the rice genome. Comparative analyses were undertaken to determine the syntenic relationship between L. perenne/F. pratensis and rice, wheat, barley, sorghum and Brachypodium distachyon. Analyses comparing recombination frequency and gene distribution indicated that a large proportion of the genes within the genome are located in the proximal regions of chromosomes which undergo low/very low frequencies of recombination. Thus, it is proposed that past breeding efforts to produce improved varieties have centred on the subset of genes located in the distal regions of chromosomes where recombination is highest. The use of alien introgression for crop improvement is important for meeting the challenges of global food supply and the monocots such as the forage grasses and cereals, together with recent technological advances in molecular biology, can help meet these challenges.
Biostimulants MTU® and pidolic acid have complementary roles in the improvement of stress tolerance, nutrient use efficiency and yield in arable crops
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Data from: Exploitation of interspecific diversity for monocot crop improvement
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Data from: Soil biota enhance agricultural sustainability by improving crop yield, nutrient uptake and reducing nitrogen leaching losses
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Data for : EU Green Deal improves food system sustainability with unequal economic impacts on consumers and producers of crops and livestock
<p>Data for an article in Communications Earth Environment - soler et al-SM3 file presents the economic model used to simulate the impacts of changes in agricultural practices, food waste and consumers' diets in the framemork of the European Green Deal proposed by the European Commission. It displays the data used to calibrate the economic model, the coefficients used to calculate the non-market environmental and nutritional impacts, and the detailed results of simulated scenarios. It provides the list of variables and parameters used in the model. The data sources used for the calibration process are detailed, as well as the status of each variable (endogenous vs exogenous). The model calibration process aims at determining (reproducing) the baseline scenario that represents the current situation; it checks that production, consumption, and price estimates calculated with the economic model are equal to those currently observed. </p> <p>soler et al-SM4 provides sensibility tests for different parameter sets</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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