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129 results for “crop yield”
Managing Crop Yield Risk at the Kellogg Biological Station, Hickory Corners, MI (2022 to 2023)
Dataset Abstract As farmers adapt to changing climate, they modify practices and technologies to manage evolving risk. Adaptive changes may be as small as adjusting a crop insurance coverage level or as large as investing in an irrigation system. Farmer attitudes toward risk and their subjective perceptions of the evolving probability distributions of crop yields drive adaptation decisions. To understand climate change adaptation behavior by farmers, we undertook the study “Elicitation and Estimation of Risk Preference and Subjective Probabilities to Understand Farmer Decisions on Climate Change Adaptation.” We interviewed 44 Michigan corn and soybean farmers to elicit mathematical expressions of their risk attitudes. During the interviews, each completed two sets of lottery choices, the first using 25 general risky gambles and the second using 18 risky gambles in a crop farming context that enable econometric estimation of risk attitudes (using variants of Expected Utility Theory). Next, they answered questions about corn yield probability distributions over the past ten years and the next ten years (triangular distributions of minimum, most likely, and maximum values) with no water management, irrigation, tile drainage, and drought-resistant seed. After that, they reported on water management investments that they have made in past and intend to make in future. Finally, they provided background information about themselves and their farms. This study (MSU Study ID: STUDY00007871) was submitted to the Michigan State University Institutional Review Board (IRB) by principal investigator Scott Swinton. On July 5, 2022, it was determined to be exempt under 45 CFR 46.104(d) 3(i)(B). Data collection took place during September 2022 through March 2023. Farmer respondents completed the survey instrument on Qualtrics with assistance from graduate students in Agricultural, Food, and Resource Economics at Michigan State University at various MSU Extension offices and restaura
Sample data for "A weakly supervised framework for high resolution crop yield forecasts"
<p>This dataset includes sample data for the United States to run the weakly supervised framework as described in the paper titled <em>A weakly supervised framework for high resolution crop yield forecasts</em>, accessible at </p> <table summary="Additional metadata"> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2205.09016">https://doi.org/10.48550/arXiv.2205.09016</a></td> </tr> </tbody> </table> <p> </p> <p>The updated paper (including results from the US) is published in Environmental Research Letters:</p> <p><a href="https://doi.org/10.1088/1748-9326/acf50e">https://doi.org/10.1088/1748-9326/acf50e</a></p> <p> </p> <p>The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting/tree/weaksup.</p> <p> </p> <p>Data</p> <p>1. County data (county-data.zip) for county-level strongly supervised models:</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p> <p>* CSSF_COUNTY_US.csv: Crop productivity indicators including total above-ground production (kg ha<sup>-1</sup>), total weight of storage organs (kg ha<sup>-1</sup>), development stage (0-2). Source: de Wit et al. (2022).</p> <p>* METEO_COUNTY_US.csv: Meteo data including maximum, minimum, average daily air temperature (℃); sum of daily precipitation (PREC) (mm); sum of daily evapotranspiration of short vegetation (ET0) (Penman-Monteith, Allen et al., (1998)) (mm); climate water balance = (PREC - ET0) (mm). Source: Boogaard et al. (2022).</p> <p>* REMOTE_SENSING_COUNTY_US.csv: Fraction of Absorbed Photosynthetically Active Radiation (Smoothed) (FAPAR). Source: Copernicus GLS (2020).</p> <p>* SOIL_COUNTY_US.csv: Soil water holding capacity. Source: WISE Soil Property Database (Batjes, 2016).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p> </p> <p>2. 10-km grid data (grid-data.zip) for grid-level strongly supervised models:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level (similar to county data above).</p> <p>* METEO_GRIDs_US.csv: Meteo data at 10km grid level (similar to county data above).</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level (similar to county data above).</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level (similar to county data above).</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021), Lobell et al. (2020).</p> <p> </p> <p>3. County labels and 10-km grid inputs (dscale-US.zip) for weak supervision:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level.</p> <p>* METEO_GRIDs_US.csv: Meteo indicators at 10km grid level.</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level.</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level.</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p>
Agronomic Yields in Row Crop Agriculture at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2021)
Dataset AbstractThis data set contains information about agronomic yields for the Main Cropping System Experiment which include treatments 1-4 (corn – wheat – soybean rotations) and after 1994 treatment 6 (alfalfa). Agronomic yields are measured during normal crop harvest; yields are determined by machine harvesters appropriate to each crop as described in the Agronomic protocol.original data source http://lter.kbs.msu.edu/datasets/23
Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"
<p>Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis</p> <p> </p>
Crop-specific global fertilizer application rates from "Closing yield gaps through nutrient and water management"
<p>Crop-specific global maps of N, P2O5, and K2O fertilizer application rates circa the year 2000 from the following paper:</p> <p>Mueller, ND, JS Gerber, M Johnston, DK Ray, N Ramankutty, and JA Foley. 2012. Closing yield gaps through nutrient and water management. <em>Nature</em> <strong>490</strong>: 254–257</p> <p>Data are provided at five arc-minute resolution and are saved as netcdf files. Fertilizer application rates are estimated from reconciling various national and subnational data sources. See the Supplementary Information from the 2012 paper for a full description of data sources and methods. Data quality for each grid cell is described in a map layer. Files containing the text "totalcons" sum nutrient consumption across crops per grid cell, using crop harvested areas from Monfreda et al. 2008 Global Biogeochemical Cycles. For maize, wheat, and soybean N application rates, additional maps and csv files (containing the text "politboundaries") identify the political units around the world containing unique information. Crops and crop group categories are consistent with those utilized in Monfreda et al. 2008 Global Biogeochemical Cycles.</p>
Contrasting effects of landscape composition on crop yield mediated by specialist herbivores
<p>Landscape composition not only affects a variety of arthropod-mediated ecosystem services, but also disservices, such as herbivory by insect pests that may have negative effects on crop yield. Yet, little is known about how different habitats influence the dynamics of multiple herbivore species, and ultimately their collective impact on crop production. Using cabbage as a model system, we examined how landscape composition influenced the incidence of three specialist cruciferous pests (aphids, flea beetles, and leaf-feeding Lepidoptera), lepidopteran parasitoids, and crop yield across a gradient of landscape composition in New York, USA. We expected that landscapes with a higher proportion of cropland and lower habitat diversity would lead to an increase in pest pressure of the specialist herbivores and a reduction in crop yield. However, results indicated that neither greater cropland area nor lower landscape diversity influenced pest pressure or yield. Rather, pest pressure and yield were best explained by the presence of non-crop habitats (i.e. meadows) in the landscape. Specifically, cabbage was infested with fewer Lepidoptera in landscapes with a higher proportion of meadows likely resulting from increased parasitism. Conversely, cabbage was infested with more flea beetles and aphids as the proportion of meadows in the landscape increased, suggesting that these pests benefit from non-crop habitats. Furthermore, path analysis confirmed that these landscape-mediated effects on pest populations can have either positive or negative cascading effects on crop yield. Our findings illustrate how different pest species within the same cropping system show contrasting responses to landscape composition with respect to both the direction and spatial scale of the relationship. Such tradeoffs resulting from the complex interaction between multiple-pests, natural enemies, and landscape composition must be considered, if we are to manage landscapes for pest suppression benefits.</p>
Dataset of crop yield and management in CS02 from Diverfarming project
<p>Auxiliary data related to crop management and data of different crop yields in mandarin monoculture and mandarin diversified treatments during three crop cycles in case study 02 from Diverfarming project</p>
Data of yield in a mandarin crop derived from Diverfarming project
<p>Crop yield data, auxiliary data and methods metadata from a mandarin crop studied in Diverfarming project</p>
Contrasting effects of landscape composition on crop yield mediated by specialist herbivores
Open the record for dataset details and reuse information.
Simulated bioenergy crop yield on agricultural land in a 10-county region of western North Carolina.
We used a mechanistic plant growth model, ALMANAC (Kiniry 1996), to simulate the growth of bioenergy crops including switchgrass, miscanthus, and hybrid poplar. We selected simulation points by overlaying SSURGO soil data polygons with a 1-km resolution observed climate dataset (Thornton et al. 2012). The centroid of each unique soil polygon and climate cell combination was used as a simulation point, resulting in over 69,000 simulation points. Crop growth was simulated at each point for 10 (grasses) or 12 (poplar) years and replicated 10 times. We limited our simulation to area currently identified as agriculture, pasture, grass- or shrubland in the 2012 National Cropdata Layer.
Ascorbic Acid Hydrolysate of Kappaphycus alvarezii as an Effective Biostimulant for Growth and Crop Yield Improvement of Hybrid Maize in Vietnam
<p>The study<strong> </strong>researches on plant production with particular attention to the environment. It first mentions the usage of ascorbic acid for depolymerization of carrageenans from dry <em>K. alvarezii</em> biomass, its hydrolysate’ characteristics, and its positive influence on the hybrid maize crop in Vietnam. The study is one of the very fewer studies evaluating the plant-growth promoting activity of oligocarrageenans on maize crops. The results of the study introduce an oligocarrageenans-rich biostimulant prepared by acid hydrolysis of <em>K. alvarezii </em>seaweed in ascorbic acid, and positive impacts of foliar spraying of the hydrolysate (at different Mw of oligocarrageenans and concentrations) on the hybrid maize crop in Vietnam. The application of this product resulted in increases in efficiency of nutrient uptake by the plant, plant height (~20. 6%), and grain yield (by 21.3% over the control). Therefore, it is suitable for application on a large scale agriculture to reduce the chemical fertilizers used</p>
High quality figures of "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China"
<p>This repository provides the figures for the publication "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China" in their original resolution, ensuring clarity and high-quality visual representations for readers.</p>
Long time series (2001-2015) high-resolution crop yield and water productivity dataset of China
<p>A long-term data series, at 1-km resolution, of crop yield (kg/ha) and crop water productivity (kg/m3) for maize and wheat across China, based on the MOD16 ET product, multiple remotely sensed crop physiological and environmental indicators, and crop phenological information, using a random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. These high-resolution crop yield and CWP datasets generated in this study revealed spatiotemporal patterns of agricultural production in China and may be applied to many scenarios, including understanding effects of climate change on agricultural production capacity in China under increasing demand for food security to optimize agricultural production strategies.</p>
Supplementary material for: "Seeds adapted to mixed cropping increase yield and drought resistance of cereal-legume mixtures"
<p>Supplementary material for the research article: "Seeds adapted to mixed cropping increase yield and drought resistance of cereal-legume mixtures".</p> <ul> <li>Raw data</li> <li>R analysis code</li> <li>Statistical analysis info: ANOVA and Tukey comparisons tables</li> </ul> <p> </p> <p>Abstract:</p> <p>Cropland diversification through mixed cropping has the potential of achieving a more sustainable agriculture while securing food production. This is of special relevance with climate change and the expected drier growing conditions in the future. Seed adaptation to this cropping method is hypothesised to be a fundamental factor to maximise these benefits, as well as the particular species combined. In this study we compared the performance of four cereal-legume mixed crops (wheat and oat mixed with lupin and lentil in pairs) with their respective monocrops. Each crop was sown using seeds adapted to monoculture and mixed cropping, respectively. Moreover, they were grown under early-season and late-season drought treatments and under control conditions. We measured above-ground vegetative biomass, seed yield and harvest index to evaluate crop production, drought resistance and the effect of seed adaptation on each mixed and monocrop. Our results show that mixed cropping either had a beneficial or neutral effect on crop yield, depending on the species combination and drought conditions, but harvest index was generally higher in monocrops. We also confirmed that seed adaptation to a particular type of cropping is clearly a determining factor in its performance. In accordance with the insurance hypothesis, mixed cropping has the effect of protecting crop yields in the case of a sudden bad performance of one of the species, for example, caused by adverse environmental conditions. It is necessary to focus on effective species combinations which have the best responses to mixed cropping. We show for the first time that wheat-lentil mixtures performed poorly, while wheat-lupin showed the most promising results improving yield and drought resistance. Oat mixed crops did not show differences with the respective monocrops, so they can be a viable cropping option as well and benefit from advantages of crop diversity not measured in this study.</p>
Data from: Controlled drainage and subirrigation suitability in the United States: A meta-analysis of crop yield and soil moisture effects
<p>Controlled drainage and subirrigation (CDSI) is an important water management strategy in many regions, but the conditions under which CDSI is most likely to increase crop yield and soil moisture are not fully understood. A meta-analysis, consisting of 154 pairwise observations from replicated and randomized trials in 30 peer-reviewed primary research articles on CDSI (6 controlled drainage, 24 CDSI, analyzed together due to data scarcity), was conducted to study the responses of yield and soil moisture to CDSI, and investigate how crop type, soil texture, and cumulative growing season precipitation (PGS) influence these responses. Based on the yield response to these moderating factors, we used a fuzzy-logic approach to map potentially suitable locations for CDSI in the conterminous United States. On average, CDSI increased yield by 8.0% (95% CI = 1.8–14.7%) compared with conventional free drainage. The yield response to CDSI did not differ among crops. However, a greater yield response to CDSI was observed in medium-textured soils (19.4% increase; 95% CI = 12.4–27.0%) than in coarse- or fine-textured soils. The positive effect of CDSI on yield increased with decreasing PGS in coarse- and medium-textured soils. There was no clear effect of CDSI on soil moisture, nor did any moderators influence this relationship, though this may be attributed to the scarcity of studies on CDSI reporting soil moisture. The fuzzy-logic-based approach revealed that while potentially suitable areas are mostly concentrated in the well studied U. S. Midwest, these areas also exist in other regions where CDSI may warrant further study.</p>
REFLOW - Dataset for the report on crop yield, crop uptake of N
<p>The dataset includes data of yields of maize and its uptake of nitrogen with application of REFLOW fertilizers from a field experiment in Denmark. </p>
Dataset for paper "Marked impacts of pollution mitigation on crop yields in China"
<p>"corn.csv", "winterwheat.csv" and "midlatepaddy.csv" report historical crop yield (unit: kg/hectare)of corn, winter wheat and single cropping rice in China. <br> "regression.ipynb" contains python code to (i) derive the sensitivity of crop yield to climate and pollution variables (ii) compare predicted yield vs. observed yield (iii) calculate the relative contribution of individual climate and pollution variables to inter-annual variations of crop yield. Here we provide the complete datasets for corn in file “organized_data_corn.csv”, which includes air temperature at 2m (t2m), precipitation (tp), aerosol optical depth (aod) and surface ozone (o3) for plant season (plant), growing season (mid) and harvest season (harvest). Please contact us if you are interested in datasets of other two crop species.</p>
Figure 4 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 4. Effects of the interaction of weed establishment time and distance from the crop row on Amoronthus polmeri dry weight before soybean harvest. Vertical bars represent ± standard error of the mean (SE2014 = 1.27; SE2015 = 0.74) from the analysis for comparisons between weed establishment times with sample size n = 72. WAE, weeks after soybean emergence.
Figure 3 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 3. Effects of the interaction of weed establishment time and distance from the crop on Amoronthus polmeri plant height at harvest. Vertical bars represent ± standard error of the mean (SE2014 = 4.68; SE2015 = 3.14) from the analysis for comparisons between weed establishment times with sample size n = 72. WAE, weeks after soybean emergence.
Figure 7 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 7. Relationship between ground cover and extinction coefficient for each sampling date (n = 12 plots) throughout the 2014 growing season. WAE, weeks after soybean emergence.
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