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82 results for “millet”
The Potential of Fragipans in Sustaining Pearl Millet during Drought Periods in North-Central Namiba (dataset)
<p><strong>Abstract</strong></p> <p>Sandy soils with fragipans are usually considered poorly suited for agriculture. However, these soils are cultivated in north-central Namibia as they can secure a minimum harvest during droughts. To understand the hydrological influence of fragipans in these soils, Ehenge, and what makes them valuable, their soil moisture content was measured over a time span of four months. These data were then compared to a deep soil without fragipan, Omutunda, which is more productive during normal years, but less productive during droughts. The results illustrate that the combination of sandy topsoil and shallow fragipan has beneficial effects on plant available water during dry periods, because of three reasons: (i) The high infiltration rate in the sandy topsoil, (ii) the prevention of deep drainage of water by the fragipan, and (iii) the limited evaporation losses through the capillary rise in the sand. The results also confirm the disadvantages of Ehenge during wet periods.</p> <p><strong>Description of Dataset</strong></p> <p>The dataset comprises a .pdf-file with a detailed data description, seven .csv-files (relevant datasets), and two txt-files with the R-code to produce the relevant Figures 4 and 5 from the paper “The Potential of Fragipans in Sustaining Pearl Millet during Drought Periods in North-Central Namibia” by Prudat et al. 2021.</p> <p><strong>File description<br> Rainfall.csv</strong>: daily rainfall in mm at two locations (<em>Omutunda</em> & <em>Ehenge</em>)<br> <strong>NDOB13_ehenge.csv/ NDOB13_omutunda.csv</strong>: soil temperature and soil moisture per minute<br> <strong>NDOB13_ehenge_daily.csv/ NDOB13_omutunda_daily.csv</strong>: soil temperature and volumetric soil moisture content (θ<sub>TDR</sub>) aggregated per day using the arithmetic mean<br> <strong>NDOB13_ehenge_RASW_daily.csv/ NDOB13_omutunda_RASW_daily.csv</strong>: The relative available soil water (RASW) was calculated based on the measured plant available water,</p> <p><strong>Scripts</strong><br> (1) <strong>RASW_rainfall_daily</strong>: Script to calculate and plot the relative available soil water (RASW) at the two stations and the rainfall<br> (2) <strong>Max_soil_surface_temperature</strong>: Script to calculate and plot the maximum soil surface temperature at the two stations</p> <p> </p><p><strong>References</strong></p> <p></p> <p>Cobos, D., Campbell, C., 2007. Correcting temperature sensitivity of ECH2O soil moisture sensors. Decagon Devices Pullman WA.</p> <p>Hillel, D., 1998. Environmental soil physics: Fundamentals, applications, and environmental considerations. Academic press.</p>
Small-scale farming in drylands: New models for resilient practices of millet and sorghum cultivation - Dataset and code
<p>This repository contains the primary research data and R code used for data analysis for the article "<em>Small-scale farming in drylands: New models for resilient practices of millet and sorghum cultivation"</em> Published in the journal PLOS ONE (<a href="https://doi.org/10.1371/journal.pone.0268120">https://doi.org/10.1371/journal.pone.0268120</a>)</p> <p>N.B. To run the code unzip the folder 9-RData.zip and save it in the same working directory as the datasets</p> <p>V 2.0 changes:</p> <p>A. Datasets 1-2-3 - small formatting corrections</p> <p>B. Dataset 7 - fixing some errors in the calculations</p> <p>C. Code - minor fixes and seimplicifaction</p> <p> </p>
Figure 5 in Rotylenchus wimbii n. sp. (Nematoda: Hoplolaimidae) associated with finger millet in Kenya
Figure 5: BI phylogenetic tree generated from the analysis of COI of mtDNA sequences using GTR + G + I nucleotide substitution model. Bayesian posterior probabilities are given next to each node and sequences of Rotylenchus wimbii n. sp. are in bold.
Figure 3 in Rotylenchus wimbii n. sp. (Nematoda: Hoplolaimidae) associated with finger millet in Kenya
Figure 3: BI phylogenetic tree generated from the analysis of D2-D3 of 28S rDNA sequences using GTR + G + I nucleotide substitution model. Bayesian posterior probabilities are given next to each node and sequences of Rotylenchus wimbii n. sp. are in bold.
Figure 4 in Rotylenchus wimbii n. sp. (Nematoda: Hoplolaimidae) associated with finger millet in Kenya
Figure 4: BI phylogenetic tree generated from the analysis of ITS of rDNA sequences using GTR + G + I nucleotide substitution model. Bayesian posterior probabilities are given next to each node and sequences of Rotylenchus wimbii n. sp. are in bold.
Figure 2 in Rotylenchus wimbii n. sp. (Nematoda: Hoplolaimidae) associated with finger millet in Kenya
Figure 2: Illustrations of Rotylenchus wimbii n. sp. female. A, B: Anterior part of the body showing lip and neck region; C: En face view; D, E: Lip region; F: Whole body; G to J: Tail region; K: Vulva region. Scales are given in µm.
Figure 1 in Rotylenchus wimbii n. sp. (Nematoda: Hoplolaimidae) associated with finger millet in Kenya
Figure 1-: Light microscopy and scanning electron microscopy images of Rotylenchus wimbii n. sp. female. A to C: En face view; D to I: Anterior part of the body showing lip and neck region; J: Whole female body; K to N: Vulva region; O to V: Tail region.
Fig. 3 a-h in Ecological characterization of habitats colonized by the freshwater gastropod Viviparus contectus (MILLET, 1813) (Gastropoda, Prosobranchia) - Theoretical and experimental data
Fig. 3 a-h: Logistic regression models of the single environmental variables for the presentation of eventual habitat preferences of V. contectus. For a validation of the models experimental data from diverse field studies were used (e.g., PATZNER & ISARCH 1999, STURM 2000a).
Fig. 1 in Ecological characterization of habitats colonized by the freshwater gastropod Viviparus contectus (MILLET, 1813) (Gastropoda, Prosobranchia) - Theoretical and experimental data
Fig. 1: Stereoscopic photographs showing the front and back of the shell of V. contectus with its typical shape and mouth geometry. The height of the shells measures about 4.5 cm.
Fig. 2 a-h in Ecological characterization of habitats colonized by the freshwater gastropod Viviparus contectus (MILLET, 1813) (Gastropoda, Prosobranchia) - Theoretical and experimental data
Fig. 2 a-h: Box-plots for the statistical evaluation of single environmental variables under incorporation of all malacological data available in the scientific literature and those data with occurrence of V. contectus, respectively. The black boxes range from the first to the third quartile, whereas the ends of the lines mark the minimum and the maximum of the data. The white line indicates the position of the median.
Seedball technology counterbalances the effect of small seed-size and low soil nutrients on early pearl millet seedling performance
<p>In the African Sahel region, pearl millet (<em>Pennisetum glaucum</em> (L). R. Brown) is often produced in low-nutrient soils. Seed weight could vary between 4 and 44 mg per seed. Evidence shows chemically infertile soil and small seed size significantly reduce seedling establishment and, in turn, cause low grain yield. Seedball technology can potentially counterbalance this effect. Therefore, the objective of this study was to investigate the influence of seedball on pearl millet seedling establishment. Conventionally sown and seedball-derived pearl millet seedlings of a local and an improved varieties were grown for 29 days from small and large seed sizes, in low- and medium-nutrient soils at a greenhouse of University of Hohenheim, Germany. Results showed that under low-nutrient conditions and with small seed sizes produced biomass was generally inferior to the other factor combinations. Seedball technology significantly enhanced seedling vigour, leaf number, plant height, dry matter, root length as well as fine root development, and nutrient uptake irrespective of soil nutrient level and seed size. These enhancement effects were more obvious in the local variety. A previous study revealed “early nutrient release in the seedling root zone” as seedball pearl millet seedling enhancement mechanism. The released nutrients presumably compensated for nutrient deficiency in low-nutrient soil and small seed seedlings. In the Sahelian pearl millet production system where (i) low soil nutrients, (ii) small seed sizes and (iii) local seed varieties are rampant, the application of the seedball technology under these conditions is proven effective for increased seedling vigour and is therefore recommended.</p>
Phenotypic dataset from early- and late-flowering pearl millet landraces
<p>This phenotypic database is used for phenotype-genotype association analyses in early- and late-flowering pearl millet landraces in Senegal. Data are generated from three field experiments performed in the 2016 rainy season in Senegal and in 2017 in both Niger and Senegal. In Senegal, trials were conducted at the<em> </em>Institut Sénégalais de Recherche Agricole (ISRA) field station in Bambey (14°70′N, -16°47’W). In Niger, the trial was conducted at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) field station in Sadoré (13°14′N, 2°17′E). The trials included three repetitions fully randomized. Eight and 10 individuals per accession for each repetition were sown in Niger and Senegal, respectively. Spacing between each hill was 0.9 m × 0.9 m in the Bambey trial, and 1 m × 0.8 m in the Sadoré trial. To avoid side effects, two rows of cultivated pearl millet were used to border the plots. The sowing dates were 2 August 2016, 21 July 2017 in Bambey and 17 July 2017 in Sadoré. The trials were conducted under rainfall conditions with supplementary sprinkler irrigation when necessary. The Eperon fungicide (3.88% metalaxyl-M + 64% mancozeb) was used at the seedling stage to prevent mildew attacks. Thinning was done to two plants per hill two weeks after sowing. All trials were fertilized using the micro-dosing technique (6 g NPK – 15–15–15/hill, corresponding to 93 kg ha<sup>-1</sup>) applied at planting, followed by a 50 kg ha<sup>-1</sup> urea topdressing after thinning. A total of 9,290 plants were phenotyped for 11 traits associated with plant morphology and fitness: heading date (i.e. number of days from sowing to heading), main stem length, main stem diameter, main panicle length, main panicle diameter, main panicle weight, total seed weight and 1,000 seed weight of the main panicle, total number of tillers and total number of productive and non-productive tillers. For each repetition, the mean trait value was calculated from 6.5 individuals on average after elimination of the minimal and maximal measures.</p>
Fig. 1 in Insect incidence and damage on pearl millet (Pennisetum glaucum) under various nitrogen regimes in Alabama
Fig. 1. Number (log 10) of insects per plot on pearl millet under 4 nitrogen rates.
Fig. 2 in Insect incidence and damage on pearl millet (Pennisetum glaucum) under various nitrogen regimes in Alabama
Fig. 2. Number (log 10) of different insects per plot on 4 pearl millet genotypes.
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: CGMS-WOFOST millet
<p>This is model output from CGMS-WOFOST for millet as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJ-GUESS millet
<p>This is model output from LPJ-GUESS for millet as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pDSSAT millet
<p>This is model output from pDSSAT for millet as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL millet
<p>This is model output from LPJmL for millet as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
Data for: Nitrous oxide emissions from groundnut and millets farms in semi-arid peninsular India
<p>Nitrous oxide (N<sub>2</sub>O) emissions response curves for crops grown outside temperate regions have been rare and have thus far arrived at conflicting conclusions. Most studies reporting N<sub>2</sub>O emissions from tropical cropping systems have examined only one or two nitrogen fertilizer application rate(s) which precludes the possibility of discovering nonlinear changes in emission factors (EF, % of added N converted to N<sub>2</sub>O-N) with increasing fertilizer-N rates. To examine the relationship between N rates and N<sub>2</sub>O fluxes in a tropical region, we compared farming practices with three or four N rates for their yield-scaled impacts from three crops in peninsular India. We measured N<sub>2</sub>O fluxes during nine seasons between 2012 and 2015, with N application rates ranging between 0 and 70, 0 and 90, and 0 and 480 kg-N ha<sup>-1</sup> for foxtail-millet (<em>Setaria italica</em> L., locally called korra), groundnut (<em>Arachis hypogaea</em> L., also called peanut) and finger-millet (<em>Eleusine coracana</em> L., locally called ragi), respectively. In two cases, the highest N application rate greatly exceeded crop-N needs. Potential climate smart farming agricultural practices (with low/optimized N rates) led to a 50-150% reduction in N<sub>2</sub>O emissions intensity (per unit yield) along with a reduction of 0.2-0.75 tCO2e ha<sup>-1</sup> season<sup>-1</sup> as compared to high N conventional applications. We found a non-linear increase in N<sub>2</sub>O flux in response to increasing applied N for both N-fixing and non N-fixing crops and the extent of super-linearity for non N-fixing crops was much higher than what has been reported earlier. If a linear fit is imposed on our datasets, the emission factors (EFs) for finger-millet and groundnut were ~3.5% and ~1.8%, respectively. Our data shows that for low-N tropical cropping systems, even when they have low soil carbon content, increase in N use to levels just above crop needs to enhance productivity might lead to relatively small increase in N<sub>2</sub>O emissions as compared to the impact of equivalent changes in fertilizer-N use in systems fertilized far beyond crop N needs.</p>
Agro-climatic sensitivity data of Proso Millet (Panicum miliaceum L.) in Sri Lanka
<p>Proso millet (<em>Panicum miliaceum </em>L.) is a drought tolerant underutilised crop cultivated in rainfed subsistence agricultural systems. Proso millet yields were simulated using a calibrated Agricultural Production Systems Simulator (APSIM) model for 95 locations in Sri Lanka. The yield maps were generated according to the Inverse Distance Weighting (IDW) model using ArcMap 10.7.1. The database contains Proso millet yield maps for current climate and yield change under 5 hypothetical climate change scenarios; 1<sup>o</sup>C, 1.5 <sup>o</sup>C and 2 <sup>o</sup>C temperature increments, 25% rainfall increment, and 25% rainfall reduction compared to the baseline (1980-2009) climate.</p>
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