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opencc-by-4.0Feb 2022View details →

Tracing Maize History in Northern Iroquoia through Radiocarbon Date Summed Probability Distributions Data

<p>These files contain the data used in the radiocarbon summed probability distribution and complementary analyses to create a history of maize (<em>Zea mays</em> ssp. <em>mays</em>) in Northern Iroquoia. Results piublished in:</p> <p>Hart, John P.. "Tracing Maize History in Northern Iroquoia Through Radiocarbon Date Summed Probability Distributions" <em>Open Archaeology</em>, vol. 8, no. 1, 2022, pp. 594-607. <a href="https://doi.org/10.1515/opar-2022-0256">https://doi.org/10.1515/opar-2022-0256</a></p> <p> </p>

opencc-by-4.0Jul 2021View details →

Figure 1 Predation success ofG. aculeifer, S in Predation capacity of soil-dwelling predatory mites on two major maize pests

Figure 1 Predation success ofG. aculeifer, S. scimitus andM. robustulus on WCR and WW first instar larvae during the 10-minutes predation assays. n=20. NS = no significant difference among predator species (p-value> 0.05). The error bars represent the 95% confidence interval for the predation success.

opencc-by-4.0May 2024View details →

Maize management and yield of smallholder farmers in Sub-Saharan Africa between 2016 and 2022

<p>Yield and management practices data were collected from smallholders’ maize fields from 2016 to 2022. All fields corresponded to maize grown in pure stands (no intercropping). Data were collected from five maize producing regions in Sub-Saharan Africa: (i) north-central Nigeria (<em>n</em> = 115), (ii) Rwanda and Burundi (<em>n</em> = 2720), (iii) central Zambia (<em>n</em> = 861)<strong>,</strong> (iv) southwest Tanzania (<em>n</em> = 3710), and (v) eastern Uganda and western Kenya (<em>n</em> = 7367). Data were collected by One Acre Fund (https://oneacrefund.org/), an NGO that provides smallholder farmers access to agricultural training, credit, crop insurance services, and farming supplies. About half of the fields in the database comprised farmers who subscribed to the One Acre Fund program and the other half farmers who did not. </p> <p>Maize grain yield, plant density, and row spacing were measured in two randomly placed boxes of 36 square meters at harvest, avoiding field edges. Field geolocation was recorded in 70% of the observations. When missing, the field geolocation was defined based on the nearby town (21%) or associated district (9%) location for the purpose of retrieving climate data. Management practices associated with each field were reported by farmers, including sowing and harvest dates, cultivar name, fertilizer inputs (types and total quantities for both organic and inorganic), fertilization method, liming, weeding, and pesticides (mainly insecticides to control fall armyworms). Farmers also reported the incidence of adversities (such as pests, diseases, Striga witchweed, hail, and excess water). Field size was reported by farmers and, in those cases in which farmers could not provide an accurate measure of their field size, or there was a strong indication of mistakes (e.g., nutrient fertilizer rates out of range), One Acre Fund personnel took in-situ measurements to determine field size. Input rates per hectare were calculated as the ratio of the farmer-reported input amount and field size. Data were subjected to quality control to remove unlikely values. Maize yield outliers were detected with a Bonferroni Outlier Test. Observations with plant densities and fertilizer rates higher than four standard deviations from the mean were excluded as well as those without geolocation, no N or P data, and atypical sowing dates. After quality control, the database contains a total of 14,773 field observations.</p> <p>Inorganic fertilizer rates were converted to nutrient rates (in elemental nutrients) following typical fertilizer nutrient contents. Organic fertilizers were encoded separately in two binary variables and one continuous variable, indicating whether compost was used, if that compost contained manure, and compost application rate. Likewise, cultivars were classified into hybrids or open pollination varieties (OPVs), which included local varieties, retained seed, and improved OPVs. For hybrids, we retrieved the associated crop cycle maturity (short, medium, and long), disease tolerance traits, and year of release from companies’ seed catalogs. Reported incidence of diseases and insect pests (e.g., anthracnose, aphids, blight, cutworms, drought, fall armyworm, stemborer, termites, and stalk or kernel rot) were simplified to two binary variables indicating whether the crop was affected by pests and/or diseases. Infestation by parasitic witchweeds (Striga hermonthica and S. asiatica) was considered as a separate variable. Fertilization methods were also simplified to whether the fertilizer was applied inside a hole or broadcasted in the surface. Number of weeding operations was simplified to zero, one or two or more weeding per season. Sowing dates were expressed as a deviation from the estimated average sowing date for each climate zone-season combination. Fields were grouped based on their location using the climate zone scheme developed by the Global Yield Gap Atlas Project (www.yieldgap.org). Isolated observations (more than three standard deviations from the median distance across sites within the climate zone) were excluded from their group. In the case of climate zones with two maize seasons, each crop season was considered as a separate group. Field elevation was retrieved from the Amazon Web Services Terrain Tiles. Total precipitation during the growing season, as well as for early, flowering, and grain filling phases, was retrieved from CHIRP.  For observations with field-level coordinates data, root-zone plant-available water-holding capacity was retrieved from the World Soil Information database, and soil clay content, pH, organic carbon, and effective cation exchange capacity from iSDA. Lastly, the topography wetness index (TWI) was calculated from the elevation data. </p> <p>Table 1. List of survey-derived variables.</p> <div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Type</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>plant_date_dev</td> <td>discrete</td> <td>days</td> <td>sowing date deviation from cluster average</td> </tr> <tr> <td>pl_m2</td> <td>continuous</td> <td># m2</td> <td>plant density (plants per area)</td> </tr> <tr> <td>row_spacing</td> <td>continuous</td> <td>cm</td> <td>distance between rows</td> </tr> <tr> <td>hybrid</td> <td>binary</td> <td>-</td> <td>Was a commercial hybrid seed used?</td> </tr> <tr> <td>hyb_mat</td> <td>ordinal</td> <td>-</td> <td>hybrid maturity (early, medium, late)</td> </tr> <tr> <td>hyb_yor</td> <td>continuous</td> <td>-</td> <td>Year of release of the cultivar</td> </tr> <tr> <td>hyb_tol_mln</td> <td>binary</td> <td>-</td> <td>Tolerance to maize lethal necrosis</td> </tr> <tr> <td>hyb_tol_msv</td> <td>binary</td> <td>-</td> <td>Tolerance to maize streak virus</td> </tr> <tr> <td>hyb_tol_gls</td> <td>binary</td> <td>-</td> <td>Tolerance to gray leaf spot</td> </tr> <tr> <td>hyb_tol_nclb</td> <td>binary</td> <td>-</td> <td>Tolerance to northern corn leaf blight</td> </tr> <tr> <td>hyb_tol_rust</td> <td>binary</td> <td>-</td> <td>Tolerance to rust</td> </tr> <tr> <td>hyb_tol_ear_rot</td> <td>binary</td> <td>-</td> <td>Tolerance to ear rot</td> </tr> <tr> <td>N_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>N fertilization rate</td> </tr> <tr> <td>P_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>P fertilization rate</td> </tr> <tr> <td>K_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>K fertilization rate</td> </tr> <tr> <td>compost</td> <td>binary</td> <td>-</td> <td>Was compost applied?</td> </tr> <tr> <td>comp_t_ha</td> <td>continuous</td> <td>t/ha</td> <td>compost rate</td> </tr> <tr> <td>manure</td> <td>binary</td> <td>-</td> <td>Did the compost contain manure?</td> </tr> <tr> <td>fert_in_hole</td> <td>binary</td> <td>-</td> <td>Was the fertilizer applied in a hole?</td> </tr> <tr> <td>lime_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>lime rate</td> </tr> <tr> <td>weeding</td> <td>discrete</td> <td>#</td> <td>number of times the plot was weeded</td> </tr> <tr> <td>pesticide</td> <td>binary</td> <td>-</td> <td>Was any pesticide applied?</td> </tr> <tr> <td>disease</td> <td>binary</td> <td>-</td> <td>Was yield affected by diseases?</td> </tr> <tr> <td>pest</td> <td>binary</td> <td>-</td> <td>Was yield affected by pests?</td> </tr> <tr> <td>striga</td> <td>binary</td> <td>-</td> <td>Was yield affected by the Striga weed?</td> </tr> <tr> <td>water_excess</td> <td>binary</td> <td>-</td> <td>Was yield affected by water excess (heavy rain or flooding)?</td> </tr> </tbody> </table> <p> </p> <p><strong>Table 2. List of environmental variables. </strong></p> <div> </div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Spatial resolution</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>GDD</td> <td>°C days</td> <td>30 arc-sec (1km)</td> <td>Growing degree days</td> <td>www.worldclim.org</td> </tr> <tr> <td>AI</td> <td>unitless</td> <td>30 arc-sec (1km)</td> <td>Aridity Index (annual precipitation over potential evapotranspiration)</td> <td>www.worldclim.org</td> </tr> <tr> <td>TS</td> <td>°C</td> <td>30 arc-sec (1km)</td> <td>Temperature seasonality</td> <td>www.worldclim.org</td> </tr> <tr> <td>season_prec</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Total rainfall during the maize season (10% of planting to 50% of the harvest)</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_1</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the first third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_2</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the second third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_3</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the last third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>elev</td> <td>m.a.s.l.</td> <td>75 meters</td> <td>Elevation (altitude) above sea level</td> <td>registry.opend26ata.aws/terrain-tiles</td> </tr> <tr> <td>soil_rzpawhc</td> <td>mm</td> <td>1 km</td> <td>Root zone plant-available water holding capacity</td> <td>www.isric.org</td> </tr> <tr> <td>soil_clay</td> <td>%</td> <td>30 meters</td> <td>Clay content at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_pH</td> <td>-</td> <td>30 meters</td> <td>pH (H2O) at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_orgC</td> <td>g/kg</td> <td>30 meters</td> <td>Organic carbon at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_ECEC</td> <td>cmolc/kg</td> <td>30 meters</td> <td>Effective cation exchange capacity at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>twi</td> <td>unitless</td> <td>75 meters</td> <td>Topographic Wetness Index</td> <td>calculated from elevation</td> </tr> </tbody> </table> </div> </div> </div>

Figure 6 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 6. Regression Graph (linear model) for the variable coefficient: Apparent digestibility of crude protein for tambaqui fed diets with different particle size of corn (Ŷ = 72.2 – 1.21.X; (R2 = 52.0%; p= 0.0014)).

Figure 5 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 5. Regression Graph (Quadratic Model) for the variable specific growth rate (TCE) after 68 days of experiment (Ŷ = 6.15 – 0.00279.X + 0.00000191.X2; (R2 = 53.7%; p= 0.0006)).

Figure 2 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 2. Regression Graph (Model Quadratic) for variable weight gain in the 68 days of experiment (Ŷ = 61.3 – 0.0807.X + 0.0000574X2 (R2 = 58.5%; p= 0.0002)).

Figure 4 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 4. Regression Graph (Quadratic Model) for variable Total feed consumption in the 68 days of experiment (Ŷ = 556.6 – 0.513.X + 0.000321.X2; (R2 = 65.6%; p<0.0001)).

Figure 3 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 3. Regression Graph (Cubic Model) for apparent feed conversion variable after 68 days of experiment (Ŷ = 1.27 – 0.00284X + 0.00000783X2 – 0.00000000570X3; (R2 = 58.,1%; p= 0.0007)).

Figure 1 in Zootechnical indices and digestibility in juveniles of tambaqui Colossoma macropomum fed a diet containing particulate maize

Figure 1. Regression Graph (Model Quadratic) for variable weight final after 68 days of experiment (Ŷ = 72.3 – 0.809.X + 0.0000576X2; (R2 = 58.5%; p= 0.0002)).

Figure 4. Mean S in Assessment of Management Options on StrigQ Infestation and Maize Grain Yield in Kenya

Figure 4. Mean S. hermonthicQ emergence at 8, 10, and 12 wk after planting (WAP) for four cropping systems under natural S. hermonthicQ infestation at three locations for 3 yr (2011–2013). IR hybrid, imazapyr-resistant maize hybrid; IR-OPV, imazapyr-resistant maize, open-pollinated variety; StrigQ hybrid, S. hermonthicQ-resistant maize hybrid; WH403, commercial maize hybrid. The whiskers represent SEs.

Figure 3. Mean S in Assessment of Management Options on StrigQ Infestation and Maize Grain Yield in Kenya

Figure 3. Mean S. hermonthicQ emergence at 8, 10, and 12 wk after planting (WAP) for four cropping systems under artificial S. hermonthicQ infestation at two locations for 3 yr (2011–2013). IR hybrid, imazapyr-resistant maize hybrid; IR-OPV, imazapyr-resistant maize, open-pollinated variety; S. hermonthicQ hybrid, S. hermonthicQ-resistant maize hybrid; WH403, commercial maize hybrid. The whiskers represent SEs.

Figure 2 in Assessment of Management Options on StrigQ Infestation and Maize Grain Yield in Kenya

Figure 2. Mean number of emerged S. hermonthicQ plants at 8, 10, and 12 wk after planting (WAP) for four maize varieties under artificial (A) and natural (B) S. hermonthicQ infestation for 3 yr (2011–2013). The whiskers represent SEs. IR hybrid, imazapyr-resistant maize hybrid; IR-OPV, imazapyr-resistant maize, open-pollinated variety; StrigQ hybrid, S. hermonthicQ-resistant maize hybrid; WH403, commercial maize hybrid.

Figure 1 in Assessment of Management Options on StrigQ Infestation and Maize Grain Yield in Kenya

Figure 1. Mean number of emerged S. hermonthicQ plants at 8, 10, and 12 wk after planting (WAP) for four cropping systems under artificial (A) and natural (B) S. hermonthicQ infestation for 3 yr (2011–2013). The whiskers represent SEs.

opencc-by-4.0Dec 2022View details →

Figure 2 in The predatory mite Neoseiulus californicus (Acari: Phytoseiidae) does not respond for volatiles of maize infested by Tetranychus urticae (Acari: Tetranychidae)

Figure 2. Olfactory response of Neoseiulus californicus in Y-olfactometer. (A) maize plants without infestation vs. maize plants infested by 100 adult females of T. urticae, (B) maize plants without infestation vs. maize plants infested by 200 adult females of T. urticae and (C) maize plants infested by ten vs. 200 adult females of T. urticae. NR represents non-responsive insects (no choice). Chi-square test with 5% significance. Numbers in bars represent individual predator that choose the indicated odor. The number of predatory mite without response to the treatments (NR), after 5 minutes, was eliminated from the statistical analysis.

opencc-by-4.0Dec 2022View details →

Figure 1 in The predatory mite Neoseiulus californicus (Acari: Phytoseiidae) does not respond for volatiles of maize infested by Tetranychus urticae (Acari: Tetranychidae)

Figure 1. Olfactory response of Neoseiulus californicus in Y-olfactometer. (A) air vs. air (white bars), (B) air vs. maize plants without infestation and (C) maize plants without infestation vs. maize plants infested by ten adult females of T. urticae. NR represents nonresponsive insects (no choice). Chi-square test with 5% significance. Numbers in bars represent individual predator that choose the indicated odor. The number of predatory mite without response to the treatments (NR), after 5 minutes, was eliminated from the statistical analysis.

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Data from: A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation

<div> <p>Understanding how plant phenotypes are shaped by their environments is crucial for addressing questions about crop adaptation to new environments. This study investigated the interplay between developmental responses to temperature fluctuations and photoperiod perception in maize that contribute to genotype-by-environment variation in flowering time. We present a physiological reaction norm for flowering time plasticity (PRN-FTP) for studying large collections of genotypes tested in multi-environment trial (MET) networks. Using a new variable for computational envirotyping of sensed photoperiod, it was found that, at high latitudes, different genotypes in the same environment can experience hours-long differences in photoperiod. This emphasizes the importance of considering genotype-specific differences in the experienced environment when investigating plasticity. A statistical framework is introduced for modeling the PRN-FTP as a non-linear response function, with parameters putatively linked to different regulatory modules for flowering time. Applying the PRN-FTP to a sample of global breeding material for maize showed that tropical and temperate maize occupy distinct territories of the trait space for PRN-FTP parameters, supporting that the geographical spread and adaptation of maize was differentially mediated by exogenous and endogenous pathways for flowering time regulation. Our results have implications for understanding crop adaptation and for future crop improvement efforts.</p> </div>

opencc-zeroJul 2024View details →

opencc-by-4.0Sep 2015View details →

Fig. 2 in Genetically modified maize resistant to corn earworm (Lepidoptera: Noctuidae) in Sinaloa, Mexico

Fig. 2. Percentage of corn ears ear damaged by Helicoverpa zea in Agrisure® VipteraTM 3111, AgrisureTM 3000 GT, and their respective isolines at El Dorado, Culiacan, and Navolato (Sinaloa, Mexico). 2012. Genetically modified hybrids and their respective isolines followed by the same letter do not differ significantly (LSD; P> 0.05). ic = insecticide control

opencc-by-4.0Sep 2015View details →

Fig. 1 in Genetically modified maize resistant to corn earworm (Lepidoptera: Noctuidae) in Sinaloa, Mexico

Fig. 1. Percentage of corn ears damaged by Helicoverpa zea in AgrisureTM 3000 GT, Agrisure® VipteraTM 3110, and their respective isolines at Oso Viejo, Culiacan (Sinaloa, Mexico) during 2011.Genetically modified hybrids and their respective isolines followed by the same letter do not differ significantly (LSD; P> 0.05). ic = insecticide control.

opencc-by-4.0Mar 2015View details →

Fig. 1B. Fall armyworm weights for 7 in Use of benzimidazole agar plates to assess fall armyworm (Lepidoptera: Noctuidae) feeding on excised maize and sorghum leaves

Fig. 1B. Fall armyworm weights for 7 sorghum (off white) and maize (black) cultivars for Trial 2. Among the maize lines 'AB24E' was known to be susceptible and 'Mp708' and FAW1430' were known to be resistant to fall armyworm feeding. 'AN109', 'Collier', Entry 22, 'AN109', and 'Honey Drip' are sorghum lines. In Trial 2, fall armyworm neonate larvae were taken from the Mississippi State, Mississippi culture. Means with the same letter are not significantly different. Error bars represent one standard error of the mean.