Abstract:The growing use of commercial unmanned aerial vehicles (UAV) and the need to adjust N fertilization rates in maize (Zea mays L.) currently constitute a key research issue. In this study, different multispectral vegetation indices (green-band and red-band based indices), SPAD and crop height (derived from a multispectral compact camera mounted on a UAV) were analysed to predict grain yield and determine whether an additional sidedress application of N fertilizer was required just before flowering. Seven different inorganic N rates (0, 100, 150, 200, 250, 300, 400 kg·N·ha −1 ), two different pig slurry manure rates (Ps) (150 or 250 kg·N·ha −1 ) and four different inorganic-organic N combinations (N100Ps150, N100Ps250, N200Ps150, N200Ps250) were applied to maize experimental plots. The spectral index that best explained final grain yield for the N treatments was the Wide Dynamic Range Vegetation Index (WDRVI). It identified a key threshold above/below 250-300 kg·N·ha −1 . WDRVI, NDVI and crop height showed no significant response to extra N application at the economic optimum rate of fertilization (239.8 kg·N·ha −1 ), for which a grain yield of 16.12 Mg·ha −1 was obtained. This demonstrates their potential as yield predictors at V12 stage. Finally, a ranking of different vegetation indices and crop height is proposed to overcome the uncertainty associated with basing decisions on a single index.
Precision agriculture requires an understanding of yield variability. The objectives of this study were to (i) document the temporal and spatial variability of corn (Zea mays L.) silage yields on dairy farms in New York, and (ii) derive farm‐based management zones that account for both types of variability. Silage yield data from 847 fields (9084 ha; six farms) were collected by yield monitoring systems between 2015 and 2017. Raw yield data were cleaned of errors via a standardized postharvest data cleaning protocol. The whole‐farm area‐weighted average yield across years and the temporal SD of yield across years for fields with 3 yr of data were used to divide each field into 10‐ by 10‐m grid‐cells. Each grid‐cell was assigned a quadrant (Q), with Q1 and Q4 having consistently higher and lower yield than the farm average yield, respectively; Q2 having variable but higher yield than the farm average; and Q3 having variable and lower yield than the farm average. The evaluation showed variability in average yield per farm, yield per field, and within‐field yield, in addition to variability across years. Spatial and temporal variability were uncorrelated, suggesting that management zones need to consider both spatial and temporal variability. The area per farm classified as variable (Q2 and Q3) ranged from 30 to 44%, illustrating the importance of implementing precision agriculture technologies and in‐season management adjustments. Research is needed to determine the optimum number of zones per farm and the number of crop years to include in developing yield stability zones. Core Ideas Corn silage yield monitors collect relevant yield data for dairy farmers. Management zones can be developed from yield stability maps. Both temporal and spatial variability are important factors to consider. A yield‐stability‐based approach can generate precision management zones.
Core Ideas Corn silage and grain yield monitors collect yield data of relevance to farmers. Evaluation of quality of yield monitor data is essential, especially for silage. A data cleaning protocol, consistent across fields, farms, and years, is needed. Semi‐automation is needed for quick and consistent processing of whole‐farm data. Yield monitor data are being used for a variety of purposes including conducting on‐farm studies, assessing nutrient balances, determining yield potential, and creating management zones. However, standardization of raw data processing is needed to obtain comparable data across fields, farms, and years. Our objective was to evaluate the impact of data cleaning protocols on corn (Zea mays L.) grain and silage yield data at the whole field (with and without headlands) and within field (soil map unit) scales. Corn silage data from 145 fields (three farms) and grain data from 88 fields (three farms) were processed. Comparisons were made to evaluate yields among three levels of cleaning: (i) none; (ii) automated cleaning (“Auto”) with filter settings derived for 10 fields per farm; and (iii) automated cleaning with manual inspection for unrepresentative patterns, after the automated cleaning step was completed (“Auto+”). The Auto+ cleaning process was conducted separately by three individuals to evaluate person‐to‐person differences. Spatial Management System software was used to read raw data and transfer to Ag Leader format. Yield Editor software was used to clean data (Auto and Auto+). Results showed the necessity of data cleaning, especially for corn silage. However, considering less than 5% deviation between methods at three spatial scales, the Auto and Auto+ cleaning resulted in similar output, as long as (i) each field or subfield included at least 100 harvester measurement points, and (ii) a moisture filter was applied for corn silage data.
Timely sowing is critical for maximizing yield for both grain and biomass in maize. The effects of early (mid-March), normal (mid-April), and late (mid-May) sowing date (SD) were studied over a three-year period in irrigated maize under Mediterranean conditions. Early SD increased the number of days from sowing to plant emergence. Late SD reduced the number of days to plant maturity, and had higher forage yields, higher grain humidity, and taller plants. The average grain and forage yields achieved were 13.2 and 21.3 Mg ha−1; 14.0 and 25.1 Mg ha−1; and 12.8 and 27.6 Mg ha−1, for crops with early, normal, and late SD, respectively. The data support the general perception of farmers that April sowings are the most appropriate in the area where the experiments were carried out. Early SD resulted in lower population densities, while later SD did not yield (grain) as high. However, late SD produced taller plants that contributed to achieve higher forage yields. Late SD could be interesting for double annual forage cropping systems. Sowing at the most appropriate time, when the soil is warm, ensures a good level of maize grain production. Future research could focus in the effect of SD for total annual yields in double-annual cropping systems
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