Alfalfa Established Successfully in Intercropping with Corn in the Midwest US

Integrating alfalfa (Medicago sativa L.) with corn (Zea mays L.) for grain will increase biodiversity, reduce the negative environmental impact of corn monoculture and increase farm profitability. The objectives of this research were to evaluate forage productivity and nutritive value, along with stand establishment of alfalfa in a corn grain system in Iowa, Minnesota, and North Dakota. The experimental design was a randomized complete block with four replicates at each site. Treatments included were: sole corn (i.e., check; T1), sole alfalfa (T2), alfalfa intercropped into corn (T3), a prohexadione-treated alfalfa intercropped with corn (T4), and a spring-seeded alfalfa in the year after intercropping (T5), which was planted in plots with T1 the previous year. All sites had below normal rainfall in 2016 and 2017. Corn grain yield was significantly lower when intercropped with alfalfa (T3 and T4) compared with the check corn crop (no alfalfa, T1). Corn grain yield reduction ranged from 14.0% to 18.8% compared with the check (T1). Corn biomass yield was reduced by intercropped alfalfa (T3 and T4) by 15.9% to 25.8%. In the seeding year, alfalfa seasonal forage yield was significantly greater when corn competition was absent in all environments. The intercropped alfalfa from the previous season (T3 and T4) had almost double the forage yield than the alfalfa in the seeding year (spring-seeded alfalfa; T5). In the second production year, there were no meaningful forage yield differences (p > 0.05) across all treatments, indicating alfalfa in intercropping systems does not affect forage yield past the first production year. Prohexadione-calcium, a growth regulator, did not affect alfalfa stand density, forage yield and nutritive value. The forage nutritive value was dependent on harvest date not the alfalfa intercropping treatments. Results of our study suggest that establishing alfalfa with corn is feasible and can be a potential alternative for the upper Midwest region. However, when under drought conditions, this system might be less resilient since competition between alfalfa and corn for soil moisture will be intensified under drought or moisture-limited conditions, and this will likely depress corn grain yield. Research targeted to reintroduce perennial crops into the current dominant corn–soybean systems in the US Corn Belt is urgently needed to improve stability and resiliency of production systems.

Control of glyphosate-resistant horseweed (Conyza canadensis L.) with tiafenacil mixes in corn

Four field experiments were completed in commercial corn fields during 2019 and 2020 to determine glyphosate-resistant (GR) horseweed control in corn with tiafenacil alone or in combination with bromoxynil, dicamba, or tolpyralate applied preplant (PP). Corn planted 1 to 10 d after herbicide application was not injured with any of the herbicides tested. GR horseweed interference reduced corn grain yield 32% when left uncontrolled. Herbicides reduced GR horseweed interference and resulted in corn grain yield that was similar to the weed-free control. Glyphosate (900 g ae ha−1) + tiafenacil at 12.5, 25, and 37.5 g ha−1 controlled GR horseweed 63, 68, and 72% at 4 weeks after treatment (WAT) and decreased GR horseweed density 64, 43, and 83%, and dry biomass 69, 55, and 83%, respectively. Glyphosate + tiafenacil at 12.5, 25, and 37.5 g ha−1 plus bromoxynil (280 g ai ha−1) controlled GR horseweed 81, 88, and 87% at 4 WAT, and reduced GR horseweed density 82, 94, and 93% and dry biomass 93, 93, and 98%, respectively. Glyphosate + tiafenacil at 12.5, 25, and 37.5 g ha−1 plus dicamba (300 g ai ha−1) controlled GR horseweed 86, 88, and 88% at 4 WAT and decreased GR horseweed density 76, 89, and 86% and dry biomass 94, 98, and 98%, respectively. Glyphosate + tiafenacil at 12.5, 25, and 37.5 g ha−1 plus tolpyralate (30 g ai ha−1) controlled GR horseweed 90, 90, and 91% at 4 WAT and decreased GR horseweed density 93, 91, and 95% and dry biomass 98, 97, and 97%, respectively. The industry standards in Ontario, glyphosate + dicamba/atrazine and glyphosate + saflufenacil/dimethenamid-p controlled GR horseweed 95 and 100% at 4, 8 and 12 WAT and caused a 99 and 100% density or biomass reduction, respectively.

VEGETATION INDICES AND THEIR CORRELATION WITH SECOND-CROP CORN GRAIN YIELD IN MATO GROSSO DO SUL, BRAZIL

The emergence of satellites covering new electromagnetic wavelengthsallowed developing different vegetation indices, enabling the study of theircorrelation with grain yield. In this sense, this study aimed to evaluate the accuracy between the mean values of seven vegetation indices and the mean corn grain yield in the field by applying linear regression equations. The indices NDVI, NDRE, GNDVI, GRNDVI, and PNDVI were used, with changes proposed in the equations of the indices GRNDVI and PNDVI, in which the red wavelength was replaced by the red edge. The multispectral bands provided by the Sentinel-2A and Sentinel-2B imaging instruments were used as a source of data to calculate the vegetation indices, while the values recorded by the grain harvester were used for the survey of grain yield data. A high correlation was observed between indices and grain yield. The replacement of the red wavelength with the red edge improves the correlation between vegetation indices and grain yield. Moreover, the indices GNDVI and NDVI easily saturate, reaching maximum values and not allowing the distinction between sample classes. Therefore, the vegetation indices PRENDVI and GRENDVI are recommended for estimating grain yield.

Vegetation sensors as a tool for plant population identification and corn grain yield estimation

ABSTRACT The estimated corn grain yield is dependent on plant density and should be monitored from the beginning of its development, especially between the phenological stages V3 and V10, since these stages are more responsive to management strategies. This study aimed to evaluate the efficiency of two methods [normalized difference vegetation index (NDVI) and plant occupation index (POI)] to estimate the density of corn plants, in order to identify the plant population in different phenological stages and corn grain yield. Two field experiments were conducted in two crop seasons and treatments consisted of four plant densities (4, 6, 8 and 10 plants m-2). The NDVI measurements of the vegetative canopy were performed in the growth stages V4, V5, V6, V7, V8 and V9 (2014) and V3, V5, V6, V8, V9, V10 and V13 (2015/2016). For the POI, the measurements were performed in the stages V5, V6, V8 and V9, in both crop seasons. The different plant densities were efficient in generating variability in the NDVI and POI values throughout the corn crop development cycle, and both tools were efficient in identifying density variations. It was observed that these tools should be used between the V4 and V9 growth stages.

Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson’s algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha−1, root mean square error (RMSE) = 0.125 t ha−1) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−1, RMSE = 0.425 t ha−1) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−1, RMSE = 0.449 t ha−1). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

Advantages of Hybrid Corn Seed Production Compared to Corn Grain

Corn seed breeders are indispensable in the supply of superior hybrid corn seeds. Its ability to produce seeds is evaluated for their superiority compared to the production of corn grain yield. A study was conducted in Jatirogo District, Tuban Regency, East Java province in July to December 2019. Involving farmers cooperator implementing hybrid corn seed production in the area of 96 ha and non cooperator farmers who produce the corn grain yield in the same location. We observed the variables: cost of production, yield and farmers income. Data and information were analyzed by Benefit Cost Ratio (B/C) and Marginal Benefit Cost Ratio (MBCR). The results showed that farmers who produced hybrid corn seeds of Nasa-29 variety (cooperators) were able to produce 4.6 t/ha of wet cobs with an income of IDR. 19,470,000/ha, while farmers who produced grain corn of 7.9 t/ha with an income of IDR. 15,943,000/ha. MBCR analysis showed that, the switching is able to added the farmer income Rp.4,100 for every use cost Rp.1,000 as long as the seed procurement policy remains unchanged and related institutions continue to provide support.

Effect of cereal rye and canola on winter and summer annual weed emergence in corn

Field experiments were conducted in 2017 and 2018 at two locations in Indiana to evaluate the influence of cover crop species, termination timing, and herbicide treatment on winter and summer annual weed suppression and corn yield. Cereal rye and canola cover crops were terminated early or late (2 wk before or after corn planting) with a glyphosate- or glufosinate-based herbicide program. Canola and cereal rye reduced total weed biomass collected at termination by up to 74% and 91%, in comparison to fallow, respectively. Canola reduced horseweed density by up to 56% at termination and 57% at POST application compared to fallow. Cereal rye reduced horseweed density by up to 59% at termination and 87% at POST application compared to fallow. Canola did not reduce giant ragweed density at termination in comparison to fallow. Cereal rye reduced giant ragweed density by up to 66% at termination and 62% at POST application. Termination timing had little to no effect on weed biomass and density reduction in comparison to the effect of cover crop species. Cereal rye reduced corn grain yield at both locations in comparison to fallow, especially for the late-termination timing. Corn grain yield reduction up to 49% (4,770 kg ha–1) was recorded for cereal rye terminated late in comparison to fallow terminated late. Canola did not reduce corn grain yield in comparison to fallow within termination timing; however, late-terminated canola reduced corn grain yield by up to 21% (2,980 kg ha–1) in comparison to early-terminated fallow. Cereal rye can suppress giant ragweed emergence, whereas canola is not as effective at suppressing large-seeded broadleaves such as giant ragweed. These results also indicate that early-terminated cover crops can often result in higher corn grain yields than late-terminated cover crops in an integrated weed management program.