Variable-rate in corn sowing for maximizing grain yield

Abstract

Sowing density is one of the most influential factors affecting corn yield. Here, we tested the hypothesis that, according to soil attributes, maximum corn productivity can be attained by varying the seed population. Specifically, our objectives were to identify the soil attributes that affect grain yield, in order to generate a model to define the optimum sowing rate as a function of the attributes identified, and determine which vegetative growth indices can be used to predict yield most accurately. The experiment was conducted in Chapadão do Céu-GO in 2018 and 2019 at two different locations. Corn was sown as the second crop after the soybean harvest. The hybrids used were AG 8700 PRO3 and FS 401 PW, which have similar characteristics and an average 135-day cropping cycle. Tested sowing rates were 50, 55, 60, and 65 thousand seeds ha−1. Soil attributes evaluated included pH, calcium, magnesium, phosphorus, potassium, organic matter, clay content, cation exchange capacity, and base saturation. Additionally, we measured the correlation between the different vegetative growth indices and yield. Linear correlations were obtained through Pearson’s correlation network, followed by path analysis for the selection of cause and effect variables, which formed the decision trees to estimate yield and seeding density. Magnesium and apparent electrical conductivity (ECa) were the most important soil attributes for determining sowing density. Thus, the plant population should be 56,000 plants ha−1 to attain maximum yield at ECa values > 7.44 mS m−1. In addition, the plant population should be 64,800 plants ha−1 at values < 7.44 mS m−1 when magnesium levels are greater than 0.13 g kg−1, and 57,210 plants ha−1 when magnesium content is lower. Trial validation showed that the decision tree effectively predicted optimum plant population under the local experimental conditions, where yield did not significantly differ among populations.

Variable Rate Seeding in Precision Agriculture: Recent Advances and Future Perspectives

“…Jafari et al (2010) presented a system for modifying VRS technology requires the preparation of a prescription map showing how the electronic controller of seed drill should change the gaps between the seeds in a row to make it as convenient as possible to change the seeding rate. Indeed, VRS can be performed by commercial seed drills with electronic seed metering disc controllers that can change the seeding rate by changing the seed distance along the row [63]. There are two main ways to adapt uniform rate seed drills for variable rate seeding, i.e., by changing the active feed-roll length or by changing the seed meter drive shaft speed [62].…”

“…Optimal plant density is a function of the interaction between variety and environment, which varies between different agronomic zones [75]. The influence of plant cultivar on grain yield varies greatly depending on seeding, and the change in the seeding rate has an effect on yield components, especially stem density and the number of ears per m 2 [63,76]. As the seeding rate increases, the number of germinated plants [77] and the crop density increases [63,76], but the grain weight decreases [65,75,78], partly due to the lower efficiency of lighting [67,77].…”

“…The influence of plant cultivar on grain yield varies greatly depending on seeding, and the change in the seeding rate has an effect on yield components, especially stem density and the number of ears per m 2 [63,76]. As the seeding rate increases, the number of germinated plants [77] and the crop density increases [63,76], but the grain weight decreases [65,75,78], partly due to the lower efficiency of lighting [67,77]. Varieties have different abilities and adaptations to compensate for low or high seeding rate by modifying crop components such as the number of ears, the number of stems per m 2 [76], the number of grains per ear and the grain weight [65,67].…”

Path analysis associated with fungicide use in corn hybrids

“…Pearson correlation coefficient is used to assess the relationship between plant characters and grain yield, which has been widely applied in studies on corn (Bonea & Bonciu, 2019;Crevelari et al, 2020;Silva et al, 2021;Ruiz et al, 2022). Using this coefficient, it is possible to identify the measure of association between two variables with a linear relationship.…”

Quantifying corn emergence using UAV imagery and machine learning

“…Moreover, precision planting, which places seed at a precision spacing and depth to ensure uniform emergence for maximizing final yield, has been facilitated with technologies in today's planter systems. These include hydraulic or pneumatic downforce systems for active in-field adjustment , seed singulation to drop one seed at a time (Iacomi and Papescu, 2015), variable rate seeding based on varying soil conditions (Silva et al, 2021), and variable planting depths based on soil moisture data in real-time (Precision Planting, 2019). Early crop emergence uniformity maps could be critical to evaluate the performance of these technologies up to a field scale level to make necessary adjustments or improvements.…”