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

2021 ◽  
Vol 51 ◽  
Author(s):  
André Luis Vian ◽  
Christian Bredemeier ◽  
Maicon Andreo Drum ◽  
João Leonardo Fernandes Pires ◽  
Elizandro Fochesatto
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Field Experiments ◽  
Plant Density ◽  
Plant Population ◽  
Growth Stages ◽  
Phenological Stages ◽  
Plant Densities ◽  
Corn Grain ◽  
Corn Grain Yield

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.

GRAIN YIELD COMPARISON OF PURE STANDS AND MIXTURES OF DIFFERENT PROPORTIONS FOR TWO HYBRIDS OF MAIZE

1985 ◽  
Vol 65 (3) ◽  
pp. 481-485 ◽  
Author(s):  
G. J. HOEKSTRA ◽  
L. W. KANNENBERG ◽  
B. R. CHRISTIE
Keyword(s):  
Zea Mays ◽  
Grain Yield ◽  
Zea Mays L ◽  
Pure Stand ◽  
Higher Plant ◽  
Data Support ◽  
Plant Densities ◽  
The Difference ◽  
Corn Grain ◽  
Corn Grain Yield

The objective of this study was to determine the effects on grain yield of growing cultivars in mixtures of different proportions. Two maize (Zea mays L.) hybrids, Pride 116 and United 106, were grown for 2 yr in pure stand and in seven mixtures of different proportions (7:1, 6:2, 5:3, 4:4, 3:5, 2:6, 1:7) at plant densities of 61 500, 99 400, and 136 000 plants per hectare. The total number of mixture combinations was 42, i.e. 2 years × three densities × seven proportions. All but one mixture yielded as expected based on the yield of component hybrids in pure stand. The higher yielding hybrid (United 106) yielded significantly less grain per plant in mixtures than in pure stand. The lower yielding hybrid (Pride 116) yielded more in mixtures than in pure stand, although the difference was not significant. These data support previous observations that the ability of a hybrid to yield in pure stands is not necessarily related to its ability to yield in mixtures. High plant densities appear to enhance the likelihood of interactions occurring among hybrids. For United 106, the number of proportions yielding less grain per plant than in pure stand was highly significant at the two higher plant densities. For Pride 116, the number of proportions yielding more than in pure stand was highly significant at the highest plant density.Key words: Corn, grain yield, mixtures of different proportions, high plant densities, Zea mays

In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index

2006 ◽  
Vol 98 (6) ◽  
pp. 1488-1494 ◽  
Author(s):  
R. K. Teal ◽  
B. Tubana ◽  
K. Girma ◽  
K. W. Freeman ◽  
D. B. Arnall ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Yield Potential ◽  
Corn Grain ◽  
Corn Grain Yield

Comparative Response of Conventional and Quality Protein Maize Hybrids to High Population Densities in the Southern Guinea Savanna of Nigeria

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
O B Bello
Keyword(s):  
Grain Yield ◽  
Plant Density ◽  
Plant Population ◽  
Yield Potential ◽  
High Yield ◽  
Quality Protein Maize ◽  
Population Densities ◽  
Higher Plant ◽  
Guinea Savanna ◽  
Plant Densities

Optimum plant population is very important in enhancing high and stable grain yield especially in quality protein maize (QPM) production. A field trial was therefore conducted to compare the performance of six hybrids (three each of QPM and normal endosperm) at three population densities using a split-plot design at the sub-station of the Lower Niger River Basin Development Authority, Oke-Oyi, in the southern Guinea savanna zone of Nigeria during the 2010 and 2011 cropping seasons. Plant population -1 densities (53,333, 66,666, and 88,888 plants ha ) constituted the main plots and the six hybrids were assigned to the subplots, replicated three times. Our results showed a differential response of maize -1 hybrids to high densities, with plant populations above 53,333 plants ha reduced grain yield, and this is more pronounced in QPM than normal endosperm hybrids. This is contrary to the results observed in many other countries. This might be that the hybrids were selected in low yield potential area at low plant densities, and hence not tolerant to plant density stress. It may also be due to low yield potential of the experimental site, which does not allow yield increases at high plant densities. Though normal endosperm hybrids 0103-11 and 0103-15 as well as QPM Dada-ba were superior for grain yield among -1 the hybrids at 53,333 plants ha , hybrid 0103-11 was most outstanding. Therefore, genetic improvement of QPM and normal endosperm hybrids for superior stress tolerance and high yield could be enhanced by selection at higher plant population densities.

scholarly journals Integrated Effect of Plant Density, N Rates and Irrigation Regimes on the Biomass Production, N Content, PAR Use Efficiencies and Water Productivity of Rice Under Irrigated Semiarid Environment

2012 ◽  
Vol 40 (1) ◽  
pp. 201 ◽  
Author(s):  
Shakeel AHMAD ◽  
Mirza HASANUZZAMAN
Keyword(s):  
Grain Yield ◽  
Management Practices ◽  
Field Experiments ◽  
Plant Density ◽  
Block Design ◽  
Water Productivity ◽  
Research Area ◽  
Semiarid Environment ◽  
Irrigation Regimes ◽  
Plant Densities

Two field experiments were conducted for two years (2000 and 2001) at Agronomic Research Area, University of Agriculture Faisalabad (UAF), Pakistan. There were 15 treatment combinations for experiment-I having three plant densities, viz., one seedling hill-1 (PD1), two seedlings hill-1 (PD2) and three seedlings hill-1 (PD3) and five nitrogen rates, viz., 0, (N0); 50, (N50); 100, (N100); 150, (N150); and 200 (N200) kg N ha-1. Experiment-II also included 15 treatments having three plant densities, viz., one seedling hill-1 (PD1), two seedlings hill-1 (PD2) and three seedlings hill-1 (PD3) and five irrigation regimes, viz., 62.5 cm (I1), 77.5 cm (I2), 92.5 cm (I3), 107.5 cm (I4), and 122.5 cm (I5). A randomized complete block design (RCBD) was employed with three repetitions. The results for experiment-I revealed that the highest biomass (1438 g m-2), grain yield (497 g m-2), crop growth rate (15.36 g m-2 d-1), net assimilation rate (4.24 g m-2 d-1) were observed in the treatment having combination of two seedlings hill-1 and 200 kg N ha-1 (PD2N200). The agronomic and economic nitrogen and PAR use efficiencies were also higher in this treatment. In case of experiment-II, the highest biomass and grain yield were obtained in case of treatment having combination of two seedlings hill-1 and 107.5 cm irrigation regime (PD2I107.5). The irrigation application based water productivity ranged from 0.36 kg mm-3 to 0.61 kg mm-1, irrigation plus precipitation based water productivity ranged from 0.32 kg mm-3 to 0.55 kg mm-3 and evapotranspiration based water productivity ranged from 0.65 kg mm-3 to 0.84 kg mm-3 among 15 treatments combination of plant density and irrigation regimes. This study concludes that for increasing the benefits for the resource-poor growers, the integration of crop management practices is an optimum strategy to substantially increase the resources use efficiency under irrigated semiarid environment.

scholarly journals Growth regulator and maize response to the increase in plant density

2017 ◽  
Vol 52 (11) ◽  
pp. 997-1005 ◽  
Author(s):  
Lucieli Santini Leolato ◽  
Luis Sangoi ◽  
Murilo Miguel Durli ◽  
Fernando Panison ◽  
Ramon Voss
Keyword(s):  
Grain Yield ◽  
Growth Regulator ◽  
Plant Density ◽  
Stem Length ◽  
Growth Stages ◽  
Sowing Time ◽  
Growing Seasons ◽  
Sowing Dates ◽  
Plant Densities ◽  
Maize Response

Abstract: The objective of this work was to evaluate the effect of application of the growth regulator Trinexapac-ethyl on maize response to the increase in plant density at two sowing dates. A field experiment was carried out in the municipality of Lages, state of Santa Catarina, Brazil, during the 2014/2015 and 2015/2016 growing seasons. Two sowing dates (10/15 - preferential, and 12/5 - late), four plant densities (5, 7, 9, and 11 plants m-2), with and without Trinexapac-ethyl application, were tested. The growth regulator was sprayed at a rate of 150 g a.i. ha-1, when hybrid P30F53YH was at the V5 and V10 growth stages. The spraying of Trinexapac-ethyl decreased the stem length above the ear insertion node at both growing seasons. Grain yield ranged from 11,422 to 14,805 kg ha-1, and increased in a quadratic way with the increment in plant density. The highest yields were reached when maize was sown in October. The spraying of Trinexapac-ethyl did not affect grain yield, but decreased the 1,000 kernels mass at both sowing dates. The use of Trinexapac-ethyl does not enhance grain yield of maize hybrid P30F53YH at crowded stands in response to the densification, regardless of sowing time.

scholarly journals Tillering does not interfere on white oat grain yield response to plant density

2003 ◽  
Vol 60 (2) ◽  
pp. 253-258 ◽  
Author(s):  
Milton Luiz de Almeida ◽  
Luís Sangoi ◽  
Márcio Ender ◽  
Anderson Fernando Wamser
Keyword(s):  
Grain Yield ◽  
Dry Matter ◽  
Field Experiments ◽  
Plant Density ◽  
Dry Mass ◽  
Main Stem ◽  
Yield Response ◽  
Individual Plant ◽  
Growing Seasons ◽  
Plant Densities

Plant density is one of the cropping practices that has the largest impact on individual plant growth. This work was conducted to evaluate the response of white oat (Avena sativa) cultivars with contrasting tillering patterns to variations in plant density. Two field experiments were carried out in Lages, SC, Brazil, during the 1998 and 1999 growing seasons. A split plot experimental design was used. Four oat cultivars were tested in the main plots: UFRGS 14, UFRGS 18, UPF 16 and UPF 17 using five plant densities split plots: 50, 185, 320, 455 and 550 plants m-2. Five plant samples were taken 25, 34, 48, 58 and 70 days after plant emergence to assess the treatment effects on dry matter partition between main stem and tillers. UFRGS 18 promoted dry matter allocation to tillers whereas UPF 17 directed dry mass mostly to the main stem. Differences in dry mass allocation between the main stem and tillers had no impact on grain yield, UPF 16 presenting the highest values for both growing seasons. The lack of interaction between population density and cultivar and the small effect of plant population on grain yield indicates that the oat tillering ability is not fundamental to define its grain yield.

Nitrogen management will influence threshold values of green foxtail (Setaria viridis) in corn

Weed Science ◽  
2003 ◽  
Vol 51 (6) ◽  
pp. 975-986 ◽  
Author(s):  
R. Jason Cathcart ◽  
Clarence J. Swanton
Keyword(s):  
Grain Yield ◽  
Field Experiments ◽  
Response Surfaces ◽  
Corn Yield ◽  
Threshold Values ◽  
Corn Production ◽  
Green Foxtail ◽  
N Rates ◽  
Corn Grain ◽  
Corn Grain Yield

Environmental legislation may impose limitations on the quantity of nitrogen (N) used in corn production on the basis of soil type and ground water flow. If N rates are reduced, this might influence the relative competitiveness of weed species. Therefore, the objectives of this research were to develop a surface response model to provide estimations of the effect of differing N rates on threshold values of green foxtail in corn and to use this model as a theoretical framework for hypothesis testing. Field experiments were conducted from 1999 to 2001 to examine the interaction of N rate and green foxtail density on corn grain yield. The experiment was designed as a two-factor factorial with N levels ranging from 0 to 200 kg N ha−1and targeted green foxtail densities ranging from 0 to 300 green foxtail plants m−2. The addition of up to 200 kg N ha−1increased corn grain yield in both weed-free and weedy treatments. Corn yield loss attributed to green foxtail ranged from 35 to 40% at 0 kg N ha−1to 12 to 17% at 200 kg N ha−1. Ridge analysis of the response surfaces indicated that optimal corn grain yield could be achieved at derived values of 131 to 138 kg N ha−1while maintaining a green foxtail density of 8 to 9 green foxtail plants m−2on a sandy soil with less than 2% organic matter. The analyses of simulation results led to the generation of hypotheses of practical relevance to N management. On the basis of the generated hypotheses, a legislated reduction in N or an increase in the cost of N fertilizer would result in a lower threshold value for green foxtail in corn. If legislation were to ban the use of all herbicides in corn production, higher N rates or an increase in mechanical weed control measures would be required to offset yield losses caused by green foxtail. The human health and environmental consequences of such legislation would be significant.

Plant density and yield of grain maize in England

1973 ◽  
Vol 81 (3) ◽  
pp. 455-463 ◽  
Author(s):  
E. S. Bunting
Keyword(s):  
Grain Yield ◽  
Water Content ◽  
Field Experiments ◽  
Plant Density ◽  
Unit Area ◽  
Maximum Yield ◽  
Spatial Arrangement ◽  
Practical Significance ◽  
Plant Densities ◽  
The Difference

SUMMARYResults from 10 field experiments are reported. Inra 200, the standard variety in official maize grain trials in Britain, was grown in six of the trials; comparative information was obtained on a range of competitive commercial hybrids and an experimental, early flowering, hybrid. The final plant densities most commonly involved ranged from 5 to 20 plants/m2, with extremes of 2 and 30 plants/m2. The effects of spatial arrangement were also considered in multifactorial or systematic designs; in general, yields increased slightly with more even spacing but no evidence was adduced that spacing, within the limits likely to be encountered in commercial practice, would significantly modify interpretations of density effects.In all varieties tested, a satisfactory model for the response in yield of grain to changes in plant density was 1/y = a + bx + cx2, where y = grain yield/plant and x = density. Estimated parameter values, however, were not the same for all varieties and significant genotype × density interactions were obtained.Grain yield/unit area in Inra 200 was maximal at densities of 8–10 plants/m2, but the response curve did not have a pronounced peak; differences in average yieldat densities ranging from 6 to 14 plants/m2 were less than 6%, and yield at 20 plants/m2 was about 80% of the maximum. Other flint × dent hybrids grown commercially for grain in northern areas (Anjou 210, L.G. 11, Warwick SL 209) reached maximum grain yield/unit area at lower densities (6–8 plants/m2), and the decline in yield with increasing density was much more marked than in Inra 200. In contrast, an earlier flowering, shorter growing, experimental hybrid (ARC 51 A) did not reach maximum yield until density was raised to 14 plants/m2, and was even more tolerant of high plant densities than Inra 200. With increasing plant density the number of ears/plant declined, falling below 1–0 in Inra 200 at densities in excess of 10 plants/m2, and averaging about 0–8 at plants/m2. Over the range 6–20 plants/m2 shelling percentage was reduced by no more than 4%, but water content of the ear (grain plus rachis) increased significantly with density. In the very early hybrid, ARC 51A, the difference in water content of the ear at 6 and 20 plants/m2 was less than 3%, but in Inra 200 it averaged about 8% and in varieties less tolerant of high densities it was often ofthe order of 15%. These results could be related to the delaying effects of increasing density on time of silk emergence. Relatively, time of pollen shed was little affected by density changes. In Inra 200 the difference in time between mid-anthesis and mid-silk was about 7 days more at 20 plants/m2 than at 6 plants/m2 while in Anjou 210 and Kelvedon 59A the comparable increase was 14 days.The practical significance of the findings is discussed in relation to current grain and silage maize production practices, and to future breeding and testing programmes in Northern Europe.

scholarly journals 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

Agriculture ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 277 ◽  
Author(s):  
Héctor García-Martínez ◽  
Héctor Flores-Magdaleno ◽  
Roberto Ascencio-Hernández ◽  
Abdul Khalil-Gardezi ◽  
Leonardo Tijerina-Chávez ◽  
...  
Keyword(s):  
Neural Network ◽  
Grain Yield ◽  
Correlation Coefficient ◽  
Plant Density ◽  
Vegetation Indices ◽  
Canopy Cover ◽  
Relative Importance ◽  
Yield Estimation ◽  
Corn Grain ◽  
Corn Grain Yield

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.

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