scholarly journals Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

2020 ◽  
Vol 13 (1) ◽  
pp. 165
Author(s):  
Hillary M. O. Otieno ◽  
George N. Chemining’wa ◽  
Shamie Zingore
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nutrient Management ◽  
Fertilizer Application ◽  
Growth And Yield ◽  
Yield Prediction ◽  
Maize Productivity ◽  
Dry Biomass ◽  
The Right

To mitigate low maize productivity, improve on-farm planning and policy implementation, the right fertilizer combinations and yield forecasting should be prioritized. Therefore, this research aimed at assessing the effect of applying different nutrient combinations on maize growth and yield and in-season grain yield prediction from biomass and normalized difference vegetation index (NDVI) readings. The research was done in Embu and Kirinyaga counties, in Central Kenya. Nutrient combinations tested were P+K, N+K, N+P, N+P+K, and N+P+K+Ca+Mg+Zn+B+S. The results showed consistently lowest and highest NDVI reading, dry biomass, and grain yields due to P+K and N+P+K+Ca+Mg+Zn+B+S treatments, respectively. Positive NDVI responses of 56%, 14%, 15%, and 15% were recorded with N, P, K, and combined Ca+Mg+Zn+B+S, respectively. These nutrients, in the same order, recorded 54%, 20%, 8%, and 18% positive responses with biomass. The GreenSeeker NDVI reading with grain yield and aboveground dry biomass with grain yield recorded R2 ranging from 0.23-0.53 and 0.30-0.61 (in Embu), and 0.31-0.64 and 0.30-0.50 (in Kirinyaga), respectively. When data were pooled, the prediction strength increased, reaching a maximum of 67% and 58% with NDVI and biomass, respectively. Yield prediction was even more robust when the independent variables were combined through multiple linear model at both 85 and 105 days after emergence. From this research, it is evident that the effects of balanced fertilizer application are detectable from NDVI readings—providing a tool for tracking and monitoring nutrient management effects—not just from the nitrogen perspective as commonly studied but from the combined effects of multiple nutrients. Also, grain yield could be accurately predicted early before harvesting by combining NDVI and biomass yields.

scholarly journals Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Early Spring ◽  
European Countries ◽  
Yield Prediction ◽  
Country Level ◽  
Using Data

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

scholarly journals Neem Coated Urea (NCU), An Efficient Nitrogen Source for Paddy Cultivation: A Review

2021 ◽  
Author(s):  
Ashvin Kumar Meena ◽  
R.N. Meena ◽  
Kartikeya Choudhary ◽  
Anoop Kumar Devedee ◽  
Kamlesh Meena
Keyword(s):  
Grain Yield ◽  
Nitrogen Use Efficiency ◽  
Nutrient Management ◽  
Green Revolution ◽  
Fertilizer Application ◽  
Ammonium Nitrogen ◽  
Nitrogen Use ◽  
Use Efficiency ◽  
Paddy Cultivation ◽  
The Right

Green revolution dramatically change the nitrogen application in paddy cultivation and day by day its demand increased but excessive and imbalanced use of nitrogen fertilizer has raised certain global concerns, also its low nitrogen use efficiency. Nitrogen is required in huge amounts for rice and supply of N in the right amount, at the right rate and at right time throughout the growing season is most important to increase the yield. Approximately 90% of the N-fertilizer applied worldwide is in the NH4+ form, which is rapidly oxidized to NO3- by soil nitrifier bacteria. Whereas, NCU temporarily delays the bacterial oxidation of the ammonium-nitrogen by depressing over a certain period of time the activity of Nitrosomonas bacteria in the soil. So far more than 75 studies have been conducted to study the performance of NCU in increasing the yield of rice and several other crops. In rice more than 30% of the urea consumed in India is applied, the mean increase in grain yield by replacing urea with NCU is 5 to 6%. NCU has been observed to improve nitrogen use efficiency and subsequently grain yield of rice. Possibly, applying NCU following the site-specific nutrient management principles will lead to paddy production of higher levels as observed with ordinary urea but with lower fertilizer application rates.

scholarly journals Evaluating the implementation of fertilizer-based decision support systems to increase yield productivity and efficiency of irrigated rice farming in West Java

2021 ◽  
Vol 911 (1) ◽  
pp. 012039
Author(s):  
Hasil Sembiring ◽  
Nia Romania Patriyawaty ◽  
Dedi Nugraha ◽  
Rizky Prayogo Ramadhan ◽  
Oky Dwi Purwanto ◽  
...  
Keyword(s):  
Grain Yield ◽  
Nutrient Management ◽  
Financial Analysis ◽  
Block Design ◽  
Rice Variety ◽  
Fertilizer Application ◽  
Growth And Yield ◽  
West Java ◽  
Fertilizer Recommendation ◽  
Panicle Number

Abstract Nutrient management and fertilizer application are paramount elements for increasing rice productivity. However, most of farmers are still applying fertilizer in an improper way and hence economic benefit of the yield remain low. The objective of this study was to examine various fertilizer recommendations and hence the best and efficient dose of fertilizer can be obtain to increase growth and yield of rice. This experiment was conducted in farmers irrigated lowland Sukabumi, West Java in dry season 2019. The material used was high yielding IR-64 rice variety subjected to six fertilizer recommendation, namely urea only (A), LKP (B), factory’s recommendation (C), PUTS, (D), KATAM (E) and farmer’s practice (F). This experiment was arranged in randomized block design (RBD) with four replications. The quantitative morphological and physiological traits and financial analysis were observed. The result showed that fertilizer significantly affected morphological, physiological parameters and grain yield of rice. PUTS and KATAM (9,7t/ha) treatments had higher grain yield compared to other treatments. Fertilizer by farmer’s practice tended lower in morphological, physiological and grain yield responses compared to other fertilizer recommendation. Similar pattern showed for yield components such as panicle number, grains number and % empty grain were also affected by fertilizer recommendation. Based on the financial analysis that treatment with LKP fertilizer recommendation had higher profit (75.61%) compared with farmer’s practice. That treatment can reduce fertilizer costs by 61.57%, can increase revenue by 14.04% and give a profit of Rp. 5,580,969,-.

Growth and Yield of Wheat under Irrigation by Sugar Mill’s Wastewater

2014 ◽  
Vol 24 (1-2) ◽  
pp. 211-218
Author(s):  
PK Kundu ◽  
TK Acharjee ◽  
MA Mojid
Keyword(s):  
Grain Yield ◽  
Fertilizer Application ◽  
Wastewater Irrigation ◽  
Growth And Yield ◽  
Sugar Mill ◽  
Yield Data ◽  
Full Dose ◽  
Sugar Mills ◽  
Irrigation Treatments ◽  
Mixed Water

The possibility of using sugar mill’s wastewater/effluent in irrigation was evaluated by investigating the effects of wastewater on growth and yield of wheat (Triticum aestivum cv. Prodip). The experiment was conducted at North Bengal Sugar Mill site in Natore during December 2011 to March 2012. Three irrigation treatments (I1: irrigation with fresh/tubewell water, I2: irrigation with a mixture of fresh and wastewater at 1:1 ratio and I3: irrigation with wastewater) under a main factor and three fertilizer treatments (F0: no application of fertilizer, F1: half dose fertilizer and F2: full dose fertilizer) under a sub factor were evaluated. The experiment was laid out in a split-plot design with three replications of the treatments. Wheat was grown with three irrigations totaling 14 cm applied at 4, 26 and 43 days after sowing (DAS). Important growth and yield data of the crop were recorded. The highest grain yield of 1.829 t/ha was obtained under mixed water irrigation and the lowest grain yield of 1.469 t/ha was obtained under wastewater irrigation. The three irrigation treatments, however, provided statistically similar (p = 0.05) grain yield. For the interaction between irrigation and fertilizers, mixed water irrigation and full dose fertilizer application (I2F2) provided significantly higher grain yield (2.757 t/ha) than all other treatment combinations. The second highest yield, produced under freshwater irrigation and full dose fertilizer (I1F2), was statistically similar to the yield under wastewater irrigation and full dose fertilizer (I3F2). Results of this experiment thus exposed good prospects of irrigating wheat by sugar mills’ wastewater.DOI: http://dx.doi.org/10.3329/pa.v24i1-2.19174 Progress. Agric. 24(1&2): 211 - 218, 2013

Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress

2012 ◽  
Vol 131 (6) ◽  
pp. 716-721 ◽  
Author(s):  
Shahnoza Hazratkulova ◽  
Ram C. Sharma ◽  
Safar Alikulov ◽  
Sarvar Islomov ◽  
Tulkin Yuldashev ◽  
...  
Keyword(s):  
Heat Stress ◽  
Winter Wheat ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Genotypic Variation ◽  
Terminal Heat Stress

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

scholarly journals Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize

2020 ◽  
Vol 133 (10) ◽  
pp. 2853-2868
Author(s):  
Mahlet T. Anche ◽  
Nicholas S. Kaczmar ◽  
Nicolas Morales ◽  
James W. Clohessy ◽  
Daniel C. Ilut ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Crop Improvement ◽  
Genetic Correlations ◽  
Random Regression ◽  
Multiple Time ◽  
Heritable Variation ◽  
Using Data

Abstract Key message Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Abstract Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

scholarly journals Effect of Seed Distribution and Population on Maize (Zea maysL.) Grain Yield

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bee Khim Chim ◽  
Peter Omara ◽  
Natasha Macnack ◽  
Jeremiah Mullock ◽  
Sulochana Dhital ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Block Design ◽  
N Uptake ◽  
Plant Spacing ◽  
Randomized Complete Block Design ◽  
Seed Distribution ◽  
Treatment Structure ◽  
Number Of Seeds

Maize planting is normally accomplished by hand in the developing world where two or more seeds are placed per hill with a heterogeneous plant spacing and density. To understand the interaction between seed distribution and distance between hills, experiments were established in 2012 and 2013 at Lake Carl Blackwell (LCB) and Efaw Agronomy Research Stations, near Stillwater, OK. A randomized complete block design was used with three replications and 9 treatments and a factorial treatment structure of 1, 2, and 3 seeds per hill using interrow spacing of 0.16, 0.32, and 0.48 m. Data for normalized difference vegetation index (NDVI), intercepted photosynthetically active radiation (IPAR), grain yield, and grain N uptake were collected. Results showed that, on average, NDVI and IPAR increased with number of seeds per hill and decreased with increasing plant spacing. In three of four site-years, planting 1 or 2 seeds per hill, 0.16 m apart, increased grain yield and N uptake. Over sites, planting 1 seed, every 0.16 m, increased yields by an average of 1.15 Mg ha−1(range: 0.33 to 2.46 Mg ha−1) when compared to the farmer practice of placing 2 to 3 seeds per hill, every 0.48 m.

scholarly journals Impact of integrated use of enriched compost, biochar, humic acid and Alcaligenes sp. AZ9 on maize productivity and soil biological attributes in natural field conditions

2019 ◽  
Vol 14 (2) ◽  
pp. 101-107 ◽  
Author(s):  
Azhar Hussain ◽  
Maqshoof Ahmad ◽  
Muhammad Zahid Mumtaz ◽  
Farheen Nazli ◽  
Muhammad Aslam Farooqi ◽  
...  
Keyword(s):  
Humic Acid ◽  
Grain Yield ◽  
Soil Quality ◽  
Microbial Biomass Carbon ◽  
Organic Amendments ◽  
Plant Growth Promoting Rhizobacteria ◽  
Growth And Yield ◽  
Maximum Increase ◽  
Maize Productivity ◽  
Biological Attributes

Organic amendments improve the soil quality and plant productivity as well as help in the establishment of introduced bacteria. The present study was conducted to evaluate the interactive impact of organic amendments and plant growth promoting rhizobacteria strain Alcaligenes sp. AZ9 to improve maize productivity and soil quality. organic amendments including rock phosphate enriched compost (RPEC), biochar, and humic acid were applied in soil along with and without Alcaligenes sp. AZ9. The results revealed that the sole application of organic amendments along with Alcaligenes sp. AZ9 showed increase in growth and grain yield of maize. However, a combined application of organic amendments (RPEC, biochar, and humic acid) along with Alcaligenes sp. AZ9 showed maximum increase in plant height up to 14%, shoot dry biomass up to 30%, 1000-grains weight up to 10%, grain yield up to 31%, stover yield up to 34%, and potassium (K) concentration in grains up to 12% as compared to absolute control. The increase in nitrogen (N) and phosphorus (P) concentration in grains was non-significant over control. This treatment also improved soil biological attributes in terms of the bacterial population up to 60%, microbial biomass carbon up to 22%, soil organic carbon up to 29%, and saturation percentage of soil up to 14% as compared to control. It can be concluded that the application of organic amendments improved establishment of introduced bacteria, which could be effective in improving maize growth and yield as well as soil health.

scholarly journals Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops using Small UAVs

2019 ◽  
Vol 11 (2) ◽  
pp. 112 ◽  
Author(s):  
Senlin Guan ◽  
Koichiro Fukami ◽  
Hitoshi Matsunaka ◽  
Midori Okami ◽  
Ryo Tanaka ◽  
...  
Keyword(s):  
High Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Level ◽  
Fertilizer Application ◽  
Alignment Algorithm ◽  
Distribution Maps ◽  
Ripening Stages ◽  
Highly Correlated ◽  
Multispectral Sensing

The aim of this study was to use small unmanned aerial vehicles (UAVs) for determining high-resolution normalized difference vegetation index (NDVI) values. Subsequently, these results were used to assess their correlations with fertilizer application levels and the yields of rice and wheat crops. For multispectral sensing, we flew two types of small UAVs (DJI Phantom 4 and DJI Phantom 4 Pro)—each equipped with a compact multispectral sensor (Parrot Sequoia). The information collected was composed of numerous RGB orthomosaic images as well as reflectance maps with spatial resolution greater than a ground sampling distance of 10.5 cm. From 223 UAV flight campaigns over 120 fields with a total area coverage of 77.48 ha, we determined that the highest efficiency for the UAV-based remote sensing measurement was approximately 19.8 ha per 10 min while flying 100 m above ground level. During image processing, we developed and used a batch image alignment algorithm—a program written in Python language–to calculate the NDVI values in experimental plots or fields in a batch of NDVI index maps. The color NDVI distribution maps of wide rice fields identified differences in stages of ripening and lodging-injury areas, which accorded with practical crop growth status from aboveground observation. For direct-seeded rice, variation in the grain yield was most closely related to that in the NDVI at the early reproductive and late ripening stages. For wheat, the NDVI values were highly correlated with the yield ( R 2 = 0.601–0.809) from the middle reproductive to the early ripening stages. Furthermore, using the NDVI values, it was possible to differentiate the levels of fertilizer application for both rice and wheat. These results indicate that the small UAV-derived NDVI values are effective for predicting yield and detecting fertilizer application levels during rice and wheat production.

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