Yield and Physiological Response of Common Bean in Three Semi-Commercial Farmers’ Fields in Limpopo Province

2019 ◽  
Author(s):  
K K Ayisi ◽  
L Munjonji ◽  
K V Masekela
Keyword(s):  
Grain Yield ◽  
Common Bean ◽  
Physiological Response ◽  
Vegetation Index ◽  
Irrigation Management ◽  
Limpopo Province ◽  
Area Index ◽  
Spad Value ◽  
Normalised Difference Vegetation Index ◽  
Consumption Rates

Despite the importance of common bean (Phaseolus vulgaris) in human nutrition, its production in developing countries is far below consumption rates. A study was established to understand the yield performance and physiological response of common bean under three farmers’ field in the Limpopo Province. The results revealed significant variation in grain yield and biomass between the locations. Physiological parameters such as the Normalised Difference Vegetation Index (NDVI), Leaf Area Index (LAI), SPAD value and leaf gas exchanges were strongly influenced by the location, the crop growth stage and crop management. Dry bean crop performance was significantly lower at Masemola. Grain yield at Masemola was 30% less than that achieved at Dalmada and Mokopane. NDVI and LAI were also consistently lower at Masemola when compared to the other locations. This study revealed that poor yields by emerging farmers in dry areas are due to poor agronomic and irrigation management.

scholarly journals A simple approach for monitoring vegetation change using time series remote sensing analysis: A case study from the Thathe Vondo Area in Limpopo Province, South Africa

2021 ◽  
Vol 117 (7/8) ◽  
Author(s):  
Nndanduleni Muavhi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Simple Approach ◽  
Limpopo Province ◽  
Difference Image ◽  
Normalised Difference Vegetation Index ◽  
Gain And Loss ◽  
The Difference

This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.

scholarly journals Smartphone Application Development for Rice Field Management Through Aerial Imagery and Normalised Difference Vegetation Index (NDVI) Analysis

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Nor Athirah Roslin ◽  
Nik Norasma Che’Ya ◽  
Rhushalshafira Rosle ◽  
Mohd Razi Ismail
Keyword(s):  
Weed Management ◽  
Human Error ◽  
Vegetation Index ◽  
Smartphone Application ◽  
Mobile App ◽  
Acceptance Test ◽  
Multispectral Imagery ◽  
Application Development ◽  
Spad Value ◽  
Normalised Difference Vegetation Index

In the current practices, farmers typically rely on the traditional method paper-based for farming data records, which leads to human error. However, the paper-based system can be improved by the mobile app technology to ease the farmers acquiring farm data as all of the farm information will be stored in digital form. This study aimed to develop a smartphone agricultural management app known as Padi2U and implement User Acceptance Test (UAT) for end-users. Padi2U was developed using Master App Builder software and integration with the multispectral imagery. Padi2U provides recommendations based on the Department of Agriculture’s (DOA), such as rice check, pest and disease control, and weed management. Through the Padi2U, farmers can access the field data to understand the crop health status online using the Normalised Difference Vegetation Index (NDVI) map derived from the multispectral images. The NDVI is correlated to the Soil Plant Analysis Development (SPAD) value, corresponding to R² = 0.4012. UAT results showed a 100 percent satisfaction score with suggestions were given to enhance the Padi2U performance. It shows that Padi2U can be improved to help farmers in the field monitoring virtually by integrating multispectral imagery and information from the field.

scholarly journals Evidence for genotypic differences among elite lines of common bean in the ability to remobilize photosynthate to increase yield under drought

2016 ◽  
Vol 155 (6) ◽  
pp. 857-875 ◽  
Author(s):  
I. M. RAO ◽  
S. E. BEEBE ◽  
J. POLANIA ◽  
M. GRAJALES ◽  
C. CAJIAO ◽  
...  
Keyword(s):  
Drought Stress ◽  
Grain Yield ◽  
Common Bean ◽  
Harvest Index ◽  
Genotypic Differences ◽  
Area Index ◽  
Wild Bean ◽  
Terminal Drought ◽  
Terminal Drought Stress ◽  
Pod Harvest Index

SUMMARYCommon bean (Phaseolus vulgaris L.) is the most important food legume for human consumption. Drought stress is the major abiotic stress limitation of bean yields in smallholder farming systems worldwide. The current work aimed to determine the role of enhanced photosynthate mobilization to improve adaptation to intermittent and terminal drought stress and to identify a few key adaptive traits that can be used for developing drought-resistant genotypes. Field studies were conducted over three seasons at Centro Internacional de Agricultura Tropical, Palmira, Colombia to determine genotypic differences in adaptation to intermittent (two seasons) and terminal (one season) drought stress compared with irrigated conditions. A set of 36 genotypes, including 33 common bean, two wild bean and one cowpea were evaluated using a 6 × 6 lattice design under irrigated and rainfed field conditions. Three common bean elite lines (NCB 226, SEN 56, SER 125) were identified with superior levels of adaptation to both intermittent and terminal drought stress conditions. The greater performance of these lines under drought stress was associated with their ability to remobilize photosynthate to increase grain yield based on higher values of harvest index, pod harvest index, leaf area index and canopy biomass. Two wild bean germplasm accessions (G 19902, G 24390) showed very poor adaptation to both types of drought stress. One small-seeded black line (NCB 226) was superior in combining greater values of canopy biomass with greater ability to mobilize photosynthates to grain under both types of drought stress. Two small-seeded red lines (SER 78, SER 125) seem to combine the desirable traits of enhanced mobilization of photosynthates to seed with effective use of water through canopy cooling under terminal drought stress. Pod harvest index showed significant positive association with grain yield under both types of drought stress and this trait can be used by breeders as an additional selection method to grain yield in evaluation of breeding populations for both types of drought stress.

Use of normalised difference vegetation index, nitrogen concentration, and total nitrogen content of whole maize plant and plant fractions to estimate yield and nutritive value of hybrid forage maize

2011 ◽  
Vol 62 (5) ◽  
pp. 374 ◽  
Author(s):  
M. R. Islam ◽  
S. C. (Yani) Garcia ◽  
D. Henry
Keyword(s):  
Grain Yield ◽  
Multiple Regression ◽  
Vegetation Index ◽  
Nutritive Value ◽  
Biomass Yield ◽  
Maize Plant ◽  
Forage Maize ◽  
Leaf Stage ◽  
Whole Plant ◽  
Normalised Difference Vegetation Index

This study was conducted to investigate the potentials of normalised difference vegetation index (NDVI), nitrogen (N) concentration (%), and N content (g/plant) of whole maize plant to estimate yield and nutritive value of hybrid forage maize. Hybrid forage maize was grown with two rates of pre-sowing fertiliser N (0, 135 kg/ha) and three rates of post-sowing fertiliser N (0, 79, 158 kg N/ha) applied at the six-leaf stage. Data on the NDVI and N (% and g/plant) of maize were collected at 2-, 3-, 6-, 8-, 12-, 16-, 18-leaf stages and at harvest. Metabolisable energy (ME) content of the whole maize plant at harvest was estimated from in vitro digestibility. Simple, polynomial, and multiple regression analyses were conducted and only the best-fit models were selected. The 8-leaf stage was found to be the most effective stage for use of the NDVI in predicting biomass yield (R2 = 0.81), grain yield (R2 = 0.72), and N (%) (R2 = 0.92) of forage maize. Nitrogen (%) at the 8-leaf stage was also best related to biomass yield (R2 = 0.88). Multiple regressions at the 3-leaf stage increased the coefficient of determination for both biomass yield and grain yield (R2 = 0.77) over the relationships obtained from N (%) of the whole plant at 2- or 3-leaf stage. The NDVI and N (%) of the whole plant at 8-leaf stage were the best predictors of yield, but failed to predict ME content of the hybrid forage maize. Multiple regression models at the 3-leaf stage were almost as effective as the NDVI and N (%) of whole maize plant at the 8-leaf stage in predicting biomass and grain yield of forage maize.

The validation of a model estimating the Leaf Area Index of grasslands in southern China

2013 ◽  
Vol 35 (3) ◽  
pp. 245 ◽  
Author(s):  
Chengming Sun ◽  
Zhengguo Sun ◽  
Tao Liu ◽  
Doudou Guo ◽  
Shaojie Mu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Southern China ◽  
Carbon Sink ◽  
Measurement Data ◽  
Light Transmittance ◽  
Area Index ◽  
Normalised Difference Vegetation Index

In order to estimate the leaf area index (LAI) over large areas in southern China, this paper analysed the relationships between normalised difference vegetation index (NDVI) and the vegetation light transmittance and the extinction coefficient based on the use of moderate resolution imaging spectroradiometer data. By using the improved Beer–Lambert Law, a model was constructed to estimate the LAI in the grassy mountains and slopes of southern China with NDVI as the independent variable. The model was validated with field measurement data from different locations and different years in the grassland mountains and slopes of southern China. The results showed that there was a good correlation between the simulated and observed LAI values, and the values of R2 achieved were high. The relative root mean squared error was between 0.109 and 0.12. This indicated that the model was reliable. The above results provided the theoretical basis for the effective management of the grassland resources in southern China and the effective estimation of grassland carbon sink.

scholarly journals Soybean growth and yield under cover crops

Revista CERES ◽  
2013 ◽  
Vol 60 (2) ◽  
pp. 249-256 ◽  
Author(s):  
Priscila de Oliveira ◽  
Adriano Stephan Nascente ◽  
João Kluthcouski
Keyword(s):  
Grain Yield ◽  
Common Bean ◽  
Pearl Millet ◽  
Cover Crops ◽  
Growth Analysis ◽  
Block Design ◽  
Growth And Yield ◽  
Area Index ◽  
Positive Effects ◽  
Soybean Plants

The use of cover crops in no-tillage systems can provide better conditions for the development of soybean plants with positive effects on grain yield and growth analysis techniques allow researchers to characterize and understand the behavior of soybean plants under different straw covers. Thus, the aim of this study was to characterize, using growth analysis, yield components and agronomic performance of soybean under common bean, Brachiaria brizantha and pearl millet straws. The experiment was performed on a soil under cerrado in the municipality of Santo Antônio de Goiás, GO. The experiment was arranged in a randomized complete block design with three treatments (cover crops) and five replications. Soybean grain yield was lower in the B. brizantha straw treatment (3,708 kg ha-1) than both in the pearl millet (4.772 kg ha-1) and common bean straw treatments (5,200 kg ha-1). The soybean growth analysis in B. brizantha, pearl millet and common bean allowed characterizing the variation in the production of dry matter of leaves, stems, pods and total and leaf area index that provided different grain yields. The cover crop directly affects the soybean grain yield.

scholarly journals Optimization of Nitrogen Rate and Planting Density for Improving the Grain Yield of Different Rice Genotypes in Northeast China

Agronomy ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 555 ◽  
Author(s):  
Chanchan Zhou ◽  
Yuancai Huang ◽  
Baoyan Jia ◽  
Shu Wang ◽  
Fugen Dou ◽  
...  
Keyword(s):  
Grain Yield ◽  
Nitrogen Fertilization ◽  
Unit Area ◽  
Net Photosynthesis ◽  
Planting Density ◽  
Rice Cultivars ◽  
Area Index ◽  
Spad Value ◽  
Spikelets Per Panicle ◽  
Yield Formation

Nitrogen fertilization and planting density are two key factors that can interactively affect the grain yield of rice. Three different types of rice cultivars—inbred Shendao 47, inbred Shendao 505, and hybrid Jingyou 586—were applied to investigate the effects of the nitrogen (N) rate and planting density (D) on the aboveground biomass, harvest index, leaf photosynthetic features, grain yield, and yield components using a split-split-plot design at two sites over two continuous years. The main plots were assigned to four nitrogen fertilizer rates: 0 (N0), 140 (N1), 180 (N2), and 220 (N3) kg ha−1 N; the subplots were assigned to three planting densities: 25 × 104 (D1), 16.7 × 104 (D2), and 12.5 × 104 (D3) hills ha-1, and the sub-subplots were assigned to three rice cultivars. The results showed that the grain yield had a significantly positive correlation with the stomatal conductance (Gs), net photosynthesis rate (Pn), transpiration rate (Tr), chlorophyll content (SPAD value), leaf area index (LAI), panicles per unit area, and spikelets per panicle. The N rate and planting density had significant interaction effects on grain yield, and the maximum values of Shendao 47, Shendao 505, and Jingyou 586 appeared in N3D2, N2D1, and N3D3, respectively. The higher grain yield of midsized panicle Shendao 47 was mostly ascribed to both panicles per unit area and spikelets per panicle. More panicles per unit area and spikelets per panicle primarily contributed to a larger sink capacity of small-sized panicle rice Shendao 505 and large-sized panicle rice Jingyou 586. We found that the treatments N3D2, N2D1, and N3D3 could optimize the contradiction between yield formation factors for Shendao 47, Shendao 505, and Jingyou 586, respectively. Across years and sites, the regression analysis indicated that the combinations of nitrogen fertilization of 195.6 kg ha−1 with a planting density of 22 × 104 hills ha−1, 182.5 kg ha−1 with 25 × 104 hills ha−1, and 220 kg ha−1 with 13.1 × 104 hills ha−1 are recommended for medium-, small-, and large-sized panicle rice cultivars, respectively.

scholarly journals Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1108 ◽  
Author(s):  
Jiayi Zhang ◽  
Xia Liu ◽  
Yan Liang ◽  
Qiang Cao ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Vegetation Index ◽  
Field Experiments ◽  
Growth Parameters ◽  
Leaf Nitrogen ◽  
Crop Growth ◽  
Nitrogen Accumulation ◽  
Wheat Cultivars ◽  
Area Index

Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013–2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68–70%, 10–12%, and 67–73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77–85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.

Effect of Limited Irrigation on Water Consumption and Grain Yield of Spring Maize (Zea Mays)

2013 ◽  
Vol 404 ◽  
pp. 415-419
Author(s):  
Heng Jia Zhang ◽  
Jun Hui Li
Keyword(s):  
Grain Yield ◽  
Soil Water ◽  
Leaf Area ◽  
Water Use ◽  
Water Consumption ◽  
Irrigation Management ◽  
Grain Filling ◽  
Total Water ◽  
Area Index ◽  
Spring Maize

The soil water contents in spring maize field were monitored continuously using soil neutron probe combined with drying-weighing method. Meanwhile, the effect of limited irrigation on crop periodic water consumption and its percentage in total water use, leaf area index, and grain yield of spring maize were explored. The results indicated that both the periodic water consumption and its percentage in total water use varied from low to high then to low within maize growing season, with the maximum valued both at silking to middle grain filling. In addition, leaf area indexes were greatly improved by full irrigation before maize filling, and grain yield was not reduced by efficient limited irrigation management, contrarily, yield increase and 31.1% of significant irrigation water saving were achieved, which was beneficial to the optimization of soil water ecological processing and limited irrigation management.

Prediction of crop productivity and evapotranspiration with two photosynthetic parameter regionalization methods

2012 ◽  
Vol 152 (1) ◽  
pp. 119-133 ◽  
Author(s):  
S. HU ◽  
X. MO
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Census Data ◽  
Wheat Yield ◽  
Ecosystem Model ◽  
Crop Productivity ◽  
Photosynthetic Parameter ◽  
Area Index

SUMMARYParameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.

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