2006-06-12, Miah, M A B, Rahman, M M, Emon, M S H, Islam, M A

This study was conducted during the period from June to November, 2004 with a view to study the effect of storage relative humidity (RH) on germination and vigour of wheat seed. Four different storage relative humidities (viz. 50%, 60%, 70% and 80%) and two crop varieties (wheat cv. Satabdi and wheat cv. Gourab) were used as treatment variables. Seed moisture content (SMC), germination and vigour test of seeds were done at one months interval starting from 10 July to 10 November 2004, while the initial test for SMC and germination was done on 10 June, 2004. The results showed that storage relative humidity, crop variety and their interaction significantly influenced the seed moisture content, germination and vigour index. The moisture contents of wheat seed in storage were found between 7.9-8.8, 9.5-10.0, 12.0-12.5 and 15.3-16.0%, respectively for 50, 60, 70 and 80% RH against their initial moisture content of 8.4-9.5% just before storage. The germination and vigour of all the varieties decreased with increases RH. For each of the crop variety, the highest germination was obtained at 50% RH. No seed germination was occurred in any of the variety stored at 80% RH after two months of storage. More than 92% seed germination was recorded from all the varieties after 6 months of storage. The seeds kept at 70% RH showed 83% germination in July 2004 that reduced to 74% in November.

2018, Alam, Muhammad Shahinur, Lamb, David W, Rahman, Muhammad Moshiur

Evapotranspiration (ET) is the total amount of water released by a crop or pasture canopy in the form of Transpiration (T) and Evaporation (E). ET accounts for up to 96% of the water loss depending on the types of vegetation cover and climatic conditions (Wilcox et al., 2003). Transpiration is related to the productivity of crops and pasture; whereas, evaporation is the loss of water directly from soil surface. Estimating separately the components of ET in the field is challenging yet it is highly significant in terms of improving crop water use and irrigation efficiency, since a anywhere between 30 and 80% of the water flux can be associated with the all important evaporation component (Wilcox et al., 2003). Sap flow monitoring, using micro-lysimeters or isotopic analysis are the usual options for separately measuring transpiration and evaporation in plants but these are incompatible with in-situ field deployment. In this study a portable and convenient method for separately determining the evapotranspiration components has been developed. When coupled with widely-used active optical sensors, the device can be used to develop, in-situ, relationships between spectro-optical indices such as NDVI and evapotranspiration coefficients (Kc, Kcb and Ke) that characterize actual ET water loss relative to evaporative demand.

2020-02-07, Alam, Muhammad Shahinur, Lamb, David, Mccarthy, Cheryl, Rahman, Muhammad, Warwick, Nigel William

Knowledge of crop evapotranspiration is crucial for irrigation decision making. An appropriate, user-friendly and time-efficient means of inferring such information is therefore essential. In this study, a closed hemispherical chamber was instrumented, calibrated and deployed in the field for measuring actual evapotranspiration of a vital pasture species, Tall Fescue (Festuca arundinacea). The pasture crop coefficient (K_c) was calculated from the measured instantaneous evapotranspiration and reference crop evapotranspiration (ET_o) for a range of growth stages. Also the relationship between K_c and Normalized Difference Vegetation Index (NDVI) as measured using an active optical sensor was established. Using the FAO dual crop coefficient approach and the hemispherical chamber, a technique for partitioning evapotranspiration components was developed. The components of evapotranspiration in terms of basal crop coefficient (K_cb) and soil evaporation coefficient (K_e) were expressed relative to canopy NDVI and Leaf Area Index (LAI). A theoretical model for estimating transpiration was also developed by scaling up stomatal conductance to canopy level in a controlled glasshouse environment. The model was validated against the measured transpiration.

2016, Rahman, Muhammad Moshiur, Lamb, David

The normalized difference vegetation index (NDVI), derived from ground based or satellite borne, passive sensors is often used to estimate the fraction of absorbed photosynthetically active radiation (fAPAR) of a plant canopy. It is well documented that the measured NDVI from passive sensors is affected by the sun and/or view geometry due to the non-Lambertian properties of plant canopies. Despite this the fAPAR-NDVI relationships are often found to be independent of the solar elevation angle (ᶿs) because the ᶿs-dependent absorption of the Red wavelengths within the canopy, which dominates the fAPAR, cancels out the ᶿs-dependency of the NIR scattering which dominates the NDVI measurement. Active optical sensors (AOS), which have their own illuminating light source measure NDVI (NDVI AOS) without any interference of solar geometry. However as fAPAR of a plant canopy does change with solar elevation angle (ᶿs), the fAPAR-NDVIAOS relationship too changes with varying ᶿs. The objective of this study was to explore a correction factor which can eliminate the ᶿs-dependency in fAPAR-NDVIAOS relationship. Data were collected using LightScout quantum bar and CropCircle™ for Tall fescue ('Festuca arundinacea' var. Fletcher) at ᶿs ranging from 40° to 80°. A ᶿs-dependent vegetation index, NDVI*AOS that introduces simple trigonometric correction factors to the measured Red and NIR irradiance for nadir-viewing active optical sensor provides a fAPAR-NDVI relationship that is independent of ᶿs. When the solar elevation angle is introduced this way into the NDVIAOS the fAPAR can then be calculated from the NDVIAOS for any solar elevation angle within the range of 40-80°.

2020-01-08, Alam, Muhammad Shahinur, Lamb, David, McIntyre, Cheryl, Rahman, Muhammad, Warwick, Nigel William

Knowledge of crop evapotranspiration is crucial for irrigation decision making. An appropriate, user-friendly and time-efficient means of inferring such information is therefore essential. In this study, a closed hemispherical chamber was instrumented, calibrated and deployed in the field for measuring actual evapotranspiration of a vital pasture species, Tall Fescue (Festuca arundinacea). The pasture crop coefficient (Kc) was calculated from the measured instantaneous evapotranspiration and reference crop evapotranspiration (ETo) for a range of growth stages. Also the relationship between Kc and Normalized Difference Vegetation Index (NDVI) as measured using an active optical sensor was established. Using the FAO dual crop coefficient approach and the hemispherical chamber, a technique for partitioning evapotranspiration components was developed. The components of evapotranspiration in terms of basal crop coefficient (Kcb) and soil evaporation coefficient (Ke) were expressed relative to canopy NDVI and Leaf Area Index (LAI). A theoretical model for estimating transpiration was also developed by scaling up stomatal conductance to canopy level in a controlled glasshouse environment. The model was validated against the measured transpiration.

, Alam, Muhammad Shahinur, Lamb, David William, Rahman, Muhammad Moshiur, Randall, Marcus

To establish an indirect method for estimating and partitioning pasture evapotranspiration, it is vital to develop a direct reference method that aligns with the required temporal and spatial resolution. An evapotranspiration chamber offers an effective solution as it is easy to deploy and operates at an appropriate measurement scale. In this study, we prepared and tested a closed hemispherical chamber for on-site measurements of evaporation and/ or transpiration. Advanced data monitoring and logging techniques were integrated to enhance the precision and reliability of direct in-field evapotranspiration measurements. During laboratory testing, vapor accumulation within the chamber was monitored to identify the best representative segment of the vapor accumulation curve. Results indicated that the instrument stabilizes its readings within 5 to 10 s post-deployment in laboratory settings. The subsequent 15 s produce stable readings that best represent actual vapor accumulation. The optimal fan speed, producing an air speed of 5.36 ms^{− 1} at the vicinity of the fan within the chamber, paired with a wire mesh above the vapor-producing surface, yielded the best results. The study established a calibration factor (C) of 1.02 based on the actual water loss and vapor accumulation readings from the sensors at this fan speed. Advanced data analytics were applied to derive the calibration factor and to calculate the values of evapotranspiration from the changing microclimate within the chamber. Direction towards complete automation and the limitations of the chamber in field measurement are provided. The chamber was also tested under field conditions, and the paper examines its practical application and necessary adjustments for field measurements.

2016, Robson, Andrew, Rahman, Muhammad Moshiur, Muir, Jasmine, Saint, Ashley, Simpson, Chad, Searle, Chris

Accurate yield forecasting in high value fruit tree crops provides vital management information to growers as well as supporting improved decision making, including postharvest handling, storage and forward selling. Current research evaluated the 8 spectral band WorldView 3 (WV-3) with a spatial resolution of 1.2 m, as a tool for exploring the relationship between individual tree canopy reflectance and a number of tree growth parameters, including yield. WV-3 imagery was captured on the 7th of April, 2016, over two Macadamia ('Macadamia integrifolia') and three Avocado ('Persea americana') orchards growing near the Queensland township of Bundaberg, Australia. Using the extent of each block, the WV-3 imagery was sub-setted and classified into 8 Normalised Difference Vegetation index (NDVI) classes. From these classes 6 replicate trees were selected to represent high, medium and low NDVI regions (n=18) and subsequently ground truthed for a number of yield parameters during April and May, 2016. The measured parameters were then correlated against 20 structural and pigment based vegetation indices derived from the 8 band spectral information corresponding to each individual tree canopy (12.6 m2). The results identified a positive relationship between derived vegetation indices (VI) and fruit weight (kg/tree) R2 > 0.69 for Macadamia and R2 > 0.68 for Avocado; and fruit number R2 > 0.6 for Macadamia and R2 > 0.61 for Avocado. The algorithm derived between the optimum VI and yield for each block was then applied across the entire block to derive a yield map. The results show that remote sensing of tree canopy condition can be used to measure yield parameters in Macadamia and Avocado grown in the Bundaberg region.

2015, Rahman, Muhammad

In arable farming (e.g. sugar-beet, onion, potato, carrot, etc.), plant root activities primarily occur within the top 0.3 m of soil layer. Therefore, measurement of soil spatial variability and identifying field-scale heterogeneities of this top layer is very important from the perspective of site specific crop management. Apparent soil electrical conductivity (EC_a), which is related to different soil physical properties, such as clay content, moisture content, bulk density, pH and salinity, can be used to determine the soil spatial variability in a convenient way. Electromagnetic induction based EM38 instrument when placed on ground can measure soil EC_a upto a depth of 0.75 m in horizontal dipole mode and 1.5 m in vertical dipole mode. Numerous researchers used EM38 to measure depth weighted EC_a for discrete soil depth intervals; however, there is no simple technique that can be used to measure EC_a in top 0.3 m soil layer. To visualize EC_a variations in a field, EC_a mapping is also an important aspect of precision agriculture. Furthermore, the accuracy of interpolation methods for spatially varying soil properties has been analysed in several studies. However, a large discrepancy exists among the findings of the researchers.
The objectives of this study were to find a simple method to measure root-zone (top 0.3m) soil EC_a with the help of an EM38 and to check if it can be representative of the measured soil physical properties. Further intent of this study was to investigate different deterministic, geo-statistical and hybrid interpolation techniques for EC_a and soil properties mapping. To evaluate the accuracy of maps using relatively a fine (e.g. 10.5x10.5 m) and a coarse (21x21 m) grid size was also a sub-objective of this study.
A simple method has been developed based on the electromagnetic induction theory to determine the EC_a of root-zone soil layer. The EM38 was placed in both horizontal and vertical modes at 0, 15, 30. 60, 90 and 120 cm above soil surface to get the depth weighted EC_a. From this depth weighted EC_a profile, root-zone EC_a was calculated. Two equations were derived for both horizontal and vertical dipole modes. The EC_a measured from top 0.3 m in horizontal and vertical dipole modes was correlated with soil physical properties such as, clay content, moisture content, bulk density, pH and electrical conductivity. Mapping techniques investigated in this study were comprised on deterministic (inverse distance weighting, spline), geostatistical (ordinary kriging, universal kriging) and hybrid (Co-kriging) interpolation techniques. Root mean square error (RMSE) was used to compare the accuracy of different interpolation techniques for EC_a and different soil properties mapping. To evaluate the accuracy of maps between a fine and a coarse grid size, cross validation technique was used.
In horizontal dipole mode, about 57.70, 19.00, 11.15 and 7.27% response was calculated whereas in vertical dipole mode 50.20, 19.60, 13.40 and 9.25% response was calculated from 0-0.3, 0.3-0.6, 0.6-0.9 and 0.9-1.2 m depth respectively. As EC_a in horizontal mode can give better response from top soil, therefore, 57.70% of EC_a (H) is the root-zone EC_a. Positive correlation between EC_a and clay content, MC, pH and BD conclude that these soil properties contribute to soil EC_a. All techniques showed comparable results, however, Co-kriging outperformed slightly over other techniques. Ordinary kriging gave better predictions for clay (RMSE = 2.03) whereas universal kriging interpolated pH with lowest RMSE (RMSE = 0.226). The fine grid size (10.Sm) gave better result (R² = 0.67) than a coarse grid size (R² = 0.55).
Our technique is very simple and can be used easily by taking a few EM38 readings at various height above ground on a field level. Different relations can be developed for different types of soils with varying soil properties. Root-zone EC_a measurement and mapping can be used for better soil management in arable farming.

2014, Rahman, Muhammad Moshiur, Lamb, David, Stanley, John, Trotter, Mark

Monitoring pasture growth rate is an important component of managing grazing livestock production systems. In this study, we demonstrate that a pasture growth rate (PGR) model, initially designed for NOAA AVHRR normalised difference vegetation index (NDVI) and since adapted to MODIS NDVI, can provide PGR at spatial resolution of ~2 m with an accuracy of ~2 kg DM/ha.day when incorporating in-situ sensor data. A PGR model based on light-use efficiency (LUE) was combined with 'in-situ' measurements from proximal weather (temperature), plant (fraction of absorbed photosynthetically active radiation, fAPAR) and soil (relative moisture) sensors to calculate the growth rate of a tall fescue pasture. Based on an initial estimate of LUEmax for the candidate pasture, followed by a process of iterating LUEmax to reduce prediction errors, the model was capable of estimating PGR with a root mean square error of 1.68 kg/ha.day (R² = 0.96, P-value ≈ 0). The iterative process proved to be a convenient means of estimating LUE of this pasture (1.59 g DM/MJ APAR) under local conditions. The application of the LUE-PGR approach to developing an in-situ pasture growth rate monitoring system is discussed.

2018, Muir, Jasmine, Robson, Andrew, Rahman, M M

Satellite imagery has been demonstrated to be an effective technology for producing accurate pre-harvest estimates in many agricultural crops. For Australian sugarcane, yield forecasting models have been developed from a single date SPOT satellite image acquired around peak crop growth. However, a failure to acquire a SPOT image at this critical growth stage, from continued cloud cover or from competition for the satellite, can prevent an image being captured and therefore a forecast being made for that season. In order to reduce the reliance on a single image capture and to improve the accuracies of the forecasts themselves, time series yield prediction models have been developed for eight sugarcane growing regions using multiple years of free Landsat satellite images. In addition to the forecasting of average regional yield, an automated computational and programming procedure enabling the derivation of crop vigour variability (GNDVI) maps from the freely available Sentinel 2 satellite imagery was developed. These maps, produced for 15 sugarcane growing regions during the 2017 growing season, identify both variations in crop vigour across regions and within every individual crop. These outputs were made available to collaborating mills within each growing region. This paper presents the accuracies achieved from the time series yield forecasting models versus actual 2017 yields for the respective regions, as well as provides an example of the derived mapping outputs.