Broadacre crop yield in the lee of windbreaks in the medium and low rainfall areas of south-western Australia

2002 ◽  
Vol 42 (6) ◽  
pp. 739 ◽  
Author(s):  
R. A. Sudmeyer ◽  
M. A. Adams ◽  
J. Eastham ◽  
P. R. Scott ◽  
W. Hawkins ◽  
...  
Keyword(s):  
Crop Yield ◽  
Western Australia ◽  
Crop Yields ◽  
Farming Systems ◽  
Crop Damage ◽  
Yield Data ◽  
Canopy Development ◽  
Documented Information ◽  
Average Rainfall ◽  
Subsequent Crop

In Western Australia, the paucity of documented information detailing crop yield in the lee of windbreaks is a constraint to the adoption of tree windbreaks in dryland farming systems. This paper presents grain yield data for crops growing in the lee of windbreaks in the medium to low rainfall areas of the south-west of Western Australia for 64 field years between 1994 and 1997. Two distinct areas were identified in the lee of windbreaks: a zone of reduced crop yield extending 3–5 times the windbreak height (H) from the trees (competition zone), and a zone of unchanged or improved yield extending 15–20 H (sheltered zone). Yield between 1 and 20 H was less than unsheltered yield in years with average rainfall, similar to unsheltered yield in years, or areas, with low rainfall and higher than unsheltered yield if the unsheltered crop was subjected to sandblasting. Changes in microclimate in shelter appeared to be of benefit in increasing crop yields in drier years or areas. Lupin yield was generally increased in the sheltered zone while cereal yield was generally unchanged. The rate of canopy development may be critical to crop response. In dry years, reduced wind speed in shelter reduced evaporation of soil moisture, increasing the amount of soil water available to establishing crops and reducing sandblasting damage. The principle benefit of windbreaks was their ability to reduce wind erosion and subsequent crop damage. As such, windbreaks are best regarded as a form of insurance.

scholarly journals Geostatistical analysis of crop yield maps in a long term no tillage system

Bragantia ◽  
2010 ◽  
Vol 69 (suppl) ◽  
pp. 9-18 ◽  
Author(s):  
Osvaldo Guedes Filho ◽  
Sidney Rosa Vieira ◽  
Marcio Koiti Chiba ◽  
Célia Regina Grego
Keyword(s):  
Crop Yield ◽  
Crop Yields ◽  
Spherical Model ◽  
Exponential Model ◽  
Spatial And Temporal Variability ◽  
No Tillage ◽  
Yield Data ◽  
The North ◽  
Tillage System

It is known, for a long time, that crop yields are not uniform at the field. In some places, it is possible to distinguish sites with both low and high yields even within the same area. This work aimed to evaluate the spatial and temporal variability of some crop yields and to identify potential zones for site specific management in an area under no-tillage system for 23 years. Data were analyzed from a 3.42 ha long term experimental area at the Centro Experimental Central of the Instituto Agronômico, located in Campinas, Sao Paulo State, Brazil. The crop yield data evaluated included the following crops: soybean, maize, lablab and triticale, and all of them were cultivated since 1985 and sampled at a regular grid of 302 points. Data were normalized and analyzed using descriptive statistics and geostatistical tools in order to demonstrate and describe the structure of the spatial variability. All crop yields showed high variability. All of them also showed spatial dependence and were fitted to the spherical model, except for the yield of the maize in 1999 productivity which was fitted to the exponential model. The north part of the area presented repeated high values of productivity in some years. There was a positive cross correlation amongst the productivity values, especially for the maize crops.

scholarly journals A multi-model analysis of teleconnected crop yield variability in a range of cropping systems

2019 ◽  
Author(s):  
Matias Heino ◽  
Joseph H. A. Guillaume ◽  
Christoph Müller ◽  
Toshichika Iizumi ◽  
Matti Kummu
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Food System ◽  
Crop Yields ◽  
Southern Oscillation ◽  
Yield Variability ◽  
Management Scenarios ◽  
Yield Data ◽  
Climate Oscillations ◽  
Crop Models

Abstract. Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño Southern Oscillation (ENSO), which has been found to impact crop yields in all continents that produce crops, while two other climate oscillations – the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) – have been shown to impact crop production especially in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD and NAO on the growing conditions of maize, rice, soybean and wheat at the global scale, by utilizing crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that simulated crop yield variability is correlated to climate oscillations to a wide extent (up to almost half of all maize and wheat harvested areas for ENSO) and in several important crop producing areas, e.g. in North America (ENSO, wheat), Australia (IOD & ENSO, wheat) and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed, and fully fertilized scenarios, while the sensitivity tends to be lower if crops are fully irrigated. Since, the development of ENSO, IOD and NAO can be reliably forecasted in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate related shocks.

Comparative soil water, pasture production, and crop yields in phase farming systems with lucerne and annual pasture in Western Australia

2001 ◽  
Vol 52 (2) ◽  
pp. 295 ◽  
Author(s):  
R. A. Latta ◽  
L. J. Blacklow ◽  
P. S. Cocks
Keyword(s):  
Soil Water ◽  
Western Australia ◽  
Oil Content ◽  
Field Experiments ◽  
Crop Yields ◽  
Wheat Yield ◽  
Farming Systems ◽  
Yield Response ◽  
Grain Protein ◽  
Pasture Production

Two field experiments in the Great Southern region of Western Australia compared the soil water content under lucerne (Medicago sativa) with subterranean clover (Trifolium subterranean) and annual medic (Medicago polymorpha) over a 2-year period. Lucerne depleted soil water (10–150 cm) between 40 and 100 mm at Borden and 20 and 60 mm at Pingrup compared with annual pasture. There was also less stored soil water after wheat (Triticum aestivum) and canola (Brassica napus) phases which followed the lucerne and annual pasture treatments, 30 and 48 mm after wheat, 49 and 29 mm after canola at Borden and Pingrup, respectively. Lucerne plant densities declined over 2 seasons from 35 to 25 plants/m2 (Borden) and from 56 to 42 plants/m2 (Pingrup), although it produced herbage quantities similar to or greater than clover/medic pastures. The lucerne pasture also had a reduced weed component. Wheat yield at Borden was higher after lucerne (4.7 t/ha) than after annual pasture (4.0 t/ha), whereas at Pingrup yields were similar (2 t/ha) but grain protein was higher (13.7% compared with 12.6%) . There was no yield response to applied nitrogen after lucerne or annual pasture at either site, but it increased grain protein at both sites. There was no pasture treatment effect on canola yield or oil content at Borden (2 t/ha, 46% oil). However, at Pingrup yield was higher (1.5 t/ha compared to 1.3 t/ha) and oil content was similar (41%) following lucerne–wheat. The results show that lucerne provides an opportunity to develop farming systems with greater water-use in the wheatbelt of Western Australia, and that at least 2 crops can be grown after 3 years of lucerne before soil water returns to the level found after annual pasture.

The tree - crop interface: the effects of root pruning in south-western Australia

2002 ◽  
Vol 42 (6) ◽  
pp. 763 ◽  
Author(s):  
R. A. Sudmeyer ◽  
D. J. M. Hall ◽  
J. Eastham ◽  
M. A. Adams
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Crop Yield ◽  
Western Australia ◽  
Tree Growth ◽  
Crop Yields ◽  
Soil Surface ◽  
Root Pruning ◽  
Tree Roots

This paper examines the effect severing lateral tree roots (root pruning) has on crop and tree growth and soil water content at 2 sites in the south-west of Western Australia. Crop and tree growth and soil water content were assessed in a Pinus pinaster windbreak system growing on 0.45–1.00 m of sand over clay, and crop growth was assessed adjacent to Eucalyptus globulus windbreaks growing on 4–5 m of sand. Crop yield was depressed by 23–52% within 2.5 times the tree height (H) of unpruned pines and by 44% within 2.5 H of pruned eucalypts. Depressed yields made cropping uneconomical within 1.5 H of the eucalypts and 1 H of the pines. Root pruning most improved crop yields where lateral tree roots were confined close to the soil surface and decreased in effectiveness as the depth to confining layer (clay) increased. Crop losses within 2.5 H of the pines were reduced from 39 to 14% in the year the trees were root pruned and were 25% 1 year after root pruning. Subsequent root pruning of the eucalypts did not improve crop yield. While root pruning severed lateral pine roots, tree growth was not significantly reduced. The principal cause of reduced crop yield near the trees appeared to be reduced soil moisture in the area occupied by tree roots. Competition for nutrients and light appeared to have little effect on crop yield. Root pruning can spatially separate tree and crop roots where the tree roots are confined close to the surface, and significantly improve crop yields without reducing tree growth.

scholarly journals Novel Approach for Forecasting Sugarcane Crop Yield: A Real-Time Prediction

2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Ankit Kumar ◽  
Anil Kumar Kapil
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Yields ◽  
Yield Data ◽  
Sugarcane Crop ◽  
Use Of Data ◽  
Novel Approach ◽  
Technological Advances ◽  
Plant Area ◽  
Cultivation Technique

Agriculture is an environment in which there is considerable confusion. Crop development depends largely on several variables, including climate, temperature, genetics, politics and economics. In addition, a huge number of raw agriculture statistics are available, but study of those details for estimating crop yield is quite challenging. The most challenging job is therefore to include accurate details and awareness about the raw farm data. In order to evaluate cultivation yield, data mining could customize data expertise. The objective of this study was to predict crop yields through the use of data mining technological advances. Moreover, this paper compared various classification algorithms and it is expected that the results of the study may enhance the actual yields of sugarcane in a wide number of tropical fields. The specifications used in the forecast were plot (soil size, plant area, rain distance, previous year's plant yield), sugar-cane characteristics (cane class and sort), crop cultivation procedure (normal water resource size, cultivation technique, disease management process, sort / procedure of fertilizer) as well as rain quantity.

Climate change impacts on crop water productivity in Africa using a multi-model inter-comparison

2020 ◽  
Author(s):  
Imeshi Weerasinghe ◽  
Celray James Chawanda ◽  
Ann van Griensven
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Water Productivity ◽  
Baseline Scenario ◽  
Crop Water ◽  
Yield Data ◽  
Water Vapour Flux ◽  
Crop Models ◽  
Crop Water Productivity

<p>Evapotranspiration (ET) or the water vapour flux is an important component in the water cycle and is widely studied due to its implications in disciplines ranging from hydrology to agricultural and climate sciences. In the recent past, growing attention has been given to estimating ET fluxes at regional and global scales. However, estimation of ET at large scales has been a difficult task due to direct measurement of ET being possible only at point locations, for example using flux towers. For the African continent, only a limited number of flux tower data are openly available for use, which makes verification of regional and global ET products very difficult. Recent advances in satellite based products provide promising data to fill these observational gaps.</p><p>In this study we propose to investigate the Climate Change (CC) impact on crop water productivity across Africa using ET and crop yield predictions of different crop models for future climate scenarios. Different model outputs are evaluated including models from both the ISI-MIP 2a and 2b protocols. Considering the problem of direct observations of ET being difficult to obtain, especially over Africa, we use ET estimates from several remotely sensed derived products as a references to evaluate the crop models (maize) in terms of magnitude, spatial patterns and variations between models. The crop model results for crop yield are compared to FAO reported crop yields at country scale. The results show a very strong disagreement between the different crop models of the baseline scenario and when compared with ET and crop yield data.  Also, a very large uncertainty is obtained for the climate change predictions. It is hence recommended to improve the crop models for application in Africa.</p>

scholarly journals Implementation of Effective Crop Selection by Using the Random Forest Algorithm

2018 ◽  
Vol 7 (3.34) ◽  
pp. 287
Author(s):  
R Karthikeyan ◽  
M Gowthami ◽  
A Abhishhek ◽  
P Karthikeyan
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Crop Yields ◽  
Learning Algorithm ◽  
Yield Data ◽  
Crop Selection ◽  
Efficient Machine ◽  
Performance Statistics ◽  
Model Training ◽  
Large Crop

Accurate predictions of crop yield are critical for developing agriculture. We have provided a machine-learning method, Random Forests which has a ability to predict crop yield corresponds to the current climate and biophysical change. We have collected a large crop yield data from various sources. These data are used for both for the model training and testing.RF was found huge capable of predicting crop yields and over performed MLR standards in every performance statistics that were compared. From various results that shows that RF is an efficient machine-learning algorithm for crop predictions at current condition and has a huge accuracy in data analysis.  

scholarly journals The impact of drought on the productivity of two rainfed crops in Spain

2019 ◽  
Author(s):  
Marina Peña-Gallardo ◽  
Sergio Martín Vicente-Serrano ◽  
Fernando Domínguez-Castro ◽  
Santiago Beguería
Keyword(s):  
Crop Yield ◽  
Crop Yields ◽  
Spatial Scales ◽  
Standardized Precipitation Index ◽  
Yield Response ◽  
Drought Indices ◽  
Economic Sectors ◽  
Yield Data ◽  
Agriculture Sector ◽  
The Impact

Abstract. Drought events are of great importance in most Mediterranean climate regions because of the diverse and costly impacts they have in various economic sectors and on the environment. The effects of this natural hazard on rainfed crops are particularly evident. In this study the impacts of drought on two representative rainfed crops in Spain (wheat and barley) were assessed. As the agriculture sector is vulnerable to climate, it is especially important to identify the most appropriate tools for monitoring the impact of the weather on crops, and particularly the impact of drought. Drought indices are the most effective tool for that purpose. Various drought indices have been used to assess the influence of drought on crop yields in Spain, including the standardized precipitation and evapotranspiration index (SPEI), the standardized precipitation index (SPI), the Palmer drought indices (PDSI, Z-Index, PHDI, PMDI), and the standardized Palmer drought index (SPDI). Two sets of crop yield data at different spatial scales and temporal periods were used in the analysis. The results showed that drought indices calculated at different time scales (SPI, SPEI) most closely correlated with crop yield. The results also suggested that different patterns of yield response to drought occurred depending on the region, period of the year, and the drought time scale. The differing responses across the country were related to season and the magnitude of various climate variables.

scholarly journals Design of ANN Based Machine Learning Method for Crop Prediction

2021 ◽  
Vol 3 (3) ◽  
pp. 223-239
Author(s):  
S. Sairamkumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Crop Yield ◽  
Crop Yields ◽  
Farming Systems ◽  
Operating Costs ◽  
Yield Prediction ◽  
Yield Efficiency ◽  
Learning Technique ◽  
Significant Effort

In agriculture, crop yield estimation is critical. Remote sensing is being used in farming systems to increase yield efficiency and lower operating costs. Remote sensing-based strategies, on the other hand, necessitate extensive processing, necessitating the use of machine learning models for crop yield prediction. Descriptive analytics is a form of analytics that is used to accurately estimate crop yields. This paper discusses the most recent research on machine learning-based strategies for efficient crop yield prediction. In general, the training model's accuracy should be higher, and the error rate should be low. As a result, significant effort is being put forward to propose a machine learning technique that will provide high precision in crop yield prediction.

scholarly journals A multi-model analysis of teleconnected crop yield variability in a range of cropping systems

2020 ◽  
Vol 11 (1) ◽  
pp. 113-128 ◽  
Author(s):  
Matias Heino ◽  
Joseph H. A. Guillaume ◽  
Christoph Müller ◽  
Toshichika Iizumi ◽  
Matti Kummu
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Food System ◽  
Crop Yields ◽  
Southern Oscillation ◽  
Yield Variability ◽  
Management Scenarios ◽  
Yield Data ◽  
Climate Oscillations ◽  
Crop Models

Abstract. Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño–Southern Oscillation (ENSO), which has been found to impact crop yields on all continents that produce crops, while two other climate oscillations – the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) – have been shown to especially impact crop production in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD, and NAO on the growing conditions of maize, rice, soybean, and wheat at the global scale by utilising crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that, while accounting for their potential co-variation, climate oscillations are correlated with simulated crop yield variability to a wide extent (half of all maize and wheat harvested areas for ENSO) and in several important crop-producing areas, e.g. in North America (ENSO, wheat), Australia (IOD and ENSO, wheat), and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed and fully fertilised scenarios, while the sensitivity tends to be lower if crops were to be fully irrigated. Since the development of ENSO, IOD, and NAO can potentially be forecasted well in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate-related shocks.

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