scholarly journals Climate Variability and Sugarcane Yield in Louisiana

2005 ◽  
Vol 44 (11) ◽  
pp. 1655-1666 ◽  
Author(s):  
David Greenland

Abstract This paper seeks to understand the role that climate variability has on annual yield of sugarcane in Louisiana. Unique features of sugarcane growth in Louisiana and nonclimatic, yield-influencing factors make this goal an interesting and challenging one. Several methods of seeking and establishing the relations between yield and climate variables are employed. First, yield–climate relations were investigated at a single research station where crop variety and growing conditions could be held constant and yield relations could be established between a predominant older crop variety and a newer one. Interviews with crop experts and a literature survey were used to identify potential climatic factors that control yield. A statistical analysis was performed using statewide yield data from the American Sugar Cane League from 1963 to 2002 and a climate database. Yield values for later years were adjusted downward to form an adjusted yield dataset. The climate database was principally constructed from daily and monthly values of maximum and minimum temperature and daily and monthly total precipitation for six cooperative weather-reporting stations representative of the area of sugarcane production. The influence of 74 different, though not independent, climate-related variables on sugarcane yield was investigated. The fact that a climate signal exists is demonstrated by comparing mean values of the climate variables corresponding to the upper and lower third of adjusted yield values. Most of these mean-value differences show an intuitively plausible difference between the high- and low-yield years. The difference between means of the climate variables for years corresponding to the upper and lower third of annual yield values for 13 of the variables is statistically significant at or above the 90% level. A correlation matrix was used to identify the variables that had the largest influence on annual yield. Four variables [called here critical climatic variables (CCV)], mean maximum August temperature, mean minimum February temperature, soil water surplus between April and September, and occurrence of autumn (fall) hurricanes, were built into a model to simulate adjusted yield values. The CCV model simulates the yield value with an rmse of 5.1 t ha−1. The mean of the adjusted yield data over the study period was 60.4 t ha−1, with values for the highest and lowest years being 73.1 and 50.6 t ha−1, respectively, and a standard deviation of 5.9 t ha−1. Presumably because of the almost constant high water table and soil water availability, higher precipitation totals, which are inversely related to radiation and temperature, tend to have a negative effect on the yields. Past trends in the values of critical climatic variables and general projections of future climate suggest that, with respect to the climatic environment and as long as land drainage is continued and maintained, future levels of sugarcane yield will rise in Louisiana.

Author(s):  
Andro M. Enovejas ◽  
◽  
Sharmaine Maldia ◽  
Nurul Amri Komarudin ◽  
Dante Gideon K. Vergara ◽  
...  

Climate variability is one of the factors that directly and greatly affect cropping system and plant yield. It is therefore very important to obtain a good understanding about climate variability or changes in the climate and the effect of these changes to clearly understand the vulnerability of food crops as well as its agronomic impacts for us to create and implement adaptive strategies to mitigate its negative effects. This study assessed the effect of climate in rice crop yield in both irrigated and rainfed ecotype farming system in Nueva Ecija Province in the Philippines using semi-annual yield data and the different climate variables such as seasonal rainfall, mean temperature, minimum temperature, and relative humidity by using empirical/statistical method through time series analysis, and correlation analysis. Results indicated that rice yield for irrigated and rainfed ecosystem type of farming in Nueva Ecija show an overall increasing trend from year 1991-2018, although there are observed decline and fluctuations in some years. The different climate variables (i.e., rainfall, temperature, and humidity) show fluctuating trends and irregularities spanning from the year 1991-2018. But it showed overall decreasing trends for relative humidity and increasing trends for rainfall, minimum temperature, and mean temperature. There are significant correlations between rice yield the all the climate variables in both irrigated and rainfed farming ecosystem types.


2019 ◽  
Vol 7 (2) ◽  
pp. 11
Author(s):  
Ebrima Sonko ◽  
Sampson K. Agodzo ◽  
Philip Antwi-Agyei

Climate change and variability impact on staple food crops present a daunting challenge in the 21st century. The study assesses future climate variability on maize and rice yield over a 30-year period by comparing the outcomes under two GCM models, namely, CSIRO_RCP4.5 and NOAA_RCP4.5 of Australia’s Commonwealth Scientific and National Oceanic and Atmospheric Administration respectively. Historical climate data and yield data were used to establish correlations and then subsequently used to project future yields between 2021 and 2050. Using the average yield data for the period 1987-2016 as baseline yield data, future yield predictions for 2021-2030, 2031-2040 and 2041-2050 were then compared with the baseline data. The results showed that the future maize and rice yield would be vulnerable to climate variability with CSIRO_RCP4.5 showing increase in maize yield whilst CSIRO_RCP4.5 gives a better projection for rice yield. Furthermore, the results estimated the percentage mean yield gain for maize under CSIRO_RCP4.5 and NOAA_ RCP4.5 by about 17 %, 31 % and 48 % for the period 2021-2030, 2031-2040 and 2041-2050 respectively. Mean rice yield lossess of -23 %, -19 % and -23 % were expected for the same period respectively. The study recommended the use of improved rice and maize cultivars to offset the negative effects of climate variability in future.


Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 82 ◽  
Author(s):  
Youcai Kang ◽  
Jianen Gao ◽  
Hui Shao ◽  
Yuanyuan Zhang

Climate and land-use change are the two main driving forces that affect watershed hydrological processes. Separately assessing their impacts on hydrology is important for land-use planning and water resource management. In this research, the SWAT (Soil and Water Assessment Tool) and statistical methods were applied to evaluate the effects of climate and land-use change on surface hydrology in the hilly-gully region of the Loess Plateau. The results showed that surface runoff and soil water presented a downward tendency, while evapotranspiration (ET) presented an upward tendency in the Yanhe watershed from 1982 to 2012. Climate is one the dominant factors that influence surface runoff, especially in flooding periods. The average contribution rate of surface runoff on stream flow accounted for 55%, of which the flooding period accounted for 40%. The runoff coefficient declined by 0.21 after 2002 with the land-use change of cropland transformed to grassland and forestland. The soil water exhibited great fluctuation along the Yanhe watershed. In the upstream region, the land-use was the driving force to decline soil water, which reduced the soil water by 51%. Along the spatial distribution, it converted from land-use change to climate variability from northwest to southeast. The ET was more sensitive to land-use change than climate variability in all sub-basins, and increased by 209% with vegetation restoration. To prevent the ecosystem degradation and maintain the inherent ecological functions of rivers, quantitative assessment the influence of climate variability and land-use change on hydrology is of great importance. Such evaluations can provide insight into the extent of land use/cover change on regional water balance and develop appropriate watershed management strategies on the Loess Plateau.


2020 ◽  
Author(s):  
Danilo Rabino ◽  
Marcella Biddoccu ◽  
Giorgia Bagagiolo ◽  
Guido Nigrelli ◽  
Luca Mercalli ◽  
...  

<p>Historical weather data represent an extremely precious resource for agro-meteorology for studying evolutionary dynamics and for predictive purposes, to address agronomical and management choices, that have economic, social and environmental effect. The study of climatic variability and its consequences starts from the observation of variations over time and the identification of the causes, on the basis of historical series of meteorological observations. The availability of long-lasting, complete and accurate datasets is a fundamental requirement to predict and react to climate variability. Inter-annual climate changes deeply affect grapevine productive cycle determining direct impact on the onset and duration of phenological stages and, ultimately, on the grape harvest and yield. Indeed, climate variables, such as air temperature and precipitation, affect evapotranspiration rates, plant water requirements, and also the vine physiology. In this respect, the observed increase in the number of warm days poses a threat to grape quality as it creates a situation of imbalance at maturity, with respect to sugar content, acidity and phenolic and aromatic ripeness.</p><p>A study was conducted to investigate the relationships between climate variables and harvest onset dates to assess the responses of grapevine under a global warming scenario. The study was carried out in the “Monferrato” area, a rainfed hillslope vine-growing area of NW Italy. In particular, the onset dates of harvest of different local wine grape varieties grown in the Vezzolano Experimental Farm (CNR-IMAMOTER) and in surrounding vineyards (affiliated to the Terre dei Santi Cellars) were recorded from 1962 to 2019 and then related to historical series of climate data by means of regression analysis. The linear regression was performed based on the averages of maximum and minimum daily temperatures and sum of precipitation (1962–2019) calculated for growing and ripening season, together with a bioclimatic heat index for vineyards, the Huglin index. The climate data were obtained from two data series collected in the Experimental farm by a mechanical weather station (1962-2002) and a second series recorded (2002-2019) by an electro-mechanical station included in Piedmont Regional Agro-meteorological Network. Finally, a third long-term continuous series covering the period from 1962 to 2019, provided by Italian Meteorological Society was considered in the analysis.</p><p>The results of the study highlighted that inter-annual climate variability, with a general positive trend of temperature, significantly affects the ripening of grapes with a progressive anticipation of the harvest onset dates. In particular, all the considered variables excepted precipitation, resulted negatively correlated with the harvest onset date reaching a high level of significance (up to P< 0.001). Best results have been obtained for maximum temperature and Huglin index, especially by using the most complete dataset. The change ratios obtained using datasets including last 15 years were greater (in absolute terms) than results limited to the period 1962-2002, and also correlations have greater level of significance. The results indicated clearly the relationships between the temperature trend and the gradual anticipation of harvest and the importance of having long and continuous historical weather data series available.</p>


2017 ◽  
Vol 145 (3-4) ◽  
pp. 335-349 ◽  
Author(s):  
M. Jayakumar ◽  
M. Rajavel ◽  
U. Surendran ◽  
Girish Gopinath ◽  
K. Ramamoorthy

1995 ◽  
Vol 31 (3) ◽  
pp. 299-306 ◽  
Author(s):  
R. E. Kamidi

SummaryLinear covariance analysis of crop yield data using plot stand as the covariate is not satisfactory when the missing plants are eliminated before maturity. This is because the resulting relationship between yield and plot stand is non-linear. In this paper, a transformation of plot yield data to adjust for sub-optimal stands in circumstances involving interplant competition is examined. A biomathematical derivation and interpretation of the proposed model is provided. An experiment involving two varieties of maize (Zea mays) grown at three locations provided the data used to validate the model. Adjusted genotype mean yield values obtained using the proposed exponential transformation were in agreement with observed values.


2021 ◽  
Vol 11 ◽  
Author(s):  
Weikai Yan

Replicated multi-location yield trials are conducted every year in all regions throughout the world for all regionally important crops. Heritability, i.e., selection accuracy based on variety trials, improves with increased number of replicates. However, each replicate is associated with considerable cost. Therefore, it is important for crop variety trials to be optimally replicated. Based on the theory of quantitative genetics, functions that quantitatively define optimal replication on the single-trial basis and on multi-location trial basis were derived. The function on the single-trial basis often over-estimates the optimum number of replicates; it is the function on multi-location trial basis that is recommended for determining the optimal number of replicates. Applying the latter function to the yield data from the 2015–2019 Ottawa oat registration trials conducted both in Ontario and in other provinces of Canada led to the conclusion that a single replicate or two replicates would have sufficed under the current multi-location trial setup. This conclusion was empirically confirmed by comparing genotypic rankings based on all replicates with that on any two replicates. Use of two replicates can save 33–50% of field plots without affecting the selection efficacy.


Author(s):  
Gazi Tamiz Uddin ◽  
Md. Altaf Hossain ◽  
Fahmida Ishaque

The study is conducted to determine the correlation between climatic parameters and rice yield. The present study is also undertaken to analyze the land cover change in Sylhet district between 2013 and 2018 using LANDSAT-8 images. Local climate and rice yield data are collected from BMD (Bangladesh Meteorological Department) and BRRI (Bangladesh Rice Research Institute) and BBS (Bangladesh Bureau of Statistics). ArcGIS 10.5 and SPSS software are used to show the vegetation condition and correlation coefficient between rice yield and climatic variables respectively. It is revealed from the result that rainfall is negatively correlated with Aman and Boro (local and HYV) rice whereas temperature and relative humidity showed a positive correlation with local Aman and Boro rice. On the other hand, relative humidity showed a strong linear relationship with HYV Boro rice. Finally, both temperature and relative humidity have substantial effects on yields in the Boro rice. Furthermore, vegetation condition is observed through NDVI and found the moderate-high vegetation in 2013. After that NDVI value is fluctuating which evidently signifies the rapid vegetation cover change due to a flash flood, flood and other climate changing aspects. Additionally, Forested and high land vegetation’s are endangered rapidly. Some adaptation strategies should be followed to minimize the effects of natural calamities for improving better vegetation condition.


Author(s):  
Dada Ibilewa ◽  
Samaila K. Ishaya ◽  
Joshua I. Magaji

The knowledge of exposure of croplands to climate variability is of paramount importance in adaptive capacity planning to boost food production for the world’s growing population. The study assessed the exposure of croplands to climate variability in the Federal Capital Territory (FCT) of Nigeria using Geo-informatics. This was achieved by examining the distribution pattern of climate indices in FCT from 1981-2017, determining the exposure index of croplands in FCT Area Councils and production of exposure map of FCT Area Councils, The spatial scope of this study is the entire arable land in FCT which is made up of six Area Councils. The research is contextually restricted to exposure of croplands to climate variables while other variables remain constant. The selected climatic variables are rainfall, temperature, relative humidity and potential evapotranspiration (exposure indicators). The arable crops in focus are yam, beans and maize while the soil variables selected for the study are: soil erosion, organic carbon content of the soil, clay content of the soil and percentage of arable land available for crop production. The temporal scope of the examined exposure indicators (climate variables) was limited to a period of thirty (37) years from 1981- 2017. The result indicates that Bwari has the highest exposure (0.1671) to climate variables while Abaji has the least (0.0868) exposure. AMAC is high (0.1371), Kuje (0.1304) is moderate while Gwagwalada (0.1132) and Kwali (0.1154) have low exposures to climate variability. The implication of this on the referenced crops is that crop yield will be highly reduced in Bwari and optimum in Abaji Area Councils due to their climatic requirement. The power of Geo-Spatial Technology in combining different indices of exposure to produce exposure map was demonstrated in the study.


2020 ◽  
Vol 12 (7) ◽  
pp. 82
Author(s):  
Angela Madalena Marchizelli Godinho ◽  
Asdrubal Jesus Farias-Ramírez ◽  
Maria Alejandra Moreno-Pizani ◽  
Tadeu Alcides Marques ◽  
Franklin Javier Paredes-Trejo ◽  
...  

Sugarcane (Saccharum officinarum L.) is one of the most important crops in Brazil and its growth and development can be simulated through process-based models. The current study evaluated a model based on the decision support system for the transfer of Agrotechnology DSSAT/CANEGRO to simulate the sugarcane crop productivity in the western region of São Paulo. The DSSAT/CANEGRO model was calibrated using published yield parameters from a selection of five Brazilian sugarcane cultivars, while sugarcane yield data (tons of stems per hectare) from commercial land were used as benchmark data. Other modeling inputs were derived from the primary regional cultivar. The root mean square error (RMSE), Willmott agreement index (d), and mean absolute error (MAE) were used as performance metrics. The DSSAT/CANEGRO model resulted in a good RMSE performance. The productivity estimates were better for the cultivars SP791010 and RB835486, with RMSE equal to 2.27 and 4.48 Mg ha-1, respectively. The comparison between model-based estimates and observed data produced d values in the range from 0.86 to 0.99, and MAE values in the range of 1.84 to 4.22 Mg ha-1.


Sign in / Sign up

Export Citation Format

Share Document