scholarly journals Analysis of the Effects of High Precipitation in Texas on Rainfed Sorghum Yields

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1920 ◽  
Author(s):  
Sharma ◽  
Kannan ◽  
Cook ◽  
Pokhrel ◽  
McKenzie
Keyword(s):  
Crop Yield ◽  
Missing Values ◽  
Crop Yields ◽  
Principal Component ◽  
Growing Season ◽  
Grain Sorghum ◽  
Extreme Weather Events ◽  
Total Precipitation ◽  
Independent Variables ◽  
Two Parameters

Most of the recent studies on the consequences of extreme weather events on crop yields are focused on droughts and warming climate. The knowledge of the consequences of excess precipitation on the crop yield is lacking. We attempted to fill this gap by estimating reductions in rainfed grain sorghum yields for excess precipitation. The historical grain sorghum yield and corresponding historical precipitation data are collected by county. These data are sorted based on length of the record and missing values and arranged for the period 1973–2003. Grain sorghum growing periods in the different parts of Texas is estimated based on the east-west precipitation gradient, north-south temperature gradient, and typical planting and harvesting dates in Texas. We estimated the growing season total precipitation and maximum 4-day total precipitation for each county growing rainfed grain sorghum. These two parameters were used as independent variables, and crop yields of sorghum was used as the dependent variable. We tried to find the relationships between excess precipitation and decreases in crop yields using both graphical and mathematical relationships. The result were analyzed in four different levels; 1. Storm by storm consequences on the crop yield; 2. Growing season total precipitation and crop yield; 3. Maximum 4-day precipitation and crop yield; and 4. Multiple linear regression of independent variables with and without a principal component analysis (to remove the correlations between independent variables) and the dependent variable. The graphical and mathematical results show decreases in rainfed sorghum yields in Texas for excess precipitation could be between 18% and 38%.

scholarly journals Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi

2021 ◽  
Vol 13 (12) ◽  
pp. 2249
Author(s):  
Sadia Alam Shammi ◽  
Qingmin Meng
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Negative Impact ◽  
Positive Impact ◽  
Information Criterion ◽  
Growing Season ◽  
Maximum Temperature ◽  
Linear Regression Models ◽  
The Impact

Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.

The relationship between extreme weather and low crop yields

2020 ◽  
Author(s):  
Matias Heino ◽  
Weston Anderson ◽  
Michael Puma ◽  
Matti Kummu
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Crop Yields ◽  
Explanatory Power ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Yield Variability ◽  
Weather Events

<p>It is well known that climate extremes and variability have strong implications for crop productivity. Previous research has estimated that annual weather conditions explain a third of global crop yield variability, with explanatory power above 50% in several important crop producing regions. Further, compared to average conditions, extreme events contribute a major fraction of weather induced crop yield variations. Here we aim to analyse how extreme weather events are related to the likelihood of very low crop yields at the global scale. We investigate not only the impacts of heat and drought on crop yields but also excess soil moisture and abnormally cool temperatures, as these extremes can be detrimental to crops as well. In this study, we combine reanalysis weather data with national and sub-national crop production statistics and assess relationships using statistical copulas methods, which are especially suitable for analysing extremes. Further, because irrigation can decrease crop yield variability, we assess how the observed signals differ in irrigated and rainfed cropping systems. We also analyse whether the strength of the observed statistical relationships could be explained by socio-economic factors, such as GDP, social stability, and poverty rates. Our preliminary results indicate that extreme heat and cold as well as soil moisture abundance and excess have a noticeable effect on crop yields in many areas around the globe, including several global bread baskets such as the United States and Australia. This study will increase understanding of extreme weather-related implications on global food production, which is relevant also in the context of climate change, as the frequency of extreme weather events is likely to increase in many regions worldwide.</p>

Analyzing Temperature and Precipitation Influences on Yield Distributions of Canola and Spring Wheat in Saskatchewan

2017 ◽  
Vol 56 (4) ◽  
pp. 897-913 ◽  
Author(s):  
Ting Meng ◽  
Richard Carew ◽  
Wojciech J. Florkowski ◽  
Anna M. Klepacka
Keyword(s):  
Crop Yield ◽  
Spring Wheat ◽  
Climate Model ◽  
Crop Yields ◽  
Wheat Yield ◽  
Growing Season ◽  
Weather Data ◽  
Growth Stages ◽  
Yield Distribution ◽  
The Impact

AbstractThe IPCC indicates that global mean temperature increases of 2°C or more above preindustrial levels negatively affect such crops as wheat. Canadian climate model projections show warmer temperatures and variable rainfall will likely affect Saskatchewan’s canola and spring wheat production. Drier weather will have the greatest impact. The major climate change challenges will be summer water availability, greater drought frequencies, and crop adaptation. This study investigates the impact of precipitation and temperature changes on canola and spring wheat yield distributions using Environment Canada weather data and Statistics Canada crop yield and planted area for 20 crop districts over the 1987–2010 period. The moment-based methods (full- and partial-moment-based approaches) are employed to characterize and estimate asymmetric relationships between climate variables and the higher-order moments of crop yields. A stochastic production function and the focus on crop yield’s elasticity imply choosing the natural logarithm function as the mean function transformation prior to higher-moment function estimation. Results show that average crop yields are positively associated with the growing season degree-days and pregrowing season precipitation, while they are negatively affected by extremely high temperatures in the growing season. The climate measures have asymmetric effects on the higher moments of crop yield distribution along with stronger effects of changing temperatures than precipitation on yield distribution. Higher temperatures tend to decrease wheat yields, confirming earlier Saskatchewan studies. This study finds pregrowing season precipitation and precipitation in the early plant growth stages particularly relevant in providing opportunities to develop new crop varieties and agronomic practices to mitigate climate changes.

Evaluating Soil Solarization for Weed Control and Strawberry (Fragariaxananassa) Yield in Annual Plasticulture Production

2017 ◽  
Vol 31 (3) ◽  
pp. 455-463 ◽  
Author(s):  
Jayesh B. Samtani ◽  
Jeffrey Derr ◽  
Mikel A. Conway ◽  
Roy D. Flanagan
Keyword(s):  
Data Collection ◽  
Weed Control ◽  
Crop Yield ◽  
Crop Yields ◽  
Growing Season ◽  
Field Studies ◽  
Soil Solarization ◽  
Row Spacing ◽  
Weed Density ◽  
Growing Seasons

Field studies were initiated in the 2013-14 and 2014-15 growing seasons to evaluate the potential of soil solarization (SS) treatments for their efficacy on weed control and crop yields and to compare SS to 1,3-dichloropropene (1,3-D)+chloropicrin (Pic) fumigation. Each replicate was a bed with dimension 10.6 m long by 0.8 m wide on top. The center 4.6 m length of each bed, referred to as plots, was used for strawberry plug transplanting and data collection. Treatments included: i) 1,3-D+Pic (39% 1,3-dichloropropene+59.6% chloropicrin) that was shank-fumigated in beds at 157 kg ha−1and covered with VIF on August 30 in both seasons; ii) SS for a 6 wk duration initiated on August 15, 2013 and August 21, 2014 by covering the bed with 1 mil clear polyethylene tarp; iii) SS for a 4wk duration initiated on September 6, 2013 and September 3, 2014; iv) SS 4 wk treatment initiated September 6, 2013 and September 3, 2014 and replaced with black VIF on October 4, 2013 and October 1, 2014 and v) a nontreated control covered with black VIF on October 4, 2013 and October 1, 2014. In both seasons, following completion of the preplant treatments, ‘Chandler’ strawberry was planted in two rows at a 36 cm in-row spacing in plots during the first wk of October. Over both seasons, the 6 wk SS treatment consistently lowered the weed density compared to the nontreated control. Weed density in the 6wk SS treatment was not statistically different from the 4wk SS treatments in the 2013-14 growing season. In both seasons, crop yield in the 4 wk SS was significantly lower than other treatments.

scholarly journals Using a Climate Index to Measure Crop Yield Response

2013 ◽  
Vol 45 (4) ◽  
pp. 719-737 ◽  
Author(s):  
Ruohong Cai ◽  
Jeffrey D. Mullen ◽  
John C. Bergstrom ◽  
W. Donald Shurley ◽  
Michael E. Wetzstein
Keyword(s):  
Crop Yield ◽  
Crop Yields ◽  
Principal Component ◽  
Weather Conditions ◽  
Future Climate Change ◽  
Climate Index ◽  
Yield Response ◽  
Climate Projections ◽  
Southern States ◽  
Yield Responses

Using principal component analysis, a climate index is developed to estimate the linkage between climate and crop yields. The indices based on three climate projections are then applied to forecast future crop yield responses. We identify spatial heterogeneity of crop yield responses to future climate change across a number of U.S. northern and southern states. The results indicate that future hotter/drier weather conditions will likely have significant negative impacts on southern states, whereas only mild impacts are expected in most northern states.

scholarly journals Considering uncertainties expands the lower tail of maize yield projections

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259180
Author(s):  
Haochen Ye ◽  
Robert E. Nicholas ◽  
Samantha Roth ◽  
Klaus Keller
Keyword(s):  
Crop Yield ◽  
Crop Yields ◽  
Maize Yield ◽  
Extreme Weather Events ◽  
Small Subset ◽  
Model Parameters ◽  
Yield Model ◽  
Lower Tail ◽  
Uncertainty Sources ◽  
Climate Forcings

Crop yields are sensitive to extreme weather events. Improving the understanding of the mechanisms and the drivers of the projection uncertainties can help to improve decisions. Previous studies have provided important insights, but often sample only a small subset of potentially important uncertainties. Here we expand on a previous statistical modeling approach by refining the analyses of two uncertainty sources. Specifically, we assess the effects of uncertainties surrounding crop-yield model parameters and climate forcings on projected crop yield. We focus on maize yield projections in the eastern U.S.in this century. We quantify how considering more uncertainties expands the lower tail of yield projections. We characterized the relative importance of each uncertainty source and show that the uncertainty surrounding yield model parameters is the main driver of yield projection uncertainty.

scholarly journals Crop correlations with structure elements in varieties of grain sorghum of various ecological and geographical origin

2020 ◽  
Vol 222 ◽  
pp. 03018
Author(s):  
Alexander Baranovskiy ◽  
Nikolay Konoplya ◽  
Tatyana Kosogova ◽  
Sergey Kapustin ◽  
Andrey Kapustin
Keyword(s):  
Crop Yield ◽  
Western Europe ◽  
Geographical Origin ◽  
Negative Relationship ◽  
Field Research ◽  
Growing Season ◽  
Grain Sorghum ◽  
Panicle Number ◽  
Early Maturing ◽  
Panicle Weight

In the conditions of the Donbass region, 38 varieties of grain sorghum of various ecological and geographical origin-breeding companies of Western Europe (“RAGT SEMENCES”, “EURALIS SEMENCES”, “PIONEER”), Ukraine, Russia, the international company “ADVANTA”, the American company “RICHARDSON SEED” were studied. Field research was carried out in the period from 2016 to 2018 in the experimental field of the Lugansk National Agrarian University on ordinary shallow weakly washed blackearth (chernozem) on loess-loam. It was found that the most adapted and productive (6,0 t/ha of grain or more) were early – maturing hybrids – Swift and Queyras; middle early – Solarius, PR88Y20, Bianca, Puma Star; mid-season - Bounty. The most productive (5,0 t/ha and more grain) varieties – Odessky 205, Krupinka 10, Darunok, Zersta 97. The average positive correlation between the duration of the growing season of varieties and crop yield was established. The density of the productive stem in the range from 11 to 25 panicles per 1 m2, the length of the panicle leg and the weight of 1000 grains did not have a significant correlation with the level of sorghum yield. Sorghum yield had an average and increased correlation with panicle weight, grain weight per panicle, number of grains per panicle, grain type, and medium negative relationship with plant height.

Applicability of crop models in the context of parametric insurance – a Caribbean case study

2021 ◽  
Author(s):  
Beatrice Monteleone ◽  
Luigi Cesarini ◽  
Rui Figueiredo ◽  
Mario Martina
Keyword(s):  
Real Time ◽  
Crop Yield ◽  
Vegetation Index ◽  
Crop Yields ◽  
Growing Season ◽  
Gridded Data ◽  
Crop Models ◽  
Weather Events ◽  
Weather Stations ◽  
Weather Parameters

<p>Evaluating the impacts of weather events on the agricultural sector is of high importance. Weather has a huge influence on crop performance and agricultural system management, particularly in those countries where agriculture is mainly rainfed. Climate change is expected to further affect farmers’ incomes since the risk of extreme weather events with a relevant impact on crop yields is predicted to increase.</p><p>Appropriate strategies to deal with the economic impacts of agriculture need to be developed, to enable farmers to quickly recover after a disaster. In this context, weather-based index insurance (also known as parametric insurance) plays a key role since it allows farmers to receive financial aid soon after a disaster occurs.</p><p>This study evaluates the applicability of crop models run with gridded data in the framework of index-based insurance to assess their added value in providing estimations of crop yield in case of drought events.</p><p>At first, the cropland area is identified using satellite data on Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) retrieved from various sources, such as Sentinel and Landsat. Crop Type maps are then produced to identify the location of the different crops grown in a region. Then, weather data coming from stations are exploited to run the AquaCrop crop model and estimate the crop yield for the areas near the weather stations.</p><p>Since in many countries weather stations are often missing or do not record continuously, the AquaCrop model is also run with gridded data coming from reanalysis, specifically ERA, which is a product released by the European Centre for Medium Range Weather Forecast and has the advantage to provide daily estimation of  multiple weather parameters on a 0.25° grid. In addition, ERA5 has a short latency time (in the order of days) and thus allows a near-real time monitoring of the crop growing season. The AquaCrop outputs obtained when the model is run with the station data are then compared to the ones obtained when the model is run with gridded data. The performance of the two model configurations (weather parameters coming from stations or from ERA5) in estimating yield reductions during drought events, previously identified using the Probabilistic Precipitation Vegetation Index (PPVI), are evaluated.</p><p>The framework was applied in the context of the Dominican Republic, a Caribbean country in which 52% of the national territory is devoted to agriculture. The Dominican agricultural industry has as main products cocoa, tobacco, sugarcane, coffee, cotton, rice, beans, potatoes, corn and bananas. Results shows that gridded data can be a valuable tool to provide near-real time estimates of the crop growing season and thus help in forecasting final crop yields in near-real time.</p>

scholarly journals Weather Variability and Instability in Agricultural Production: Evidence from Odisha

2021 ◽  
pp. 111-121
Author(s):  
Manoj Kumar Das
Keyword(s):  
Crop Yield ◽  
Agricultural Production ◽  
Crop Yields ◽  
The State ◽  
Extreme Weather Events ◽  
Weather Variability ◽  
Negative Effects ◽  
Agrarian Economy ◽  
Weather Events ◽  
Agricultural Yield

This study focused on exploring the weather variability induced instability in agriculture in the Odisha, India. In this study, growth and instability in ten major crops are analysed, followed by a depiction of weather variability in Odisha and then the association between weather variability and instability in selected crops are analysed using regression analysis. It is observed that weather variability is a major concern in the state of Odisha. In the context of agrarian economy of Odisha, the dimensions, magnitude and erratic nature of the weather variability and extreme weather events have made the situation more complex. Wide variations are observed in the rainfall both across time and space in the state. The long term average rainfall is indicating a declining trend. The weather variability has produced profound negative effects on agricultural production and yields in the state, causing agricultural fluctuations and has been a serious threat to the agrarian economy. Empirical findings lend credence to the negative effects of weather variability on agricultural yield and the regression analyses of yield instability on weather variability have only reaffirmed the same. The negative effects of weather variability on crop yield leads to a clear policy implication of proper provisioning of irrigation and weather variability resistance crop for increasing the crop yields and reduce the crop yield instability.

scholarly journals The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

2020 ◽  
Vol 12 (7) ◽  
pp. 1111
Author(s):  
Yun Gao ◽  
Songhan Wang ◽  
Kaiyu Guan ◽  
Aleksandra Wolanin ◽  
Liangzhi You ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Crop Yield ◽  
Vegetation Index ◽  
Crop Yields ◽  
Growing Season ◽  
Supplementary Information ◽  
Climate Data ◽  
Modis Evi ◽  
Combining Data ◽  
The U.S

Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data.

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