Crop Moisture Index Research Articles

A Brief Analysis of the Triangle Method and a Proposal for its Operational Implementation

The well-known triangle method in optical/thermal remote sensing, its construction, uncertainties, and the significance of its products are first discussed. These topics are then followed by an outline of how the method can be implemented operationally for practical use, including a suggestion for constructing a dynamic crop moisture index.

Evaluating the Utility of Drought Indices as Soil Moisture Proxies for Drought Monitoring and Land–Atmosphere Interactions

AbstractThere are a variety of metrics that are used to monitor drought conditions, including soil moisture and drought indices. This study examines the relationship between in situ soil moisture, NLDAS-2 soil moisture, and four drought indices: the standardized precipitation index, the standardized precipitation evapotranspiration index, the crop moisture index, and the Palmer Z index. We evaluate how well drought indices and the modeled soil moisture represent the intensity, variability, and persistence of the observed soil moisture in the southern Great Plains. We also apply the drought indices to evaluate land–atmosphere interactions and compare the results with soil moisture. The results show that the SPI, SPEI, and Z index have higher correlations with 0–10-cm soil moisture, while the CMI is more strongly correlated with 0–100-cm soil moisture. All the drought indices tend to overestimate the area affected by moderate to extreme drought conditions. Significant drying trends from 2003 to 2017 are evident in SPEI, Z index, and CMI, and they agree with those in the observed soil moisture. The CMI captures the intra- and interannual variability of 0–100-cm soil moisture better than the other drought indices. The persistence of CMI is longer than that of 0–10-cm soil moisture and shorter than that of 0–100-cm soil moisture. Model-derived soil moisture does not outperform the CMI in the 0–100-cm soil layer. The Z index and CMI are better drought indices to use as a proxy for soil moisture when examining land–atmosphere interactions while the SPI is not recommended. Soil type and climate affect the relationship between drought indices and observed soil moisture.

Assessment of changing pattern of crop water stress in Bangladesh

The Palmers’ crop moisture index (CMI) was used to assess the changing pattern of crop water stress of Bangladesh. Daily rainfall and temperature data for the period 1961–2010 recorded at eleven meteorological stations distributed across the country were used to estimate the time series of CMI. The run theory was used to estimate a set of metrics from CMI to define different characteristics of annual and seasonal crop water stress. The Mann–Kendall trend test was used for the assessment of the significance of the changes in crop water stress indicators at 95% and 99% level of confidence. The results showed that crop water stress in Bangladesh has increased in recent years, particularly in the pre-monsoon season. The annual and pre-monsoon cumulative crop water stress index was found to increase significantly in 5 and 4 out of 11 stations, respectively. As the major portion of total crop in Bangladesh is grown during pre-monsoon season, increasing crop water stress can affect agriculture and food security of Bangladesh. The set of matrices developed in this study can be to understand the different characteristics of water stress and adopting necessary mitigation measures in the context of climate change.

Assessment of SMADI and SWDI agricultural drought indices using remotely sensed root zone soil moisture

Abstract. The increasing frequency of drought events has expanded the research interest in drought monitoring. In this regard, remote sensing is a useful tool to globally mapping the agricultural drought. While this type of drought is directly linked to the availability of root zone soil moisture (RZSM) for plants growth, current satellite soil moisture observations only characterize the water content of the surface soil layer (0–5 cm). In this study, two soil moisture-based agricultural drought indices were obtained at a weekly rate from June 2010 to December 2016, using RZSM estimations at 1 km from the Soil Moisture and Ocean Salinity (SMOS) satellite, instead of surface soil moisture (SSM). The RZSM was estimated by applying the Soil Water Index (SWI) model to the SMOS SSM. The Soil Moisture Agricultural Drought Index (SMADI) and the Soil Water Deficit Index (SWDI) were assessed over the Castilla y León region (Spain) at 1 km spatial resolution. They were compared with the Atmospheric Water Deficit (AWD) and the Crop Moisture Index (CMI), both computed at different weather stations distributed over the study area. The level of agreement was analyzed through statistical correlation. Results showed that the use of RZSM does not influence the characterization of drought, both for SMADI and SWDI.

Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations

The OPtical TRApezoid Model (OPTRAM) has recently been proposed for estimation of soil moisture using only optical remote sensing data. The model relies on a physical linear relationship between the soil moisture content and shortwave infrared transformed reflectance (STR) and can be parameterized universally (i.e., a single calibration for a given area) based on the pixel distribution within the STR-Normalized Difference Vegetation Index (NDVI) trapezoidal space. The main motivation for this study was to evaluate how the universal parameterization of OPTRAM works for long periods of time (e.g., several decades). This is especially relevant for uncovering the soil moisture and agricultural drought history in response to climate change in different regions. In this study, MODIS satellite observations from 2001 to 2017 were acquired and used for the analysis. Cosmic-ray neutron (CRN) soil moisture data, collected with the COsmic-ray Soil Moisture Observing System (COSMOS) at five different sites in the U.S. covering diverse climates, soil types, and land covers, were applied for evaluation of the MODIS-OPTRAM-based soil moisture estimates. The OPTRAM soil moisture estimates were further compared to the Soil Moisture Active and Passive (SMAP) (L-band), the Soil Moisture Ocean Salinity (SMOS) (L-band), and the Advanced AScatterometer (ASCAT) (C-band) soil moisture retrievals. OPTRAM soil moisture data were also analyzed for potential monitoring of agricultural drought through comparison of the OPTRAM-based Soil Water Deficit Index (OPTRAM-SWDI) with the widely-applied Crop Moisture Index (CMI). Evaluation results indicate that OPTRAM-based soil moisture estimates provide overall unbiased RMSE and R between 0.050 and 0.085 cm3 cm−3 and 0.10 to 0.70, respectively, for all investigated sites. The performance of OPTRAM is comparable with the ASCAT retrievals, but slightly less accurate than SMAP and SMOS. OPTRAM and the three microvave satellites captured CRN soil moisture temporal dynamics very well for all five investigated sites. A close agreement was observed between the OPTRAM-SWDI and CMI drought indices for most selected sites. In conclusion, OPTRAM can estimate temporal soil moisture dynamics with reasonable accuracy for a range of climatic conditions (semi-arid to humid), soil types, and land covers, and can potentially be applied for agricultural drought monitoring.

Temporal and Spatial Comparison of Agricultural Drought Indices from Moderate Resolution Satellite Soil Moisture Data over Northwest Spain

During the last decade, a variety of agricultural drought indices have been developed using soil moisture (SM), or any of its surrogates, as the primary drought indicator. In this study, a comprehensive study of four innovative SM-based indices, the Soil Water Deficit Index (SWDI), the Soil Moisture Agricultural Drought Index (SMADI), the Soil Moisture Deficit Index (SMDI) and the Soil Wetness Deficit Index (SWetDI), is conducted over a large semi-arid crop region in northwest Spain. The indices were computed on a weekly basis from June 2010 to December 2016 using 1-km satellite SM estimations from Soil Moisture and Ocean Salinity (SMOS) and/or Moderate Resolution Imaging Spectroradiometer (MODIS) data. The temporal dynamics of the indices were compared to two well-known agricultural drought indices, the atmospheric water deficit (AWD) and the crop moisture index (CMI), to analyze the levels of similarity, correlation, seasonality and number of weeks with drought. In addition, the spatial distribution and intensities of the indices were assessed under dry and wet SM conditions at the beginning of the growing season. The results showed that the SWDI and SMADI were the appropriate indices for developing an efficient drought monitoring system, with higher significant correlation coefficients (R ≈ 0.5–0.8) when comparing with the AWD and CMI, whereas lower values (R ≤ 0.3) were obtained for the SMDI and SWetDI.

The development of Atmospheric Crop Moisture Index for irrigated agriculture

This paper develops the Atmospheric Crop Moisture Index (ACMI), an indicator of atmospheric drought. Levels in the index represent the possibility of rainfall or the lack of moisture needed at various stages of crop growth. The lack of moisture is determined with the ratio between water supply and demand, where the supply indicator is Precipitable Water Vapor (PWV). The ACMI was calculated from data collected between February 2010 and September 2014. The example of calculation of 10-day values of ACMI10 for rice is provided. The comparison of ACMI and other indices shows poor correlation with the SPI, the SPEI, and the scPDSI; however, it displays high correlation with precipitation, the PE, and the MAI. The ACMI is a parameter affected by surface temperature, relative humidity, wind speed, atmospheric pressure, and solar irradiation; all these parameters are included in the study of drought. The ACMI is an effective tool for agricultural water management.

Enhancing the Ability of a Soil Moisture-based Index for Agricultural Drought Monitoring by Incorporating Root Distribution

Agricultural drought differs from meteorological, hydrological, and socioeconomic drought, being closely related to soil water availability in the root zone, specifically for crop and crop growth stage. In previous studies, several soil moisture indices (e.g., the soil moisture index, soil water deficit index) based on soil water availability have been developed for agricultural drought monitoring. However, when developing these indices, it was generally assumed that soil water availability to crops was equal throughout the root zone, and the effects of root distribution and crop growth stage on soil water uptake were ignored. This article aims to incorporate root distribution into a soil moisture-based index and to evaluate the performance of the improved soil moisture index for agricultural drought monitoring. The Huang-Huai-Hai Plain of China was used as the study area. Overall, soil moisture indices were significantly correlated with the crop moisture index (CMI), and the improved root-weighted soil moisture index (RSMI) was more closely related to the CMI than averaged soil moisture indices. The RSMI correctly identified most of the observed drought events and performed well in the detection of drought levels. Furthermore, the RSMI had a better performance than averaged soil moisture indices when compared to crop yield. In conclusion, soil moisture indices could improve agricultural drought monitoring by incorporating root distribution.

Soil Water Monitoring and Numerical Flow Modeling to Quantify Drought Conditions in a Rangeland Ecosystem

Core Ideas Soil water content data can be used for drought monitoring. Expected soil water conditions can be modeled using reanalysis climate data. Drought conditions can be inferred by comparing measured and expected soil water contents. A one‐dimensional vertical numerical model for coupled water flow and heat transport was calibrated for a mixed‐grass rangeland at the Sustainable Agricultural Research and Extension Center, Lingle, WY. The model predicted soil water content reasonably well, with a modeling efficiency (ME) of 0.71 (at 10‐cm depth), 0.79 (30 cm), and 0.78 (60 cm) during calibration. After further validation, the model was used to calculate historical soil water content for the rangeland site using North American Regional Climate Change Assessment Program (NARCCAP) reanalysis climate data (available years 1971–1999). The 29‐yr calculated median soil water content, representing the expected (normal) soil water content during the year, was used to assess drought conditions for the 2011 to 2015 water years with measured soil water contents. The soil water anomaly , calculated as the deviation of measured root zone soil water content from the expected root zone soil water content, confirmed that 2012 was a drought year. The Climate Prediction Center (CPC) crop moisture index (CMI), which was used for comparison, indicated that 2011 to 2015 were all drought years, which did not agree with observations.

Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index

Abstract Drought is a major cause of limited agricultural productivity and of crop yield uncertainty throughout the world. For that reason, agricultural drought research and monitoring are of increasing interest. Although soil moisture is the main variable to define and identify agricultural drought, the actual soil water content is rarely taken into account because this type of drought is commonly studied using methodologies based on either climatological data or hydrological modeling. Currently, it is possible to use remote sensing to obtain global and frequent soil moisture data that could be directly used for agricultural drought monitoring everywhere. For example, the SMOS (Soil Moisture and Ocean Salinity) satellite was launched in 2009 and provides global soil moisture maps every 1–2 days. In this work, the Soil Water Deficit Index (SWDI) was calculated using the SMOS L2 soil moisture series in the REMEDHUS (Soil Moisture Measurement Stations Network) area (Spain) during the period 2010–2014. The satellite index was thus calculated using several approaches to obtain the soil water parameters and was compared with the SWDI obtained from in situ data. One approach was based directly on SMOS soil moisture time series (using the 5th percentile as an estimator for wilting point and the 95th percentile and the minimum of the maximum value during the growing season as estimators for field capacity). In this case, the results of the comparison were good, but the temporal distribution and the range of the index data were unrealistic. Other approaches were based on in situ data parameters and pedotransfer functions estimation. In this case, the results were better, and the satellite index was able to adequately identify the drought dynamics. Therefore, the final choice to apply the index in one particular site will depend on the availability of data. Finally, a comparison analysis was made with the SMOS SWDI and two indices (Crop Moisture Index, CMI, and Atmospheric Water Deficit, AWD) commonly used for agricultural drought monitoring and assessment. In both cases, the agreement was very good, and it was proven that SMOS SWDI reproduces well the soil water balance dynamics and is able to appropriately track agricultural drought.

Spatio-temporal variation in terminal drought over western India using dryness index derived from long-term MODIS data

Drought has always been one of the major hazards in semi-arid and arid regions of the world. This study was undertaken to investigate spatial trends in terminal drought over western India composed of the states of Rajasthan and Gujarat, which are characterized by semi-arid to arid climate. These two states have always been vulnerable to drought since they receive highly variable moderate to scanty rainfall. Leaf area index (LAI)-based temperature vegetation dryness index (TVDI) for the period 2002–2012 was computed using 8-day composites of Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and LAI datasets with 1-km spatial resolution. TVDI maps were prepared for all days of the year (DOY) for the months of September and October, which facilitated our understanding of the spatial extent of drought during different 8-day periods. Based on the analysis, it was found that the LAI-based TVDI could capture drought conditions over space and time. Temporal evolution of the observed crop moisture index (CMI) against TVDI reveals that the TVDI exhibited an increasing trend as the CMI decreased or became negative during the 2002 drought year. A significant and negative relationship (R2=0.23) with TVDI and CMI indicates more sensitivity of TVDI to soil moisture in sparse vegetation canopies under drought conditions. Spatial trends in terminal drought intensity were analyzed using linear regression and Mann–Kendall trend test. It was revealed that a majority of the northern part of the study area witnessed an increase in terminal drought intensity and the southern portion registered negative trends. With 80% confidence, it can be stated that the terminal drought intensity increased at scattered locations over southwestern and central Gujarat. However, over central and southwestern Rajasthan, decreasing trends were registered.

Detection of anomalous crop condition and soil variability mapping using a 26 year Landsat record and the Palmer crop moisture index

Cost-effective and reliable vegetation monitoring methods are needed for applications ranging from traditional agronomic mapping, to verifying the safety of geologic injection activities. A particular challenge is defining baseline crop conditions and subsequent anomalies from long term imagery records (Landsat) in the face of large spatiotemporal variability. We develop a new method for defining baseline crop response (near peak growth) using the normalized difference vegetation index (NDVI) from 26 years (1986–2011) of Landsat data for 400km2 surrounding a planned geologic carbon sequestration site near Jacksonville, Illinois. The normal score transform (yNDVI) was applied on a field by field basis to accentuate spatial patterns and level differences due to planting times. We tested crop type and soil moisture (Palmer crop moisture index (CMI)) as predictors of expected crop condition. Spatial patterns in yNDVI were similar between corn and soybeans – the two major crops. Linear regressions between yNDVI and the cumulative CMI (CCMI) exposed complex interactions between crop condition, field location (topography and soils), and annual moisture. Wet toposequence positions (depressions) were negatively correlated to CCMI and dry positions (crests) positively correlated. However, only 21% of the landscape showed a statistically significant (p<0.05) linear relationship. To map anomalous crop conditions, we defined a tolerance interval based on yNDVI statistics. Tested on an independent image (2013), 63 of 1483 possible fields showed unusual crop condition. While the method is not directly suitable for crop health assessment, the spatial patterns in correlation between yNDVI and CCMI have potential applications for pest damage detection and edaphological soil mapping, especially in the developing world.

Index-based assessment of agricultural drought in a semi-arid region of Inner Mongolia, China

Agricultural drought is a type of natural disaster that seriously impacts food security. Because the relationships among short-term rainfall, soil moisture, and crop growth are complex, accurate identification of a drought situation is difficult. In this study, using a conceptual model based on the relationship between water deficit and crop yield reduction, we evaluated the drought process in a typical rainfed agricultural region, Hailar county in Inner Mongolia autonomous region, China. To quantify drought, we used the precipitation-based Standardized Precipitation Index (SPI), the soil moisture-based Crop Moisture Index (CMI), as well as the Normalized Difference Vegetation Index (NDVI). Correlation analysis was conducted to examine the relationships between dekad-scale drought indices during the growing season (May-September) and final yield, according to data collection from 2000 to 2010. The results show that crop yield has positive relationships with CMI from mid-June to mid-July and with the NDVI anomaly throughout July, but no correlation with SPI. Further analysis of the relationship between the two drought indices shows that the NDVI anomaly responds to CMI with a lag of 1 dekad, particularly in July. To examine the feasibility of employing these indices for monitoring the drought process at a dekad time scale, a detailed drought assessment was carried out for selected drought years. The results confirm that the soil moisture-based vegetation indices in the late vegetative to early reproductive growth stages can be used to detect agricultural drought in the study area. Therefore, the framework of the conceptual model developed for drought monitoring can be employed to support drought mitigation in the rainfed agricultural region of Northern China.

Monitoring and forecasting drought impact on dryland farming areas

AbstractFrequent drought amplifies the need for a warning system and forecasting models for damage to crop yields. This study developed an operational model to assess agricultural drought impact. The dryland areas of Kermanshah Province (Iran) were selected to test the proposed modelling system. The model predicted the consequences of drought damage on wheat crop during critical stages of growth (emergence, vegetative growth, initiation of flowering, grain filling, and maturity) as a drought loss indicator. Two types of input were evaluated to correlate climate conditions versus drought losses. The first group comprises the Palmer Drought Severity Index, Z‐index, Crop Moisture Index, Crop‐Specific Drought Index (CSDI), Standardized Precipitation Index, and Effective Drought Index with one‐ to three‐month timescales used as meteorological indices. The second group, which is consistent of the vegetation condition index and temperature condition index, is based on satellite data. Also a new satellite‐based version of CSDI, so‐called standardized CSDI (S‐CDSI), where evapotranspiration was estimated using surface energy balance algorithm for land, is used. Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) technique was used for forecasting with genetic algorithms applied to select appropriate inputs from among the large number of indices. It was concluded that the combination of meteorological and satellite indices performed best in forecasting crop yield. As expected, accuracy improved over the growth stages as the crop developed. Enhancement of the model with a GIS platform made it possible to present the results more suitably, hence helping users to make more realistic decisions. Copyright © 2012 Royal Meteorological Society

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T (s)) from MODIS 8-day composite data during cloud-free period (September-October) were adopted to construct an NDVI-T (s) space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.

Using satellite and real-time weather data to predict maize production

Large-scale assessments of crop conditions prior to harvest are critical for providing early estimates of production. Satellite and weather information provide the opportunity for near real-time crop monitoring. The objective of this research was to develop an operational assessment system for crop production utilizing data from these sources. Maize (Zea mays) production was assessed in 42 Crop Reporting Districts (CRDs) across the United States Corn Belt, which produce 60% of all maize grown in the United States. Satellite, climatolocal, and agricultural data were collected for 8 years, 1985–1992, and aggregated into CRDs. A model predicting the normalized maize yields for each CRD was developed that included as independent variables a satellite data variable, the Vegetation Condition Index, and a climatological variable, the Crop Moisture Index. This model explained approximately three-quarters (R 2=0.73) of the variation observed in the normalized yields, and was examined both for its accuracy and its timeliness in providing production estimates. Predicted seasonal yields were summed to provide a maize production estimate for the entire Corn Belt study region. Production estimates deviated from the final USDA statistics, which become available several months after harvest, by less than 10% for all eight growing seasons. In addition, the production estimates were available approximately 2 months prior to the completion of the maize harvest. This system has the potential for providing timely in-formation to organizations monitoring regional or global agricultural production for humanitarian or economic benefits.

Satellite assessment of drought impact on native plant communities of southeastern New Mexico, U.S.A.

Low spatial resolution imagery from a polar orbiting meteorological satellite was utilized to detect short-term drought effects on native plant communities in southeastern New Mexico, U.S.A. rangelands. A spectrally derived Vegetation Index was compared to the Palmer Drought Severity Index, Crop Moisture Index, and monthly precipitation departures for a wet year (1988) and a dry year (1989). Differences between years as well as between plant communities were assessed. Plant communities consisting primarily of grass species were the most spectrally responsive to variation in precipitation, while forested areas were least affected.

A Crop‐Specific Drought Index for Corn: I. Model Development and Validation

AbstractMost popular indices in use today that address agricultural drought [e.g., the Palmer Drought Severity Index (PDSI) and the Crop Moisture Index (CMI)] do not consider specific crop responses to the drought event. Thus, values produced by indices such as the PDSI and CMI cannot be directly linked to drought impacts on crop production and yield since each crop responds differently to moisture and heat stress. This paper presents a new drought index that, unlike other drought indices, is specific to a single crop (corn—Zea mays L.) and takes into consideration water use during specific periods of growth. The Crop‐Specific Drought Index (CSDI) model was developed with 8 yr of data from Nebraska's East Central Crop Reporting District (CRD), and validated with 10 yr of data from Nebraska's East Central CRD, 9 yr of data from Missouri's Northeast CRD, 8 yr of data from Wisconsin's South Central CRD, and 9 yr of data from Indiana's Central CRD. Plots of model predicted vs. actual CSDI values yielded coefficients of determination (r2) ranging from 0.32 (Central Indiana) to 0.76 (Northeast Missouri); D‐index of agreement values ranging from 0.72 (Central Indiana) to 0.90 (South Central Wisconsin); and root mean square errors ranging from 7.7% (South Central Wisconsin) to 13.4% (Northeast Missouri). The CSDI model may provide an effective drought monitoring and assessment tool for the Corn Belt of the United States.

Satellite detection of the geographic core of the 1988 Nebraska drought

This study demonstrates that the normalized difference vegetation index as derived from the National Oceanic and Atmospheric Administration's advanced very high resolution radiometer — high resolution picture transmission mode, contains a considerable amount of information on the condition of vegetation. The potential exists for such satellite data to become an additional tool for measuring the physical variables that make up regional-scale climatic conditions. This study was successful in delineating detailed spatial and temporal information concerning the geographic core of the 1988 drought in Nebraska, USA. A temporal comparison between average normalized difference vegetation index and the crop moisture index, for a climatic division, revealed that these indices may be more strongly related than the normalized difference vegetation index and the Palmer drought severity index. The implication is that the average normalized difference vegetation index is a good surrogate for the short-term variables that constitute agricultural drought, which adds a considerable amount of information to existing measures of soil moisture. This technique offers an opportunity to enhance drought monitoring capability in many regions.

The relationship of climatic indices and variables to corn (maize) yields: a principal components analysis

The multivariate statistical technique of principal components analysis was used to evaluate the relationship of climatic indices and variables to corn ( Zea mays L.) yields in the Great Plains and Midwest of the United States. Results demonstrated that during years in which precipitation was consistently above or below normal throughout the growing season, the Palmer Drought Severity Index (PDSI) and the Crop Moisture Index (CMI) related well to actual yield. However, during years in which there was an occurrence of dry or wet spells lasting for several weeks (particularly during pollination), the magnitude of the PDSI and the CMI did not adequately reflect the impact of that dry or wet period on the crop. The results of this analysis demonstrate that principal components analysis is a powerful statistical tool for evaluating the relationship between crop yield and climatic variables and the associated regional patterns for these relationships.