Image masking for crop yield forecasting using AVHRR NDVI time series imagery

Time series of the Advanced Very High-Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) have been used for crop yield forecasting since the 1980s. Image masking was a critical component of several of these yield forecasting efforts as researchers attempted to isolate subsets of a region’s pixels that would improve their modeling results. Approaches generally sought to identify cropped pixels and when possible, pixels that corresponded to the particular crop type under investigation. In this paper, the former approach will be referred to as cropland masking, and the latter approach will be called crop-specific masking.
The research presented in this paper examines the underlying assumptions made when image masking for the purpose of regional crop yield forecasting. An alternative statistical image masking approach (called yield-correlation masking) is proposed that is objective (i.e., can be automated) and has the flexibility to be applied to any region with several years of time series imagery and corresponding historical crop yield information. The primary appeal of yield-correlation masking is that, unlike cropland masking, no land cover map is required, yet we will show that NDVI models generated using the two methods demonstrate comparable predictive ability.

The goal of this research is very specific: We will establish the yield-correlation masking procedure as a viable image masking technique in the context of crop yield forecasting. To accomplish this objective, we empirically evaluate and compare cropland masking and yield-correlation masking for the purpose of crop yield forecasting. Cropland masking has been shown to benefit yield forecasting models, thus providing a practical benchmark. In the process, we present a robust statistical yield forecasting protocol that can be applied to any (region, crop)-pair possessing the requisite data, and this protocol is used to evaluate the two masking methods that are being compared.
This paper is developed as follows. A brief review of related research is presented. The two primary data sets, AVHRR NDVI time-series imagery and United States Department of Agriculture (USDA) regional crop yield data, are described. Details of the study regions, crop types, and time periods under investigation are specified. Three image masking procedures are discussed, two of which are evaluated in the research.
Finally, the modeling approach and performance evaluation framework are described, along with a summary of results and conclusions drawn from the analysis. Details of the modeling strategy used are described in the Appendix.
Related research
Traditionally, yield estimations are made through agrometeorological modeling or by compiling survey information provided throughout the growing season. Yield estimates derived from agro-meteorological models use soil properties and daily weather data as inputs to simulate various plant processes at a field level (Wiegand & Richardson, 1990; Wiegand et al., 1986). At this scale, agro-meteorological crop yield modeling provides useful results. However, at regional
scales these models are of limited practical use because of spatial differences in soil characteristics and crop growthdetermining factors such as nutrition levels, plant disease, herbicide and insecticide use, crop type, and crop variety, which would make informational and analytical costs excessive. Additionally, Rudorff and Batista (1991) indicated that, at a regional level, agro-meteorological models are unable to completely simulate the different crop growing conditions that result from differences in climate, local weather conditions, and land management practices. The scale of applicability of agrometeorological models is getting larger, though, but presently only through the integration of remotely sensed imagery. For instance, Doraiswamy et al. (2003) developed a method using AVHRR NDVI data as proxy inputs to an agro-meteorological model in estimating spring wheat yields at county and subcounty scales in the U.S. state of North Dakota.
In the past 25 years, many scientists have utilized remote sensing techniques to assess agricultural yield, production, and crop condition. Wiegand et al. (1979) and Tucker et al. (1980) first identified a relationship between the NDVI and crop yield using experimental fields and ground-based spectral radiometer measurements. Final grain yields were found to be highly correlated with accumulated NDVI (a summation of NDVI between two dates) around the time of maximum greenness
(Tucker et al., 1980). In another experimental study, Das et al. (1993) used remotely sensed data to predict wheat yield 85– 110 days before harvest in India. These early experiments identified relationships between NDVI and crop response, paving the way for crop yield estimation using satellite imagery.
Rasmussen (1992) used 34 AVHRR images of Burkina Faso, Africa, for a single growing season to estimate millet yield. Using accumulated NDVI and statistical regression techniques, he found strong correlations between accumulated NDVI and yield, but only during the reproductive stages of crop growth. The lack of a strong correlation between accumulated NDVI and yield during other stages of growth was attributed to the limited temporal profile of imagery used in the study and the high variability of millet yield in his study area. Potdar (1993) estimated sorghum yield in India using 14 AVHRR images from the same growing season. He was able to forecast actual yield at an accuracy of T15% up to 45 days before harvest. Rudorff and Batista (1991) used NDVI values
as inputs into an agro-meteorological model to explain nearly 70% of the variation in 1986 wheat yields in Brazil. Hayes and Decker (1996) used AVHRR NDVI data to explain more than 50% of the variation in corn yields in the United States Corn Belt. Each of these studies found positive relationships between crop yield and NDVI, but the strength of the relationships depended upon the amount and quality of the imagery used.
Some studies have used large, multi-year AVHRR NDVI data sets. Maselli et al. (1992) found strong correlations between NDVI and final crop yields in the Sahel region of Niger using 3 years of AVHRR imagery (60 images). In India, Gupta et al. (1993) used 3 years of AVHRR data to estimate wheat yields within T5% up to 75 days before harvest. The success of this study was dependent on the fact that over 80% of the study area was covered with wheat. In Greece, 2 years of AVHRR imagery were used to estimate crop yields (Quarmby et al., 1993). Actual harvested rice yields were predicted with an accuracy of T10%, and wheat yields were predicted with an accuracy of T12% at the time of maximum greenness. Groten (1993) was able to predict crop yield with a T15% estimation error 60 days before harvest in Burkina Faso using regression techniques and 5 years of AVHRR NDVI data (41 images).
Doraiswamy and Cook (1995) used 3 years of AVHRR NDVI imagery to assess spring wheat yields in North and South Dakota in the United States. They concluded that the most promising way to improve the use of AVHRR NDVI for estimating crop yields at regional scales would be to use larger temporal data sets, better crop masks, and climate data. Lee et al. (1999) used a 10-year, biweekly AVHRR data set to forecast  corn yields in the U.S. state of Iowa. They found that the most accurate forecasts of crop yield were made using a cropland mask and measurements of accumulated NDVI. Maselli and Rembold (2001) used multi-year series of annual crop yields and monthly NDVI to develop cropland masks for four Mediterranean African countries. They found that application of the derived cropland masks improved relationships between NDVI and final yield during optimal yield prediction periods. Ferencz et al. (2004) found yields of eight different crops in Hungary to be highly correlated with optimized, weighted seasonal NDVI sums using 1-km AVHRR NDVI from 1996 to 2000. They used non-forest vegetation masks and a novel time series interpolation approach and actually obtained their best results when using a greenness index equivalent to the numerator of the NDVI formula (NIR-RED; see Section 3). Additionally, many researchers have found that crop condition and yield estimation are improved through the inclusion of metrics that characterize crop development stage (Badhwar & Henderson, 1981; Groten, 1993; Kastens, 1998; Lee et al., 1999; Quarmby et al., 1993; Rasmussen, 1992). Ancillary data have been found useful as well. For example, Rasmussen (1997) found that soil type information improved the explanation of millet and ground nut yield variation using 3 years of AVHRR NDVI from the Peanut Basin in Senegal. In a later study, Rasmussen (1998) found that the inclusion of tropical livestock unit density further improved the explanation of millet yield variation in intensively cultivated regions of the Peanut Basin.

Based on the studies described, for the purpose of crop yield forecasting, longer time series of NDVI imagery are preferred to shorter ones. Also, few image masking techniques have been thoroughly and comparatively explored, likely due to the inherent complexities underlying this phase in any remote sensing-based yield forecasting methodology. Thus, to help achieve the goal of this project, an important objective of this research is to use historical yield information and historical time series AVHRR NDVI imagery to devise a thorough and robust statistical procedure for obtaining early to mid-season crop yield forecasts, with particular emphasis on image masking. The techniques described in this paper can be applied to any (region, crop)-pair that possesses sufficient historical yield information and corresponding time series NDVI imagery. Since few meaningful crop phenology metrics can be accurately derived at early points in the growing season, our research does not attempt to use this information. Also, no ancillary information is used, to prevent dependence on the availability of such data.

Description of data
The research presented in this paper relies on two data sets. The first is a time series of biweekly AVHRR NDVI compositeimagery from 1989 to 2000, obtained from the U.S. Geological survey Earth Resources Observation Systems (EROS) Data Center (EDC) in Sioux Falls, SD (Eidenshink, 1992). This data set was chosen because it is relatively inexpensive, reliable, and is updated in near real-time. NDVI is defined by the formula (NIR-RED)/(NIR +RED), where NIR is reflectance in the near-infrared spectrum (0.75 – 1.10 Am) and RED is reflectance in the red band of the visible spectrum (0.58 – 0.68 Am). Chlorophyll uses electromagnetic energy in the RED band for photosynthesis, and plant structure is reflective of energy in the NIR band. So, for vegetated surfaces, NDVI increases if plant biomass increases or if photosynthetic activity increases.
The NDVI data were received in unsigned 8-bit integer format, with the original NDVI range [1,1] linearly scaled to the integer range 0 –200. For analysis purposes, the integer data were rescaled to their native range of [1,1]. As a consequence of the limited precision of the original 8-bit data, the precision of the rescaled data is 0.01, so there is an implicit expected numerical error of 0.005 in the pixel-level NDVI values.
The NDVI data set is not without uncertainty, both temporal and spatial. From 1989 – 2000, two polar orbiting National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11 [1989 – 1994] and NOAA-14 [1995 – 2000]) carried the AVHRR sensors that collected the data comprising our data set. The U.S. annually invariant target curve, with NDVI from periods 5– 21 (February 26 –October 21) shown for each year. This curve represents the average time series of nearly 3500 pixels selectively sampled from the 48 states in the conterminous U.S. to possess highly regular annual periodicity, thus exposing any artificial interannual NDVI value drift (Kastens et al., 2003). The NDVI data originating from NOAA-11 are fairly consistent over time. The data from NOAA-14 are less so, exhibiting a large artificial oscillation from 1997 to 2000. The range of the trend curve from 1989 to 2000 has width 0.0464. Comparing this width to
the overall effective range of the AVHRR NDVI data being used (which is approximately [0.05,0.95] for the full U.S. terrestrial range, but narrower in most practical situations), it follows that nearly 5% of the effective AVHRR NDVI data range can be attributed to artificial interannual NDVI value drift. In retrospect, we know that sensor orbit decay and sensor calibration degradation were the primary sources of the interannual NDVI value drift found in the NOAA-14 data.
Image resolution (1 km2 /pixel, or 100 ha/pixel) of the AVHRR NDVI data is also an issue because pixel size is more  than twice as large as the typical field size for soybeans and major grains in the U.S., which is roughly 40 ha (Kastens & Dhuyvetter, 2002). Furthermore, when considering spatial error of the image registration performed during the NDVI compositing process, the area of the region from which a single pixel’s values can be obtained grows to more than 4 km2, or more than 400 ha (Eidenshink, 1992). A combination of sensor factors (e.g., sensor stability, view angle, orbit integrity) and effects of image pre-processing and compositing induce this spatial variation.

The second data set is historical, final, state-level yield data, obtained from the USDA National Agricultural Statistics Service (NASS) through its publicly accessible website (http://www.usda.gov/nass). The database is updated annually for all crops, with each particular crop’s final regional yield estimates released well after harvest completion. Updates to the final regional yield estimates can occur up to 3 years after their initial release, but generally these changes are not large. No historical or expected error statistics for these estimates are published below the national spatial scale, but they are nonetheless accepted by the industry as the best widely available record for average regional crop yield in the U.S.

Description of crops, regions, and time periods under investigation
The crops and regions under investigation in the present research are corn and soybeans in the U.S. states of Iowa (IA) and Illinois (IL), winter wheat and grain sorghum in the state of Kansas (KS), and spring wheat and barley in the state of North Dakota (ND). Compared to other states, during 1989 –2000, Iowa ranked first in corn production (100.2 million mt/year; ‘‘mt’’=metric ton) and second in soybean production (26.9 million mt/year). Illinois ranked second in corn production (89.9 million mt/year) and first in soybean production (27.1 million mt/year). Kansas was the top-producing winter wheat state (25.5 million mt/year) and the top-producing grain sorghum state (14.2 million mt/year). North Dakota was the top-producer of both spring wheat (16.6 million mt/year) and barley (6.3 million mt/year).

For each crop, a six-period window of early to mid-season NDVI imagery is considered. The source data for these six biweekly composites span nearly 3 months of raw AVHRR imagery, corresponding to Julian biweekly periods 5 – 10 (approximately February 26 –May 20) for winter wheat, 9– 14 (approximately April 23 – July 15) for spring wheat and barley, and 11 –16 (approximately May 21–August 12) for corn, soybeans, and sorghum. Labeling the six biweekly periods 1 to 6, yields are modeled using data from periods 1– 4, 1 –5, and 1 – 6, with each of these three ranges providing a unique yield forecasting opportunity corresponding to a
different point in the growing season. To obtain the dates for the crop-specific ranges, the initial release dates of USDA NASS yield forecasts were considered. The first NDVI image generated after the release of the initial USDA state-level estimates for the season is assigned to period 6, which fixes periods 1– 5 as well. Initial release dates for USDA state level estimates are approximately May 11 for winter wheat, July 11 for spring wheat and barley, and August 11 for corn, soybeans, and grain sorghum. Hereafter, winter wheat will be classified as an early-season crop, spring wheat and barley as mid-season crops, and corn, soybeans, and grain sorghum as late-season crops. With this timing, forecasts generated at periods 4 and 5 for each crop are produced before any state-level USDA yield estimates are released.

Approaches to image masking in crop yield forecasting
The purpose of image masking in the context of crop yield forecasting is to identify subsets of a region’s pixels that lead to NDVI variable values that are optimal indicators of a particular crop’s final yield. A good image mask should capture the essence (i.e., salient features) of the present year’s growing season with respect to how the crop of interest is progressing. This growing season essence is a combination of climatic and terrestrial factors.
Crop-specific masking
In theory, the ideal approach to image masking for the purpose of crop yield forecasting would be to use crop-specific masking. This would allow one to consider only NDVI information pertaining to the crop of interest. However, when such masking is applied to multiple years of imagery, several difficulties are encountered. Principal among these is the widespread practice of crop rotation, which suggests that year-specific masks are needed rather than a single cropspecific mask that can be applied to all years. Regional trending in crop area (increase or decrease in the amount of a region’s area planted to a particular crop over time), if severe
enough, also may call for year-specific masking. Identifying a particular crop in the year to be forecasted presents even greater difficulties, as only incomplete growing season NDVI information is available. This is especially true early in the season when the crop has low biomass and does not produce a large
NDVI response. In addition to hindering crop classification, this low NDVI response of a crop early in its development also  stifles crop yield modeling efforts, as AVHRR NDVI measurements from pixels corresponding to immature crops are not very sensitive and are thus minimally informative (see Wardlow et al., in press, for an example of such insensitivity occurring with 250-m Moderate Resolution Imaging Spectroradiometer [MODIS] NDVI data, and MODIS has better radiometric resolution than AVHRR). Moreover, with the coarse-resolution (about 100 hectares/ pixel) AVHRR NDVI imagery used in this study, identifying monocropped pixels becomes an improbable task. This is particularly true in low-producing regions and in regions with sparse crop distribution. As noted, a single pixel covers an area well over twice the average field size, and when error of the image registration is considered, a pixel’s effective ground coverage can become more than 400 hectares/pixel, or roughly ten times the typical field size.

Cropland masking
A more feasible alternative to crop-specific masking is cropland masking, which refers to using pixels dominated by land in general agricultural crop production. Kastens (1998, 2000) and Lee et al. (1999) obtained some of their best yield modeling results using this approach. Rasmussen (1998) used a percent-cropland map to improve his yield modeling by splitting the data into two categories based on cropland density and building different models for the two classes. Maselli and Rembold (2001) used correlations between 13 years of monthly NDVI composites and 13- year series of national crop yields to estimate pixel-level cropland fraction for four Mediterranean African countries. Upon application of these derived cropland masks, the authors found improved relationships between NDVI and
final estimated yield.
Cropland masks usually are derived from existing land use/land cover maps (one exception being Maselli and Rembold (2001)). If relatively small amounts of land in a study area have been taken out of or put into agricultural crop production during a study period, a single mask can be obtained and applied to all years of data. Considering that all traditional agricultural crops are now lumped in the general class of ‘‘cropland,’’ heavily cropped pixels are more prevalent in heavily cropped regions, which allows for the construction of well-populated masks dominated by cropland. But the generation of such masks becomes difficult when low-producing regions are encountered, as well as in regions where cropland is widely interspersed with non-cropland.
As with crop-specific masking, cropland masking also can suffer the effects of minimally informative NDVI response early in a crop’s growing season (e.g., March for early-season crops, May for mid-season crops, and late May and early June for late-season crops). Many important agricultural regions are almost completely dominated by single-season crop types (e.g., Iowa produces predominately late-season crops). In such cases, cropland AVHRR NDVI from time periods early in the
particular growing season may not be very useful for predicting final yield.

By late May and June, some of the year’s terrestrial and weather-based growth-limiting factors may have already been established for some crops or regions. For instance, in the U.S., soil moisture has largely been set by this time, and soil moisture is an important determinant of crop yields in the four states comprising our study area. Such moisture information is not readily detectable in immature crops because it is usually not a limiting factor until the plant’s water needs become
significant and its roots penetrate deeper into the soil. On the other hand, available soil moisture can noticeably affect other regional vegetation that is already well developed, such as grasslands, shrublands, and wooded areas, and in some cases, early- and mid-season crops.

Yield-correlation masking
For the reasons noted above, we propose a new masking technique, which we call yield-correlation masking. All vegetation in a region integrates the season’s cumulative growing conditions in some fashion and may be more indicative of a crop’s potential than the crop itself. Thus, all pixels are considered for use in crop yield prediction. This premise is most sound early in a crop growing season (especially for mid- and late-season crops), when the NDVI response of the immature crop is not yet strong enough to be a useful indicator of final yield. Also, as noted, when the crop is in early growth stages, problems such as lack of subsoil moisture may not yet have impacted the immature crop while having already affected more mature nearby vegetation.
Each NDVI-based variable captures a different aspect of the current growing season. This aspect manifests itself in different ways within the region’s vegetation, suggesting that optimal masks for the different NDVI-based variables likely will not be identical. Thus, for each (region, crop)-pair, yield-correlation masking generates a unique mask for each NDVI variable. The technique is initiated by correlating each of the historical, pixel-level NDVI variable values with the
region’s final yield history, a strategy similar to the initial step of the cropland classification strategy presented in Maselli and Rembold (2001). The highest correlating pixels,  thresholded so that some pre-specified number of pixels is included in the mask (this issue is addressed later), are retained for further processing and evaluation of the variable at hand.
Though much more computationally intensive, the yieldcorrelation masking technique overcomes the major problems afflicting crop-specific masking and cropland masking. Unlike these approaches, yield-correlation masking readily can be applied to low-producing regions and regions possessing sparse crop distribution. Also, since yield correlation masks are not constrained to include pixels dominated by cropland, they are not necessarily hindered by the weak and insensitive NDVI
responses exhibited by crops early in their respective growing seasons. Furthermore, once the issue of identifying optimal mask size (i.e., determining how many pixels should be  included in the masks) is addressed, the entire masking/ modeling procedure becomes completely objective.

Description of cropland masks
For Iowa, Illinois, and North Dakota, the cropland masks used in this study were derived from the United States Geological Survey (USGS) National Land Cover Database (NLCD) (Vogelmann et al., 2001). The original 30-m resolution land cover maps can be obtained from the website http://landcover.usgs.gov/natllandcover.html. After generalizing the classes to cropland and non-cropland, the data were aggregated to a 1-km grid corresponding to the NDVI imagery used in this study. All annual crops, as well as alfalfa, were assigned to the cropland category, and all other cover types
were classified as non-cropland. Pixel values in the resulting  map corresponded to percent cropland within the 1-km2 footprint of the pixel.

(Source – http://kufs.ku.edu/media/uploads/work/Kastens_RSE2005_Image_masking_for_crop_yield_forecasting.pdf)

Read more

Agricultural Meteorology

            A branch of meteorology that examines the effects and impacts of weather and climate on crops, rangeland, livestock, and various agricultural operations. The branch of agricultural meteorology dealing with atmospheric-biospheric processes occurring at small spatial scales and over relatively short time periods is known as micrometeorology, sometimes called crop micrometeorology for managed vegetative ecosystems and animal biometeorology for livestock operations. The branch that studies the processes and impacts of climatic factors over larger time and spatial scales is often referred to as agricultural climatology.

            Agricultural meteorology, or agrometeorology, addresses topics that often require an understanding of biological, physical, and social sciences. It studies processes that occur from the soil depths where the deepest plant roots grow to the atmospheric levels where seeds, spores, pollen, and insects may be found. Agricultural meteorologists characteristically interact with scientists from many disciplines.

          Agricultural meteorologists collect and interpret weather and climate data needed to understand the interactions between vegetation and animals and their atmospheric environments.

            The climatic information developed by agricultural meteorologists is valuable in making proper decisions for managing resources consumed by agriculture, for optimizing agricultural production, and for adopting farming practices to minimize any adverse effects of agriculture on the environment. Such information is vital to ensure the economic and environmental sustainability of agriculture now and in the future. Agricultural meteorologists also quantify, evaluate, and provide information on the impact and consequences of climate variability and change on agriculture. Increasingly, agricultural meteorologists assist policy makers in developing strategies to deal with climatic events such as floods, hail, or droughts and climatic changes such as global warming and climate variability.

           Agricultural meteorologists are involved in many aspects of agriculture, ranging from the production of agronomic and horticultural crops, trees, and livestock to the final delivery of agricultural products to market. They study the energy and mass exchange processes of heat, carbon dioxide, water vapor, and trace gases such as methane, nitrous oxide, and ammonia, within the biosphere on spatial scales ranging from a leaf to a watershed and even to a continent.

            They study, for example, the photosynthesis, productivity, and water use of individual leaves, whole plants, and fields. They also examine climatic processes at time scales ranging from less than a second to more than a decade.

            Agricultural Climatology

            In general, the study of climate as to its effect on crops; it includes, for example, the relation of growth rate and crop yields to the various climatic factors and hence the optimum and limiting climates for any given crop. Also known as agroclimatology.

            Biometeorology

            A branch of meteorology and ecology that deals with the effects of weather and climate on plants, animals, and humans. The principal problem for living organisms is maintaining an acceptable thermal equilibrium with their environment. Organisms have natural techniques for adapting to adverse conditions. These techniques include acclimatization, dormancy, and hibernation, or in some cases an organism can move to a more favorable environment or microenvironment. Humans often establish a favorable environment through the use of technology.

            Climatology

            The scientific study of climate. Climate is the expected mean and variability of the weather conditions for a particular location, season, and time of day. The climate is often described in terms of the mean values of meteorological variables such as temperature, precipitation, wind, humidity, and cloud cover. A complete description also includes the variability of these quantities, and their extreme values. The climate of a region often has regular seasonal and diurnal variations, with the climate for January being very different from that for July at most locations. Climate also exhibits significant year-to-year variability and longer-term changes on both a regional and global basis. The goals of climatology are to provide a comprehensive description of the Earth’s climate over the range of geographic scales, to understand its features in terms of fundamental physical principles, and to develop models of the Earth’s climate for sensitivity studies and for the prediction of future changes that may result from natural and human causes.

            Crop Micrometeorology

            The branch of meteorology that deals with the interaction of crops and their immediate physical environment.

            Micrometeorology

            The study of small-scale meteorological processes associated with the interaction of the atmosphere and the Earth’s surface. The lower boundary condition for the atmosphere and the upper boundary condition for the underlying soil or water are determined by interactions occurring in the lowest atmospheric layers. Momentum, heat, water vapor, various gases, and particulate matter are transported vertically by turbulence in the atmospheric boundary layer and thus establish the environment of plants and animals at the surface. These exchanges are important in supplying energy and water vapor to the atmosphere, which ultimately determine large-scale weather and climate patterns. Micrometeorology also includes the study of how air pollutants are diffused and transported within the boundary layer and the deposition of pollutants at the surface.

            In many situations, atmospheric motions having time scales between 15 min and 1 h are quite weak. This represents a spectral gap that provides justification for distinguishing micrometeorology from other areas of meteorology. Micrometeorology studies phenomena with time scales shorter than the spectral gap (time scales less than 15 min to 1 h and horizontal length scales less than 2–10 km). Some phenomena studied by micrometeorology are dust devils, mirages, dew and frost formation, evaporation, and cloud streets.

            Ecosystem

            An ecosystem is a complete community of living organisms and the nonliving materials of their surroundings. Thus, its components include plants, animals, and microorganisms; soil, rocks, and minerals; as well as surrounding water sources and the local atmosphere. The size of ecosystems varies tremendously. An ecosystem could be an entire rain forest, covering a geographical area larger than many nations, or it could be a puddle or a backyard garden. Even the body of an animal could be considered an ecosystem, since it is home to numerous microorganisms. On a much larger scale, the history of various human societies provides an instructive illustration as to the ways that ecosystems have influenced civilizations.

            Weather Observations

            The measuring, recording, and transmitting of data of the variable elements of weather. In the India the National Data Centre (NDC), a division of the India Meteorological Department (IMD), has as one of its primary responsibilities the acquisition of meteorological information.

            The data are sent by various communication methods to the NDC of IMD. At the Center, the raw data are fed into large computers that are programmed to plot, analyze, and process the data and also to make prognostic weather charts. The processed data and the forecast guidance are then distributed by special National Weather Service systems and conventional telecommunications to field offices, other government agencies, and other stake holders. They in turn prepare forecasts and warnings based on both processed and raw data. A wide variety of meteorological data are required to satisfy the needs of meteorologists, climatologists, and users in marine activities, forestry, agriculture, aviation, and other fields.

            Weather forecasting and prediction

            Processes for formulating and disseminating information about future weather conditions based upon the collection and analysis of meteorological observations. Weather forecasts may be classified according to the space and time scale of the predicted phenomena. Atmospheric fluctuations with a length of less than 100 m (330 ft) and a period of less than 100 s are considered to be turbulent.

            The study of atmospheric turbulence is called micrometeorology; it is of importance for understanding the diffusion of air pollutants and other aspects of the climate near the ground. Standard meteorological observations are made with sampling techniques that filter out the influence of turbulence. Common terminology distinguishes among three classes of phenomena with a scale that is larger than the turbulent microscale: the mesoscale, synoptic scale, and planetary scale.

            The mesoscale includes all moist convection phenomena, ranging from individual cloud cells up to the convective cloud complexes associated with prefrontal squall lines, tropical storms, and the intertropical convergence zone. Also included among mesoscale phenomena are the sea breeze, mountain valley circulations, and the detailed structure of frontal inversions. Most mesoscale phenomena have time periods less than 12 h. The prediction of mesoscale phenomena is an area of active research. Most forecasting methods depend upon empirical rules or the short-range extrapolation of current observations, particularly those provided by radar and geostationary satellites.

            Forecasts are usually couched in probabilistic terms to reflect the sporadic character of the phenomena.

            Since many mesoscale phenomena pose serious threats to life and property, it is the practice to issue advisories of potential occurrence significantly in advance. These “watch” advisories encourage the public to attain a degree of readiness appropriate to the potential hazard. Once the phenomenon is considered to be imminent, the advisory is changed to a “warning,” with the expectation that the public will take immediate action to prevent the loss of life.

            The next-largest scale of weather events is called the synoptic scale, because the network of meteorological stations making simultaneous, or synoptic, observations serves to define the phenomena. The migratory storm systems of the extratropics are synoptic-scale events, as are the undulating wind currents of the upper-air circulation which accompany the storms. The storms are associated with barometric minima, variously called lows, depressions, or cyclones. The synoptic method of forecasting consists of the simultaneous collection of weather observations, and the plotting and analysis of these data on geographical maps. An experienced analyst, having studied several of these maps in chronological succession, can follow the movement and intensification of weather systems and forecast their positions. This forecasting technique requires the regular and frequent use of large networks of data.

            Planetary-scale phenomena are persistent, quasistationary perturbations of the global circulation of the air with horizontal dimensions comparable to the radius of the Earth. These dominant features of the general circulation appear to be correlated with the major orographic features of the globe and with the latent and sensible heat sources provided by the oceans. They tend to control the paths followed by the synoptic-scale storms, and to draw upon the synoptic transients for an additional source of heat and momentum.

            Numerical weather prediction is the prediction of weather phenomena by the numerical solution of the equations governing the motion and changes of condition of the atmosphere. Numerical weather prediction techniques, in addition to being applied to short-range weather prediction, are used in such research studies as air-pollutant transport and the effects of greenhouse gases on global climate change.

            The first operational numerical weather prediction model consisted of only one layer, and therefore it could model only the temporal variation of the mean vertical structure of the atmosphere.

            Computers now permit the development of multilevel (usually about 10–20) models that could resolve the vertical variation of the wind, temperature, and moisture. These multilevel models predict the fundamental meteorological variables for large scales of motion. Global models with horizontal resolutions as fine as 125 mi (200 km) are being used by weather services in several countries. Global numerical weather prediction models require the most powerful computers to complete a 10-day forecast in a reasonable amount of time.

            Research models similar to global models could be applied for climate studies by running for much longer time periods. The extension of numerical predictions to long time intervals (many years) requires a more accurate numerical representation of the energy transfer and turbulent dissipative processes within the atmosphere and at the air-earth boundary, as well as greatly augmented computing-machine speeds and capacities.

            Long-term simulations of climate models have yielded simulations of mean circulations that strongly resemble those of the atmosphere. These simulations have been useful in explaining the principal features of the Earth’s climate, even though it is impossible to predict the daily fluctuations of weather for extended periods. Climate models have also been used successfully to explain paleoclimatic variations, and are being applied to predict future changes in the climate induced by changes in the atmospheric composition or characteristics of the Earth’s surface due to human activities.

            Surface meteorological observations are routinely collected from a vast continental data network, with the majority of these observations obtained from the middle latitudes of both hemispheres. Commercial ships of opportunity, military vessels, and moored and drifting buoys provide similar in-place measurements from oceanic regions. Information on winds, pressure, temperature, and moisture throughout the troposphere and into the stratosphere is routinely collected from (1) balloon-borne instrumentation packages (radiosonde observations) and commercial and military aircraft which sample the free atmosphere directly; (2) ground-based remote-sensing instrumentation such as wind profilers (vertically pointing Doppler radars), the National Weather Service Doppler radar network, and lidars; and (3) special sensors deployed on board polar orbiting or geostationary satellites. The remotely sensed observations obtained from meteorological satellites have been especially helpful in providing crucial measurements of areally and vertically averaged temperature, moisture, and winds in data-sparse (mostly oceanic) regions of the world. Such measurements are necessary to accommodate modern numerical weather prediction practices and to enable forecasters to continuously monitor global storm (such as hurricane) activity.

            Forecast products and forecast skill are classified as longer term (greater than 2 weeks) and shorter term. These varying skill levels reflect the fact that existing numerical prediction models such as the medium-range forecast have become very good at making large-scale circulation and temperature forecasts, but are less successful in making weather forecasts. An example is the prediction of precipitation amount and type given the occurrence of precipitation and convection.

            Each of these forecasts is progressively more difficult because of the increasing importance of mesoscale processes to the overall skill of the forecast.

            Nowcasting is a form of very short range weather forecasting. The term nowcasting is sometimes used loosely to refer to any area-specific forecast for the period up to 12 h ahead that is based on very detailed observational data. However, nowcasting should probably be defined more restrictively as the detailed description of the current weather along with forecasts obtained by extrapolation up to about 2 h ahead. Useful extrapolation forecasts can be obtained for longer periods in many situations, but in some weather situations the accuracy of extrapolation forecasts diminishes quickly with time as a result of the development or decay of the weather systems.

            Forecasts of time averages of atmospheric variables, for example, sea surface temperature, where the lead time for the prediction is more than 2 weeks, are termed long-range or extended-range climate predictions. Extended-range predictions of monthly and seasonal average temperature and precipitation are known as climate outlooks. The accuracy of long-range outlooks has always been modest because the predictions must encompass a large number of possible outcomes, while the observed single event against which the outlook is verified includes the noise created by the specific synoptic disturbances that actually occur and that are unpredictable on monthly and seasonal time scales. According to some estimates of potential predictability, the noise is generally larger than the signal in middle latitudes.

(Source – http://hau.ernet.in/coa/Kagromet.pdf)

Read more

Weed control in no-tillage corn

Weed Control

Weed control in no-tillage corn is often more difficult  than in conventionally tilled corn. As a general rule, herbicide effectiveness decreases with the amount of crop and weed debris on the soil surface. This debris ties up herbicides and also presents a physical barrier to the uniform distribution of the herbicide on the soil for residual activity. Consequently, selection of herbicide rates and application methods is critical. Read and follow instructions on the herbicide label. On the other hand, residue cover provides some suppression of many weed species. In addition, there is generally less incidence of large-seeded weeds in continuous notillage systems.

Control of Existing Vegetation at  Planting

The types of weeds present and the type of cover determine the herbicide program required to control vegetation present at planting. It is necessary to consider the burndown materials and the postemergence characteristics of the other herbicides to be used in relationship  to the weed infestation.

Annual grasses and broadleaf weeds can be controlled with nonselective herbicides, such as glyphosate or paraquat. In general, no alteration in the residual herbicide program is needed to supplement the nonselective herbicide in these instances, although some herbicide labels require slightly higher rates to compensate for herbicide adsorption on the cover crop or other plant material.

When planting in perennial grass sods, a single paraquat application may not be sufficient to give satisfactory control. Control of orchardgrass and fescue requires use of the highest labeled rates of atrazine in addition to paraquat. The use of atrazine plus simazine combinations in perennial sods is not recommended because, unlike atrazine, simazine does not have postemergence activity and will not aid in burndown of these grass sods. In very vigorous orchardgrass or fescue sods, two applications of paraquat are sometimes required to achieve complete control.

The use of glyphosate should be considered for control of existing perennial broadleaf and grass weeds at planting. Care must be taken to allow these weeds to reach the minimum growth stages listed on the label before application is made. Often, this delays corn planting to the point that alternative crops or tillage methods should be considered as a means of control.

The use of glyphosate should also be considered when heavy infestations of annual weeds are present and have advanced to the stage at which paraquat will give only partial control.

Control of Annual Grasses

Fall panicum and other annual grasses can be major problems in no-tillage corn production. Simazine has good activity on annual grasses, and a combination of atrazine and simazine will give good control, especially of late-season flushes of these annual grasses. Chloroacetamide herbicides, including metolachlor, alachlor, and acetochlor, also provide good residual control of annual grasses and suppression of yellow nutsedge. These herbicides can be used in combination with atrazine, or in combination with atrazine and simazine.

Control of Triazine-Resistant Pigweed

Triazine-resistant pigweed is a major problem in a large part of our no-tillage corn acreage. The weed has no susceptibility to the triazine herbicides.Residual chloroacetamide herbicides afford fair-to-good control of this weed with optimum activation rainfall. If there is not  sufficient rainfall for activation or if very heavy rainfall occurs early in the season, pigweed control with these compounds will be inadequate, and a postemergence herbicide application will be required. Excellent preemergence residual control of pigweed can be obtained when flumetsulam, mesotrione, or pendimethalin are included in the pre-emergence herbicide application. These compounds are available in prepackaged mixes.

One product used extensively in Virginia no-till corn, Lumax, contains atrazine and metolachlor for general residual weed control, plus mesotrione for residual control of pigweed.

Control of Perennial Broadleaf Weeds

In the absence of tillage, herbaceous perennial broadleaf weeds can become very troublesome in no-till corn plantings. These species must be controlled with systemic herbicides at growth stages when translocation towards underground perennial plant structures is maximized. Generally, these perennials have not emerged at the time of planting, and making applications before planting are ineffective.

In most cases, the use of glyphosate-resistant corn hybrids represents the most effective overall method for perennial broadleaf weed control in no-till corn. Growers should also consider control of these perennials in rotational crops. Where soybeans are part of the rotation, perennial broadleaf weed control should be considered in glyphosate-resistant soybeans, because the soybean canopy is extremely effective in aiding the control of these species.

Control of Perennial Grasses

There are several excellent postemergence methods for perennial grass control in no-till corn. Johnsongrass can be controlled with nicosulfuron or with glyphosate in a glyphosate-resistant corn hybrid.Because of potential maize dwarf mosaic virus transmission to corn from dying johnsongrass following these applications, maize dwarf mosaic virus-tolerant corn hybrids must be used where postemergence johnsongrass herbicide programs will be employed.Bermudagrass control in no-till corn requires the use of glyphosate in glyphosate-resistant corn hybrids.Several postemergence herbicides, including halosulfuron, mesotrione, and glyphosate, can be used for the control of yellow nutsedge.

(Source – http://pubs.ext.vt.edu/424/424-030/424-030_pdf.pdf)

Read more

Potential monitoring of crop production using a satellite-based Climate-Variability Impact Index

Crop monitoring and early yield assessment are important for agriculture planning and policy making atregional and national scales. Numerous crop growth simulation models are generated using crop state variables and climate variables at the crop/soil/atmosphere interfaces to get the pre-harvest information on crop yields. However, most of these models are limited to specific regions/periods due to significant spatial–temporal variations of those variables. Furthermore, the limited network of stations and incomplete climate data make crop monitoring and yield assessment a daunting task. In addition, the meteorological data may miss important variability in vegetation production, which highlights the need for quantification of vegetation changes directly when monitoring climate impacts upon vegetation. In this sense, remotely sensed metrics of vegetation activity have the following advantages: a unique vantage point, synoptic view, cost effectiveness, and a regular, repetitive view of nearly the entire Earth’s surface, thereby making them potentially better suited for crop monitoring and yield estimation than conventional weather data. For instance, it has been shown that the application of remotely sensed data can provide more accurate crop acreage estimates at national/continental scales. Furthermore, numerous field measurements and theoretical studies have demonstrated the utility of remotely sensed data in studies on crop growth and production. These two applications suggest the feasibility of large-scale operational crop monitoring and yield estimation.

Empirical relationships between the remotely sensed data and crop production estimates have been developed for monitoring and forecasting purposes since the early 1980s. For instance, Colwell found a strong correlation between winter wheat grain yield and Landsat spectral data. However, these relationships did not hold when extended in space and time (Barnett and Thompson, 1983). Later, various other vegetation indices generated from Landsat data, such as the ratio of the reflectance at near infrared to red and the normalized difference vegetation index (NDVI) were used in yield estimation of sugarcane, wheat, and rice. The Landsat series have a spatial resolution of 30 m and can provide reflectance data from different spectral bands. However, these highresolution data require enormous processing effort, and  may not be applicable for surveys of large-area general crop conditions.

Vegetation indices derived from data from the Advanced Very High Resolution Radiometer (AVHRR) were also used for crop prediction, environmental monitoring, and drought monitoring/assessment. For example, found that millet yields in northern Burkina Faso are linearly correlated with the AVHRR NDVI integrated over the reproductive period. Similarly, Hochheim and Barber found that the accumulated AVHRR NDVI provided the most consistent estimates of spring wheat yield in western Canada. The Vegetation Condition Index (VCI) derived from AVHRR data is widely applied in real-time drought monitoring and is shown to provide quantitative estimation of drought density, duration, and effect on vegetation. The VCI can separate the short-term weather signals in the NDVI data from the long-term ecological signals. According to Domenikiotis , the empirical relationship between VCI and cotton yield in Greece are sensitive to crop condition well before the harvest and provide an indication of the final yield. Unfortunately, the AVHRR data are not ideally suited for vegetation monitoring.

Data

1. The MODIS land-cover classification product identifies 17 classes of land cover in the International Geosphere–Biosphere Programme (IGBP) global vegetation classification scheme. This scheme includes 11 classes of natural vegetation, 3 classes of developed land, permanent snow or ice, barren or sparsely vegetated land, and water. The latest version of the IGBP land-cover map is used to distinguish croplands from the other biomes in this research.

2. MODIS LAI

The retrieval technique of the MODIS LAI algorithm is as follows. For each land pixel, given red and near infrared reflectance values, along with the sun and sensor-view angles and a biome-type designation, the algorithm uses model-generated look-up tables to identify likely LAI values corresponding to the input parameters. This radioactive transfer-based look-up is done for a suite of canopy structures and soil patterns that represent a range of expected natural conditions for the given biome type. The mean value of the LAI values found within this uncertainty range is taken as the final LAI retrieval value. In certain situations, if the algorithm fails to localize a solution either because of biome misclassification/mixtures, high uncertainties in input reflectance data or algorithm limitations, a backup algorithm is utilized to produce LAI values based upon the empirical relationship between NDVI and LAI (Myneni et al., 1997).

The latest version of MODIS global LAI from February 2000 to December 2004 was taken to characterize the crop activity in this study. The 8-day LAI products are distributed to the public from the Earth Observing System (EOS) Data Gateway Distributed Active Archive Center. The 8-day products also provide quality control variables for each LAI value that indicate its reliability. The monthly global product was composited across the 8-day products using only the LAI values with reliable quality. The monthly global products at 1-km resolution with Sinusoidal (SIN) projection are available at Boston University. In this paper, monthly LAI at 1-km resolution are used to generate our Climate-Variability Impact Index. As these will be compared with estimates of crop production reported at county/state-levels, the vegetation-based CVII fields were aggregated over the corresponding counties/states using the county bound arias 2001 map from the National Atlas of the United States.

3. AVHRR LAI

AVHRR LAI is used as a substitute for the MODIS LAI to examine the temporal characteristics of vegetation activity over longer time periods. The AVHRR LAI is derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI produced by NASA GIMMS group. Monthly LAI from 1981 to 2002 at 0.258 were derived based on the empirical relationship between NDVI and LAI for different biomes. Literature works show that this empirical relationship might be different for the same biome at different locations. To eliminate this effect, models are generated for each pixel to calculate GIMMS LAI from GIMMS NDVI. The MODIS LAI and GIMMS NDVI overlapped from March 2000 to December 2002, which provides a basis for generating a piecewise linear relationship between these two products. Once the coefficients of the linear model are calculated, the whole range of GIMMS NDVI can be converted into GIMMS LAI, which is consistent with the MODIS products. Our preliminary results indicate a good agreement between GIMMS LAI and MODIS LAI at quarter degree resolution with less than 5% relative difference for each main biome (results not shown).

4. GIMMS NPP

In this research, we also use model-generated estimates of Net Primary Production (NPP) from Nemani  as a predictor of crop production. This NPP is a monthly product from 1982 to 1999 at a spatial resolution of half degree. This global NPP product was generated as follows. GIMMS NDVI were first used to create LAI and FPAR with a 3D radiative transfer model and a land-cover map as described in Myneni. Then, NPP was estimated from a production efficiency model (PEM) using the following three components: the satellite-derived vegetation properties, daily climate data, and a biome specific look-up table of various model constants and variables. Further details can be found in Nemani et al. (2003).

5. Crop production

Crop production data from several sources are used in this research. We focus upon total production, as opposed to yield, for instance, because although the two are highly correlated with each other, total production is typically the parameter of interest for crop monitoring and yield prediction. In this paper, we will explicitly refer to ‘‘production’’ when discussing quantitative results, however for simple qualitative statements wesometimes retain the generic term ‘‘yield’’ as synonymous for ‘‘production’’. The country-level crop production from 1982 to 2000 in European countries is from FAOSTAT 2004 data set. The county-, district-, and state-level production data in United States are from the National Agricultural Statistics Service (NASS) at United States

Department of Agriculture (USDA) USDA provides two independent sets of county crop data: one is a census of agriculture, which is released every 5 years; the other one is annual county crop data, which is based on reports from samples. We used the annual crop estimates in this study. Due to the processing effort required for the fine resolution remotely sensed data, we studied two crops (corn and spring wheat) in two US states (Illinois and North Dakota) at county- and district-scales. At coarser scales, we expanded the regions to include Illinois (IL), Minnesota (MN), Michigan (MI), Iowa (IA), Indiana (IN), and Wisconsin (WI) for corn; to North Dakota (ND), Montana (MT), Minnesota (MN), and South Dakota (SD) for spring wheat; to Kansas (KS), Oklahoma (OK), Colorado (CO), and Nebraska (NE) for winter wheat. The county- and district-level data of Illinois and North Dakota are from 2000 to 2004; the state-level data are from 1982 to 1999.

(Source –  http://cybele.bu.edu/download/manuscripts/zhping02.pdf)

Read more

Precision Agriculture and the Ukrainian Reality

The Committee on World Food Security research shows that nowadays global welfare largely depends on the dynamics of food production. We have learned how to synthesize scarce sapphire crystals and how to replace expensive petrol with biofuel, but still unable to cope with the hunger problem. The world population is growing, while the area of free land for the expansion of crops is limited – another deforestation or swamps draining is a potential threat of ecological disaster. Profits from sunflower oil, wheat or sugar sales in the world market are comparable to machinery and coal trade profitability. The question is why more than a third of Ukrainian agricultural enterprises are unprofitable in such favorable environment? What methods of doing business help global leaders of the agricultural market work more efficiently than domestic companies that have rich Ukrainian soils?

Ukrainian-type Efficiency of Agricultural Business

The necessity to pursue the way of the intensive agricultural development became evident to the most developed countries long ago. The most recent developments in science and technology are applied not only to space rocket engineering, but to work in the field as well. Modern agricultural machinery is equipped with computers, new varieties of crops are grown in the laboratories, whilst satellites and drones are watching crops of large landowners in real-time. Nowadays agriculture of developed countries turns to an absolutely new level of competition – the efficient one. In a market where you can not control the price, you must manage the prime cost or go away. Agricultural market has become so global that the most effective way to manage profitability is to manage production costs. Modern wars are held without tanks and infantry – just one rocket is enough if it hits the enemy’s camp navigated from space and powered with minimum resources, but reaching maximum effect. The same processes take place in the agrarian sector. All the efforts have been turned to allocate available resources with the highest efficiency to achieve the utmost result.

The general picture of precision agriculture in Ukraine calls for new efficient reforms at least to overtake the leading world agrarian producers. Let us turn to statistics. According to the State Statistics Service of Ukraine, since the proclamation of independence in Ukraine the level of the plowed area reached nearly 72% of total country territory which is one of the highest indices value in the world. At the same time the production volume of cereals/legumes and sugar beets per capita declined in comparison with 1990 by 13% and 65% respectively. This means that the extensity of using acreage has not justified itself. The same is about the excessive use of another available resource in Ukraine – manpower. In this country over 16% of the population is employed in the agricultural sector (according to the FAO research, this figure does not exceed 9% for the developed countries), but the number of added value created by one employee is “only” 2,500 dollars per year (in the U.S. it equals to 51,000 dollars, in Romania – 9,700, in Poland – 3,000).

Significant gap between leading countries and Ukraine can be actually closed only with the help of many millions of investments that seems very difficult within the global financial crisis. The lack of “long-term money” (the payback period in the agriculture reaches at least 5-7 years even in the most optimistic scenario) multiplied by the lack of investments defend guarantees, inflation and instability of commodity markets, creates difficulties to obtain financing even for the largest agricultural holdings. However, it is hardly possible to overtake the world leaders by the other way in the current business environment: according to the World Bank, Ukrainian reality shows us that Ukraine has one of the lowest rates of fertilizer expenses and tractor use per unit of cultivated area, the average productivity depending on the crop is lower than world analogs in two or three times, each planting and harvesting campaign has deficiency of oil products whose production in Ukraine is insufficient because of a lack of raw materials and general equipment deterioration.

Let’s Change the Principles!

In such a situation the Ukrainian agricultural sector needs to find alternative ways for the further development. It’s half of the problem when we lose our export positions – much worse is when we have to purchase the agricultural production for foreign currency abroad like it regularly happens with sugar. If oil, natural gas, phosphates and other raw materials for agriculture are rising in price, it is better to optimize their consumption per unit of cultivated area to achieve maximum efficiency in each field, for each type of crops. The one who will be able to offer worthy quality at a reasonable market price, without doing himself out of his share and his interest as an entrepreneur, can be a winner in the market competition. It is necessary to abandon an unprofitable principle of explicit loss and to switch to smart management “as needed”. What’s the use of distributing fertilizers evenly, if only few fields or areas within the field need more fertilizers, while the others have the surplus? Does it make sense to go around the crops every day to check whether everything is in order, if there are systems for identifying problem fields? Why do we use the weather forecast for the nearest settlement, if we need the weather for a particular field in two dozen kilometers away? These are the questions which have become the philosophy of agribusiness in developed countries but are not that popular among the Ukrainians.

Precision Agriculture

It is possible to achieve the result described above with the help of so-called “precision agriculture” – the use of the concept about the existence of heterogeneity within a single field or planting. Such features could be caused by the landscape specifics, soil composition and proximity of mineral layers, condition of groundwater, climatic characteristics and features of crops which were grown on the area before. Precision agriculture foresees the continuous monitoring of crops and soil for the operational planning of the range of actions to optimize the condition of problem areas. For example, if a separate section of the field area of 20 hectares has a small yellow spot area of 0.5 hectares, it is not necessary to fertilize or to impose additional watering sessions to the whole field – it is enough just to handle problem areas. This will result in much lower costs of fertilizer, POL, wages and depreciation of equipment, even more – it will save working hours of equipment and employees for other tasks.

Monitoring Systems

 Monitoring of fields can be realized in different ways: driving round fields, collecting and analyzing soil samples, using sensors and aerial photography. At the current level of technological development, one can launch aircraft without a pilot but equipped with sensors,  photo- and video cameras and filled with fuel to make a 30-minute flight. However, the complexity of control and maintenance of such equipment, as well as the size of field (over 100 hectares) make this work scheme quite expensive and hardly feasible. For such a scale, agrarians opt for satellite space shooting , the processing of which allows to monitor crops and to make decisions about pointed application of fertilizers, insecticides or herbicides, irrigation or other actions  based on the handling of images with overlaid in red and infrared spectrum. In addition, data from such programs can be uploaded in any electronic device or in the onboard computer of agricultural machinery making it easier to set tasks for employees in the agricultural enterprise.

Satellite crop monitoring systems are successfully used in many countries of America, Europe and the CIS. The most well-known and effective providers of this service are such companies as Cropio (USA/Germany), Astrium-Geo (France), Mapexpert (Ukraine), Vega (Russia). The use of these systems allows not only to monitor efficiently the condition of fields, but also to receive reports and notifications about the most important issues through Internet or sms, to make forecasts of the field productivity and the entire enterprise, to receive related information about the agricultural markets, currency rates and prices for agricultural products in certain markets, to compare current and historical indices of vegetation, soil moisture, content of fertilizers.

Cost Savings Plain to See

Few of us has thought that it takes at least 0.4 liters of fuel or UAH 1.2 to drive round of field area of 1 ha (100 m*100 m) 8 times per year. According to the American Institute of the Precise Farming, the differentiated fertilization brings savings of 10% per hectare. Having summed these and other explicit and implicit costs, we can obtain savings of at least UAH 146 per hectare using satellite observation in Ukrainian agriculture.

If in the Ukrainian realities domestic businessmen are progressive enough and ready to start running the management according to new standards using the techniques of precise agriculture, it is quite possible that eventually Ukraine will become one of the absolute world leaders in the production of some crops, and a number of major agricultural exchanges will be opened on its territory involving customers from around the world. The Ukrainian agronomist who uses services of satellite crop increases its professional efficiency and management methods makes a real “jump” from the Stone Age to the age of high technology. Such an agronomist is in the same league with his colleagues from around the world leveraging not only Soviet scientific school knowledge, but also the global scientific progress.

The result is smaller staff of agronomists, lower fuel and fertilizer costs. Having one or more of such satellite monitoring centres, the agrarian can cut costs that previously put at risk the profitability of enterprise, and what is even more crucially, optimize the quality and return of each resource, be it land, workers, machinery or fertilizers. It is always better to make qualitative changes rather than quantitative ones in each operation of business cycle. The customer is ready to buy the product at a price not higher than a certain threshold which occurs as the average price of all sellers, in a free market he will not overpay for our inability to run a business efficiently. As advertising recalls: “Why should I pay more?”

Summing up, let us recollect another wise saying: “Everything dies without sustained development.” Nowadays it is not enough to own hundreds of hectares of high-quality black soil or endlessly increase fleet of vehicles. Once in a while one should step back and take a look at unproductive attempts to invest and think how to make more money. The authors of political economy put it that the possessing of right information helps to make a profit.

Tags: precise agriculture, Ukraine, satellite, fertilizer, wheat, barley, legumes, sunflower, agronomist, Cropio

Read more

Green Class

State-of-the-art technologies use efficiency can not be unambiguously estimated. High-capacity productive tractors cause depth soil degradation caused by machines weight which can not be eliminated via no-till technology use as far as this problem requires larger technical efforts and deep tillage works lead to faster nutrients withdrawal. Chemical fertilizers cause soil depletion, while certain profitable crops growing leads to further higher production costs. It is rather questionable if we should replace machines with human labour, still we consider it helpful to remind about natural fertilizers to replace synthetic ones.

Some experts identify natural fertilizers as sideration – the process of ploughing under green mass from intentionally cultivated plants in order to enrich soil with nitrogen and organic nutrients. Still we can include the rests of current crop as natural fertilizer. For example we can gather, recycle and distribute the rests of straw as manure or utilize natural ashes. The ashes from only one sunflower top consist about 30 g of potassium which plants require to raise water circulation efficiency and apparently prevents from drought.

In fact “green fertilizers” use is capable in two ways: ploughing under mown crops on its cultivation field or transportation to the place use. In some cases it is possible to interplant the main crop on the field covered with green manure. Talking about the method drawbacks we must mention constrained delays in main crops cultivation provoked by green manure cultivation periods, diseases spreading and additional efforts aiming to plough under the green mass. Nevertheless the technology advantages significantly exceed the effect of the above-mentioned hardships. First of all we must mention fertilizers economy: for example green manure of legumes increases nitrogen receipts. Moreover 50% of synthetic nitrogen fertilizers quantity runs out within the first 3 month, while natural nitrogen withdraws much more slower. Secondly, natural fertilizers slow down soil degradation and even restore problem soil characteristics, while it supports natural humus generation. Thirdly, the crops with minimal chemicals content fit high quality standards and can be sold at a relatively higher price. Finally, this technology brings essential economical effect as far as it assures from fertilizers prices increase and problems with fertilizers long-term storage. Within 2009-2012 ammonia contract price has tripled (Middle East FOB, Yuzhnyy FOB).

“Green fertilizers” are particularly useful in cases of dung substitution in those countries which have problems with livestock population shortage.

There is no doubt that natural fertilizers can not be considered as the universal measure, while the resources scarcity and food deficit force agrarians to apply more and more extensive business measures. Not all agrarians are willing to wait until green manure grows to use it as fertilizer – it looks much more profitable to get two crops in a same time and to sell them at a reasonable price. But if we speak about long-term efficiency – it would be great to remind an old proverb “the miser pays twice” and look at “green fertilizers ” consumption increase all over the world.

Tags: fertilizers, green manure, wheat, sunflower, tractor, ammonia, no-till, “green fertilizers”

Read more