Crop responses to climatic variation

Crop responses to climatic variability and extremes

The yield productivity of many crops has risen over the past 40 years. Rates of yield increase in Europe have ranged from 0.8% (oats; Avena sativa) to 2.6% (triticale; Triticosecale) per year (Ewert et al. 2005). Rates of increase in wheat yields differ between European countries, but regional variation about the linear trend is less clear. More southerly European countries have lower rates of wheat yield increase than more northern ones, suggesting that weather factors such as temperature and precipitation play a more determining role in yield than in the north.. The fact that there have been lower rates of increase in yields in areas of Europe with more extreme conditions and that deviations from a linear trend have increased, points to the conclusion that warming since the start of the 1990s (Schär et al. 2004) has started to affect European wheat yields.

For crops, both changes in the mean and variability of temperature can affect crop processes, but not necessarily the same processes. Some crop processes, mostly related to growth such as photosynthesis and respiration, show continuous and mainly nonlinear changes in their rates as temperature increases. Rates of development and progression through a crop life cycle more often show linear responses to temperature. Both growth and developmental processes show temperature optima, whereby process rates increase over a range but thereafter flatten and decrease. For example, the light-saturated photosynthesis rate of C3 crops such as wheat and rice is at a maximum for temperatures from about 20–32 °C; total crop respiration, the sum of the growth and maintenance components, shows a steep nonlinear increase for temperatures from 15–40 °C followed by a rapid and nearly linear decline. The threshold developmental responses of crops to temperature are often well defined, changing direction over a narrow temperature range, as will be seen later.

An experimental study of climatic variability and wheat

Physiological responses to temperature changes in plants may occur at short or long time-scales (Wollenweber et al. 2003). Rapid changes in enzymatic reactions caused by differential thermosensitivity of various enzymes can deplete or result in accumulation of key metabolites. In addition, short-term effects involving altered gene expression, such as heat-shock protein synthesis, are likely to occur. Longer-term responses include alterations in the rate of carbon dioxide (CO2) assimilation and electron transport per unit leaf area, and impaired cell anaplerotic carbon metabolism, sucrose synthesis and carbon (C) and nitrogen (N) partitioning within and between organs (Jagtap et al. 1998). Altered carbon availability brought about by these events will affect uptake, transport and assimilation of other nutrients, disturb lipid metabolism and injure cell membranes (Maheswari et al. 1999), resulting in changes in growth rates and grain yield (Al Khatib & Paulsen 1999). However, temperature responses for specific physiological processes do not always relate directly to growth, because the latter is an integration of the effects of temperature on total metabolism (Bowes 1991).

The developmental stage of the crop exposed to increased temperatures has an important effect on the damage experienced by the plant (Slafer & Rawson 1995), but experimental studies of the effects of temperature variability on crop productivity are rare. This is mainly because of the difficulties in establishing and maintaining a temperature regime where a mean climatic value can be held constant between treatments that vary the amplitude of temperature (Moot et al. 1996). A solution to this is to examine the effects of extreme conditions at particular developmental stages (Ferris et al. 1998), in which the extreme conditions are defined with reference to literature (Porter & Gawith 1999). Wollenweber et al. (2003) tested the null hypothesis that wheat plants react to two separate periods of high temperature as if they were independent of each other. The chosen stages were the double-ridge stage of the apical meristem, which is close in time to the transition from vegetative to reproductive development of the apical meristem, and anthesis when extreme temperature events interfere with the development of fertile grains, as meiosis and pollen growth are affected (Wallwork et al. 1998). The experimental design, and the extreme temperature conditions were defined as a heat period of eight days of 25 °C at the double-ridge stage and/or a heat event of 35 °C at anthesis. Biomass accumulation, photosynthesis and the components of grain yield were analysed. While a high temperature event of 25 °C at the double-ridge stage is not a stress event sensu strictu for wheat, reproductive spikelet initiation can be impaired (Porter & Gawith 1999) and 25 °C is 13 °C higher than mean daily temperatures measured over 30 years at the experimental site in Denmark.

Grain yields were significantly lower in the treatments with high temperatures at anthesis and at both developmental stages. The major yield component reduced by the treatments was the harvest index; that is, the proportion of total dry matter invested in grain. The harvest index was lower in plants experiencing heat periods because their grain number per plant was reduced by 60% . However, there was no significant difference in the grain yield of plants as between those warmed at anthesis and those at double ridges and anthesis, meaning that the plants experienced the warming periods as independent and that critical temperatures of 35 °C for a short-period around anthesis had severe yield reducing effects. The conclusions from such results for climate change are that yield damaging weather signals for cereals such as wheat are in the form of absolute temperature thresholds, are linked to particular developmental stages and can be effective over short time-periods. This means that yield damage estimates of coupled crop–climate models need to have a maximum temporal resolution of a few days and incorporate models of crop phenology to deal with the overlap between such extreme weather events and crop sensitivity to them.

In contrast to the effects on developmentally linked processes, no significant differences were seen in the relation between light-saturated photosynthesis and leaf internal CO2 concentration for the heat treatments. A heat episode during DR increased the rates of light-saturated photosynthesis (Asat) in green leaves slightly. There were no significant differences in Asat and carboxylation efficiency, reinforcing the conclusion that the principal effects of high temperatures are on developmental processes, such as flowering and the formation of sinks for assimilated carbon, which in itself either is stimulated or is little affected by short-term warming. An extreme heat episode during vegetative development does not seem subsequently to affect the growth and developmental response of wheat to a second heat event at anthesis, and high-temperature episodes seem to operate independently of each other.

Crop temperature thresholds

In addition to the linear and nonlinear responses of crop growth and development processes described above, short-term extreme temperatures can have large yield-reducing effects on major crops. These effects were reviewed for wheat by Porter & Gawith (1999) and, for annual crops in general, by Wheeler et al. (2000). A general point arising from these reviews were that temperature thresholds are well defined as absolute threshold temperatures above which particularly the formation of reproductive sinks, such as seeds and fruits, are adversely affected, as seen in the experiment described above.

The largest standard error found was 5.0 °C for the maximum temperature for root growth, followed by 3.7 °C for the optimum temperature of root growth. Others, such as the base and optimum temperatures for shoot growth, the optimum temperature for leaf initiation and base temperature for anthesis have standard errors of less than 0.5 °C. Thus, the consensus is that functionally important temperatures for wheat are conservative when compared between different studies.

A crop that is important in the developing world is groundnut (Arachis hypogaea L.). This is an important food crop of the semi-arid tropics, including Africa, and can experience temperatures above 40 °C for periods during the growing season (Vara Prasad et al. 2000). The harvestable seeds of groundnut are formed following flowering and fruiting periods. When exposed for short-periods at high temperatures of up to 42 °C just after flowering, a clear relationship between fruit set and mean floral temperature was found (Vara Prasad et al. 2000). From between 32 and 36 °C and up to 42 °C, the percentage fruit set fell from 50% of flowers to zero and the decline in rate was linear, illustrating once more the sharpness of response of crop plants to temperatures between 30 and 35 °C during the flowering and fruiting periods.

Various literature sources have identified similar patterns for other important food crops such as maize and rice. For example, maize exhibits reduced pollen viability for temperatures above 36 °C; rice grain sterility is brought on by temperatures in the mid-30s °C and similar temperatures can lead to a reverse of the vernalizing effects of cold temperatures in wheat. What is perhaps more surprising than the consistent damaging effects of high temperatures in food crops is that cold-blooded animals also exhibit threshold temperature responses for various activities. As with plants, the lethal limits are the widest, followed by activity limits, development and growth with the reproductive limits being the narrowest from 24 to 30 °C, the upper value interestingly close to the limits seen for many crop plants, but this is presumably a coincidence. It would be very useful to have equivalent diagrams for the major crop plants in the world and thereby provide specific quantitative information on the probability and consequences, in other words the risk, from crop damaging climate change at the regional or country level. This would further be the linkage between crop physiology, crop agronomy and climate science (Porter 2005).

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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.


            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.


            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.


            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.


            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.

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Spectrum of the Pests on Cereal Crops and Influence of Soil Fertilisation

Different biotic factors such as predators, parasitoids and different pathogens affect the pests of cereals. Also affecting them are the abiotic factors temperature, rainfall, humidity, wind and sunshine (Sehgal 2006).

Climatically, the slowly rising temperatures are very important for the spectrum of pests. Winters are shorter, temperatures below freezing occur on average on fewer days than before and the soil is frozen to shallower depths. This results in pest species occurring at northern localities, whereas  before they had appeared only at lower latitudes (Gallo 2002).

Past research has shown that fertilisation of the soil affects a pest’s presence. Fertilisation had a positive effect on the crops, especially on their height and density, leading to a higher presence of pests. Stimulation of plant growth and development is the main goal of fertilisation. Regeneration of the plants is important if the pests damage the plants.

Organic fertilisation is very important because it suppresses the plant pathogens and also affects the frequency of pests (Sokolov 1991). Healthy vital plants are preferred by many pests on cereal species (Price 1991; Breton & Addicott 1992; Preszler & Price 1995).

The variety of a crop can also affect the occurrence of a pest. Ovipositonal behaviour, variability of eggs, growth and survival of the young larvae of the cereal leaf beetle were adversely affected by the trichome density on wheat leaves(Schillinger& Gallun 1968). Eggs per plant and larval survival decreased asthe length and density of trichomes increased (Hoxie et al. 1975).

Differences in the yields between the varieties and the year’s volume in the same level of the damage were detected. Later varieties were better able to compensate for the damage than earlier varieties in years with similar weather (Šedivý 1995).

Influence of a new farming system on pests and their natural enemies has began a few years ago (Khamraev 1990; Pfiffner 1990; Xie et al. 1995).

Material And Methods

Our research was conducted at Nitra-Dolná Malanta. The whole area (530 m2 ) used in the tree-year experiment (2004–2006) was split into 8 identical plots (each of 60 m2 ) separated by a 1 m belt where soil was superficially cultivated.

Pests were monitored on spring barley cvs. Jubilant (2004) and Annabell (2005, 2006), and on winter wheat cvs. Samanta (2004) and Solara (2005, 2006). At every sampling date 5 m2 of winter wheat and spring barley was swept using a standard sweeping net. Collecting began at the shooting phase, ended at wax ripeness of cereal crops and was done every 7–10 days, depending on the weather.

The insects collected from each crop were determined. Spiders were removed from samples, and aphids were excluded from the analysis at they were used in another study. We determined the influence of fertilisation and nutrition on the pests in the control variants(0) without fertilisation, and variants fertilised (F) with manure (40 t/ha) + fertilisers(winter wheat for 7 t yield: 70 kg N/ha, 30 kg P/ha, 0 kg K/ha; spring barley for 6 t yield: 30 kg N/ha, 30 kg P/ha, 0 kgK/ha).Climatic data were provided by the meteorological station nearthe Slovak University of Agriculture at Nitra, Slovak Republic. All results were evaluated with mathematical-statistic analysis Statgraphics.

Results and disscusion

Spring barley

In 2004, insects were first collected in the last decade of April; during next 2 years collecting began in the first decade of May. The beginning of collecting was affected by the weather during the years. The last insects occurred in the first decade of July. The date of the maximum occurrence of the pests changed during the research. Maximum of the insects was recorded between the May and (307pieces/5m2 ) a June (357pieces/5 m2) in 2004.The abundance of the insects decreased at about 45% after this time. The lowest abundance of insects was in May during the low average day temperatures. Maximum occurrence of total insects was in the last decade of May and in the first decade of June. Our results are similar to results of Gallo (2002), who maximum of the insects recorded in the first decade of May and June. Only one maximum of occurrence was recorded in 2004.Only one maximum was recorded in 2006 too. Two maximum of occurrence were recorded in 2005.

The maximum occurrence of the pests(89 pieces/5 m2 ) was recorded on spring barley at the beginning of the June in 2006. Phyllotreta (18 pieces/5m2 ), Thysanoptera (21 pieces per 5 m2 ) and Oulema gallaeciana (12 pieces/5 m2 )were dominant pests. Other authors presented the same spectrum of dominant pests (Gallo & Pekár 1999). There were no insects recorded on spring barley in July. The spring barley was in the wax ripeness. Abundance of the natural enemies increased at about 28% in the second decade of June but it decreased compared to the first decade about 80% by the end of month.

Different occurrence of the insectswasin20.The maximum of the collected insects (102 pieces per 5 m2 ) was at the end of June. The total number of insects 114 pieces/5 m2 was recorded in the first decade of June and from which there were 84pieces/5m2 pests. There was recorded maximum amount of natural enemies(14 pieces/5 m ). The second maximum of total insects(262 pieces/5m2) and also pests (177 pieces/5 m2 ) was recorded in the last decade of May. The collecting was realised in the first decade of July in 2005. The amount of the pests decreased at about 34% and natural enemies at about 63% in 2005. During the three years study the effect of fertilisation was monitored in the spring barley (Figure 2).According the results, more pests were recorded on fertilised variants.The more pests were recorded on the non-fertilised variants only in 2004. Our results are different from results of Samsonova (1991), according which the fertilisation had no effect on the occurrence of the pests. Levine (1993) results are similar to ours.

The occurrence of the insects had the similar character on fertilised and non-fertilised variants. The relation between fertilised and non-fertilised variants had not statistically significant difference

Total amount of collected insects on spring barley had increasing tendency during the all years. Total amount of the pests was 1630 pieces per 5 m2 collected during the entire period in2004.

This number decreased at about 55%in 2006.The amount of natural enemies had also decreasing tendency. While 66 pieces/5 m2 of natural enemies were collected in 2004, this amount decreased at about 67% in 2006 (Table 1). This drop could be caused by higher temperature during the year 2006.

According to Honěk (2003), McAvoy and Kok (2004), the temperature influenced occurrence and development of the pests in crops. Difference between the year 2004 and the other years 2005 and 2006 was statistically evident.

Winter wheat

The beginning of collecting was realised in the second decade of April and the last collections were realised in the first decade of July during the years 2004–2006.The last collection was realised atthe end of June in 2006.The beginning was affected by the weather during the years.

The first maximum of the pests was recorded in the last decade of April(247 pieces/5 m2 )in 2004. The second maximum was recorded at the end of May (350 pieces/5 m2 ) and at the beginning of June(285 pieces/5 m2 ).Thrips (44 pieces/5 m2 ) had the highest occurrence. The cereal leaf beetle was recorded at most in April. It was found occasionally in the crops in the next time. Aphids(58 pieces/5 m2) were recorded in the crops in June. Maximum occurrence of the pests was recorded in the last decade of May (284 pieces/5 m2 ), the second one in the second decade of June (314 pieces/5 m2 ) in 2005. There were trips (26 pieces/5 m2 ) and flea beetle (38 pieces/5 m2 ) and also cereal leaf beetle(15 pieces/5 m2).The aphids were not recorded in this year. During the terms with the highest level of the pests, there were also recorded the highest occurrence of the natural enemies (53pieces/5 m2).

Our results were similar to the results of Gallo and Pekár (1999, 2001),which presented the same spectrum of the pests. The high occurrence of the pests was recorded also in the first(266 pieces/5 m2) and in the second (197 pieces/5 m2 ) decade of June. The highest occurrence had again thrips (18 pieces per 5 m2 ), flea beetle (24 pieces/5 m2) and cereal leaf beetle (10 pieces/5 m2). Frittfly Oscinella fritt was recorded (6 pieces/5 m2) during the entire year.

The higher occurrence ofthe pests could be caused by higher temperature during the year. According to Petr et al. (1987), and McAvoy and Kok (2004), the temperature influenced occurrence and development of the pests in the crops. The influence of the fertilisation was study during three years research on winter wheat.

According to our results, the higher occurrence of the pests was recorded on fertilised variants. The higher occurrence of the pests was recorded on the non-fertilised variants only in the year 2004. Our results are different from the results of the other author according which the fertilisation had no influence at the pests (Samsonova 1991).The similar results reached Levine (1993) and Gallo and Pekár (1999).The occurrence of the insects had the similar character on fertilised and non-fertilised variants. Maximum amount of the pests was recorded one decade sooner on the fertilised variants than on the non-fertilised variants. Relation between fertilised and non-fertilised variants had not statistically significant difference.

The total number of insects had a decreasing tendency during the three years and in both variants of fertilisation. While 3259/5 m2 of insects were collected in 2004, this number decreased by about 51% in 2006. This decrease was expressive on the non-fertilised variantsin 2006, it began on the fertilised variants after the first year. The same decreasing tendency had also pests and their natural enemies. Their occurrence decreased at about 59%during the study. Difference between the year 2006 and the other years 2005 and 2004 was statistically evident.

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The future of precision agriculture

Using predictive weather analytics to feed future generations

By 2050, it’s expected that the world’s population will reach 9.2 billion people, 34 percent higher than today. Much of this growth will happen in developing countries like Brazil, which has the largest area in the world with arable land for agriculture. To keep up with rising populations and income growth, global food production must increase by 70 percent in order to be able to feed the world.

For IBM researcher and Distinguished Engineer Ulisses Mello and a team of scientists from IBM Research – Brazil, the answer to that daunting challenge lies in real time data gathering and analysis. They are researching how “precision agriculture” techniques and technologies can maximize food production, minimize environmental impact and reduce cost.

“We have the opportunity to make a difference using science and technological innovation to address critical issues that will have profound effect on the lives of billions of people,” said Ulisses.

Optimizing planting, harvesting and distribution

In order to grow crops optimally farmers need to understand how to cultivate those crops in a particular area, taking into account a seed’s resistance to weather and local diseases, and considering the environmental impact of planting that seed. For example, when planting in a field near a river, it’s best to use a seed that requires less fertilizer to help reduce pollution.

Once the seeds have been planted, the decisions made around fertilizing and maintaining the crops are time-sensitive and heavily influenced by the weather. If farmers know they’ll have heavy rain the next day, they may decide not to put down fertilizer since it would get washed away. Knowing whether it’s going to rain or not can also influence when to irrigate fields. With 70 percent of fresh water worldwide used for agriculture, being able to better manage how it’s used will have a huge impact on the world’s fresh water supply.

Weather not only affects how crops grow, but also logistics around harvesting and transportation. When harvesting sugar cane, for example, the soil needs to be dry enough to support the weight of the harvesting equipment. If it’s humid and the soil is wet, the equipment can destroy the crop. By understanding what the weather will be over several days and what fields will be affected, better decisions can be made in advance about which fields workers should be deployed to.

Once the food has been harvested the logistics of harvesting and transporting food to the distribution centers is crucial. A lot of food waste happens during distribution, so it’s important to transport the food at the right temperature and not hold it for longer than needed. Even the weather can affect this; in Brazil, many of the roads are dirt, and heavy rain can cause trucks to get stuck in mud. By knowing where it will rain and which routes may be affected, companies can make better decisions on which routes will be the fastest to transport their food.

The future of precision agriculture

Currently, precision agriculture technologies are used by larger companies as it requires a robust IT infrastructure and resources to do the monitoring. However, Ulisses envisions a day when smaller farms and co-ops could use mobile devices and crowd sourcing to optimize their own agriculture.

“A farmer could take a picture of a crop with his phone and upload it to a database where an expert could assess the maturity of the crop based on its coloring and other properties. People could provide their own reading on temperature and humidity and be a substitute for sensor data if none is available,” he said.

With growing demands on the world’s food supply chain, it’s crucial to maximize agriculture resources in a sustainable manner. With expertise in high performance supercomputing, computational sciences, and analytics and optimization, IBM Research is uniquely able to understand the complexities of agriculture and develop the right weather forecasts, models and simulations that enable farmers and companies to make the right decisions.

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Comparison of the effect of liquid humic fertilizers

Maize (Zea mays L.) is one of the most highly consumed  crops, and the most important foodstuff after wheat and  rice around the world. The global production of maize is 604 million tons, with a planting area of up to 140 million hectares. Iran produces 2 million tons of maize on 350000 hectares of land. However, the production from hybrid maize seeds in Iran is highly limited (FAO, 2002).

This plant, photosynthetically, is of C4 type and thrives in tropical and semitropical climates (Emam, 2008) and is native for central and southern America (Khodabandeh, 1998). Based on its role in production of grain and forage and providing food for livestock, as well as its industrial use, maize has become an important crop in Iran, as well as in other parts of the world. Expanding the area under  maize cultivation in Iran in order to become self-sufficient is one the most important goal pursued by the government and as a result of implementing programs designed to increase grain maize production over the last few years, this crop has seen a very fast growth in terms of planting area and yield.

Humic substances (HS) are the result of organic decomposition of the natural organic compounds comprising 50 to 90% of the organic matter of peat, lignites, sapropels, as well as of the non-living organic matter of soil and water ecosystems. Authors believe that humic substances can be useful for living creatures in developing organisms (as substrate material or food source, or by enzyme-like activity); as carrier of nutrition; as catalysts of biochemical reactions; and in antioxidant activity (Kulikova et al., 2005). Yang et al. (2004) argued that humic substances can both directly and indirectly

affect the physiological processes of plant growth. Soil organic matter is one of the important indices of soil fertility, since it interacts with many other components of the soil. Soil organic matter is a key component of land ecosystems and it is associated with the basic ecosystem processes for yield and structure(Pizzeghello et al., 2001).

Classically, humic substances are defined as a general group of heterogeneous organic materials which occur naturally and are characterized by yellow through dark colors with high molecular weight (Kulikova et al., 2005).  Shahryari et al. (2011) experienced the effect of two types of humic fertilizers (peat and leonardite derived) on germination and seedling growth of maize genotypes. They reported that interaction of “genotype × solutions (peat and leonardite based humic fertilizers and control) was significant in terms of the length of primary roots.

Application of leonardite based humic fertilizer had a remarkably more effect on relative root growth of Single Cross 794 and ZP 434 than other genotypes. In their experiment, the relation between germination rate and primary roots was positively significant under the condition of application of both types of humic fertilizers; but there was not the same relation for control treatment.

They argued that all types of various humic substances as a biological fertilizer can have an either similar or different effect in early growth stages of maize, as peat and leonardite based fertilizers that they applied produced more seedling roots than control, however the length of coleoptiles was higher in treatment with application of leonardite based humic fertilizer and control than treatment with application of peat based humic fertilizer. They believe that if used in lower quantity these natural fertilizers can have a lot of effect on plant growth.

Hence, in order to recognize how effective they might be, investigations should be considered based on various amounts of humic fertilizers. Finally, they suggested that both peat and leonardite based humic fertilizers could be used to stimulate growth of primary roots in maize which are critical for an optimal establishment of maize in the field.

Gadimov et al. (2009) claimed that humic substances are natural technological products with a miraculous biological effect on crops and concluded that a scientific and practical program is required to make use of this technology in the world, particularly in developing countries. Also, Shahryari et al. (2009) concluded that potassium humate is a miraculous natural material for increasing both quantity and quality of wheat and can be used to produce organic wheat. Thus, application of biological products such as humic fertilizers to provide nutrition for crops can be one of the useful methods to achieve some of the objects of organic crop production.

In addition, Shahryari et al. (2011) studied the response of various maize genotypes against chlorophyll content of the leaves at the presence of the two types of humic fertilizers. In their experiment, solutions (two types of peat and leonardite based liquid humic fertilizers and control) and interaction of “genotypes × solutions” produced significant difference at 1% probability level in terms of chlorophyll content of the leaves. Genotypes such as Single Cross 704 and 505 had the highest index for chlorophyll content when treated by leonardite based humic fertilizer. Peat based humic fertilizer decreased the index for chlorophyll content in genotypes such as 500, OS499 and 505, while leonardite based humic fertilizer decreased the index for chlorophyll content of the leaves in genotypes such as Golden West and Single Cross 704. However, peat based humic fertilizer did not have such an effect on these two maize genotypes.

Meanwhile, leonardite based humic fertilizer had no effect on index for chlorophyll content of leaves in genotypes such as 500, OS499 and 505. Genotypes such as ZP677 and ZP434 produced no response against the application of the two types of humic fertilizers. This study was aimed to compare the effect of liquid peat and leonardite based humic fertilizers on the yield of maize genotypes in Ardabil Region.


This experiment was conducted at Agriculture Research Station of Islamic Azad University, Ardabil Branch (5 km west of Ardabil City) in 2009 – 2010 cropping year. The region has a semiarid and cold climate, where the temperature during winter season usually drops below zero. This region is located 1350 m above the sea level with longitude and latitude being 48.2°E and 38.15°N, respectively.

Average annual minimum and maximum temperatures are -1.98and 15.18°C, respectively; whereas maximum absolute temperature is 21.8°C; and mean annual precipitation has been reported to be 310.9 mm. The soil of the field was alluvial clay with a pH ranging from 7.8 to 8.2.

Vegetative materials included six maize genotypes prepared from the Agriculture and Natural Resources Research Center of Ardabil Province. The Experiment was conducted as split plot in the basisof randomized complete block design with three replications. The main factor included three conditions (peat based humic fertilizer; leonardite based humic fertilizer; without the application of humic fertilizer) and the sub factor included six maize genotypes (ZP677, Golden west, OS499, ZP434, Ns540 and Single Cross 704). Each of experimental blocks included 3 plots, 320 cm length in rows, with80 cm from each other containing plants at 20 cm distances.

Pretreatment of seeds were done on the basis of 220 ml per 10 L of water to be applied for 1 ton of seeds. Moreover, irrigation was done in flooding manner. Weed-fighting was done both mechanically and manually during all growth stages. Liquid humic fertilizer was prepared and applied based on 400 ml per 50 L of water for 1 ha of maize plantation. The prepared solution was sprayed upon the aerial part of the plants during 5th leaf stage, appearance of reproductive organs, flowering and grain filling stages. All the samples were taken randomly from competitive plants at middle rows. Study traits included grain number per ear row, number of grain row per ear, ear number, weight of 1000 grains, biological yield, vegetative yield and grain yield.

Statistical analysis

Analysis of variance of data and mean comparison of them was done using MSTATC and SPSS programs. Mean comparison was done using Duncan’s multiple range test, at 5% probability level. Due to abnormality of data for ear number and its high coefficient of variation, square root conversion was used to normalize it.


Results from analysis of variance for study traits suggest that there was a significant difference  between experimental conditions in terms of grain yield and biological yield at 1 and 5% probability levels, respectively. In addition, there was a nonsignificant difference between study genotypes in terms of all evaluated traits except for number of grain row per ear and wet biomass at 1% probability level. Furthermore, there was no difference observed between the interaction of genotype and experimental conditions for any trait being studied, and this was in agreement with the report of Shahryari et al. (2009). This means that under study genotypes had the same responses to potassium humate.

Moreover, results from mean comparison of data (Table 2) for studied genotypes indicate that genotype OS499 (110.70 g) had the highest 1000 grain weight, whereas genotype Single Cross (81.20 g) had the lowest 1000 grain weight on average. Based on mean comparison of 1000 grain weight, genotypes OS499 and ZP434 were placed in the same group as NS540, whereas genotype ZP677 was placed in the same group as Golden West. Genotype ZP677 (with a mean value of 15.48) and genotype ZP434 (with a mean value of 13.49) had the highest and lowest values of number per ear, respectively; and genotypes such as Golden West and Single Cross were placed in  the same group as NS540 and had no difference in terms of this trait. Genotype ZP677 (with a mean value of 20.89 ton/ha) and genotype OS499 (with a mean value of 16.93 ton/ha) had the highest and lowest biological yield respectively and genotype OS499 was placed in the same group as ZP434, whereas genotypes such as Golden West and Single Cross were placed in the same group as NS540. Genotype ZP677 (with a mean value of 108.68 ton/ha) was the best among other genotypes in terms of wet biomass, whereas ZP434 (with a mean value of 77.52 ton/ha) had the lowest value for wet biomass. ZP677 was placed in the same group as NS540, whereas genotypes Golden West and OS499 were placed in the same group as ZP434 and had no difference in terms of this trait.

Shahryari and Shamsi (2009a) reported that potassium humate increased the rate of biological yield of wheat from 3.26 to 3.72 g/plant; but it had no effect on harvest index. Also, they found that uses of potassium humate increased grain yield. Results from mean comparison of data  for experimental conditions being studied indicate that application of leonardite based liquid humic fertilizer produced the highest biological yield(21.99 ton/ha on average), whereas no application of humic fertilizer produced the lowest biological yield(14.97 ton/ha on average). In this respect, both types of applied humic fertilizers had similar effects. Application of leonardite based liquid humic fertilizer produced the highest grain yield (7.09 ton/ha on average) among the conditions being studied, whereas under the condition of without humic fertilizer obtained the lowest value(4.07 ton/ha).

Ayas and Gulser (2005) reported that humic acid leads to increased growth and height and subsequently increased biological yield through increasing nitrogen content of the plant. It has also been reported that application of humic acid in nutritional solution led to increased content ofnitrogen within aerial parts and growth of shoots and root of maize (Tan, 2003). In another investigation, the application of humic acid led to increased phosphorus and nitrogen content of bent grass plant and increased the accumulation of dry materials (Mackowiak et al.,2001). Humic acid leads to increased plant yield through positive physiological effects such as impact on metabolism of plant cells and increasing the

concentration of leaf chlorophyll (Naderi et al., 2002).

Also, spraying humic acid on wheat crop increased its yield by 24% (Delfine et al., 2002). In general, the results from this study indicate that the application of leonardite based humic fertilizer increased biological yield by 46.89% compared to control, whereas peat based humic fertilizer increased biological yield by 34.47% compared to control. Seyedbagheri (2008)evaluated commercial humic acid products derived from lignite and leonardite in different cropping systems from 1990 to 2008. The results of those evaluations differed as a result of the source, concentration, processing, quality, types of soils and cropping systems. Under their research, crop yield increased from a minimum of 9.4%to a maximum of 35.8%. However, application of humic fertilizer in this study increased the biological yield by 40.68% on average. Application of leonardite based humic fertilizer increased the grain yield of maize by 74%.

Also, peat based humic fertilizer increased the grain yield by 44.7%. Overall, the mean increase for grain yield under the condition of application of humic fertilizers was as high as 59.45%. Similar results were also presented by Shahryari et al. (2009b) on wheat. They reported increase of grain yield (by 45%) from 2.49 ton/ha to 3.61 ton/ha affected potassium humate derived from sapropel in normal irrigation conditions.


Results from this experiment indicate that the application of liquid humic fertilizer can positively affect the maize yield and some agronomic traits related to it. These desirable effects can be a consequence of its effect on the physiology of the maize. In general, application of humic acid can lessen the need for chemical fertilizers and subsequently reduce environmental pollution, and compared with other chemical and biological fertilizers, they are affordable. Finally, it can be said that application of humic fertilizer not only increases the yield of maize, but also can play a significant role in achieving the goals of sustainable agriculture

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Soil moisture and ways to measure it

The change in soil moisture dynamics determines the irrigation regime and influences not only the biological processes which occur in the soil, but also the water provision of crops, and overall, the harvest rate. Usually, while characterizing soil moisture dynamics, experts suggest the following points:

  • water-holding capacity (the ability to hold a certain amount of moisture)
  • permeability (the ability of water to pass through itself),
  • pumping (the ability to raise the water on the hair harp spaces between soil particles)
  • evaporation of soil moisture and water absorption.

The traditional model of moisture measurement is based on the calculation of water balance (the difference between precipitation, and evaporation of water consumption, which is considered to be very conditional, provides limited information and is not very accurate).

Soil moisture is a more adequate parameter to assess mois­ture sufficiency compared to precipitation. When relying on precipitation it is necessary to calculate the water balance that remains in the soil, i.e.: precipitation minus evaporation (that depends on the temperature) minus crop water consumption.

Modern technology makes it possible to measure soil moisture at a depth of (0-100cm) using satellites. This is possible due to their function of earth microwave scanning. The data is refined, taking into account rainfall and soil type. There is only one company at the moment that provides such data; it is the field management system Cropio. Taking into consideration satellite moisture measurement advantages – application of such kind of data as common practice is a question of time.

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Weather History

To get data on historical temperature figures, cloudiness and humidity indices for each month in different regions or countries all over the world you can use World Meteorological Organization database  by following this link.

Moreover, some crop monitoring systems (e.g. satellite vegetation monitoring systems) offer the option of precise weather forecast backed by historical database

Tags: World Meteorological Organization, forecast, weather, agriculture, temperature, cloudiness, humidity, crop, yield


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