مقالات رزومه دکتر پیمان کشاورز

 Soil is one of the main drivers of global warming through losing carbon in the form of CO2. On the other hand, its ability to sequester carbon is a suitable option for reducing CO2 emissions. Therefore, even few changes in carbon sequestration or decomposition of soil organic carbon affect the global atmospheric CO2 content. Although the soils of arid and semi-arid regions have low organic carbon content, they can sequester substantial amounts of carbon due to the large area of these regions. So, the Rothamsted carbon model was used to predict the impact of future climate changes on the amount of CO2 emissions and low soil organic carbon stocks in the semi-arid arable lands of Razavi Khorasan province. This model is one of the most widely used models for the study of soil organic carbon turnover and has been evaluated in a variety of ecosystems including grasslands, forests and croplands and in various climate regions. The RothC model is consists of five conceptual soil carbon pools, four active fractions and a small amount of inert organic matter (IOM) that is resistant to decay. The active pools splits into: Decomposable Plant Material (DPM), Resistant Plant Material (RPM), Microbial Biomass (BIO) and Humified Organic Matter (HUM). This model is able to reveal the effect of soil texture, temperature, rainfall, evaporation, vegetation and crop management on the soil organic carbon turnover process.

 The Rothamsted carbon model was calibrated and validated using data measured in 2020 and available data from the long-term field experiments in the semi-arid agricultural lands of Jolge Rokh. Then, by analyzing the climate change of the study area, the impact of climate change until the end of the current century on the amount of CO2 cumulative emissions, total organic carbon (TOC) and active carbon pools model were modeled and compared in the current climate and also climate change conditions.

 The comparison between the measured and simulated soil organic carbon values by the model shows the potential of the model to provide predictions with acceptable accuracy. The outcome of comparisons revealed that R2, Root Mean Square Error (RMSE), Mean Difference (MD), Mean Absolute Error (MAE) and Model efficiency were 0.97, 2.78, 2.11, 2.33 and 0.70 respectively. Assessment of climate changes in the region (during 1981-2020) showed a decrease in precipitation and a significant increase in temperature over the past 40 years. Climate change simulation was carried out by temperature increasing and decreasing the precipitation until the end of the current century, indicated the decrease of all active carbon pools. It was found that DPM, RPM, BIO, HUM and TOC decreased respectively to 2.41, 2.72, 2.51, 1.04 and 1.32% compared to the current climatic conditions, while the cumulative CO2 emission increased by 1.26%. Temperature rising leads to increase the rate modifying factor (a) by 2.20%, which enhances microbial respiration and decomposition rate of organic carbon and CO2 emissions (carbon output). However, it also increases the ecosystem's net primary productivity (carbon input). Decreases in rainfall and increase in potential evapotranspiration cause a reduction of the rate modifying factor (b) to 0.23%, which on one side reduces the activity of microorganisms and carbon biodegradation; but on the other side, it decreases the vegetation cover and following that reduces CO2 trapping during the photosynthesis process and transfers it to the soil. It seems that in arid and semi-arid climates where the lack of moisture is the most important limiting factor of the plants growth; the role of precipitation in carbon decomposition and sequestration is greater than temperature.

 The Rothamsted carbon model is suitable for regional simulations because it requires only easily obtainable inputs. Therefore RothC is an appropriate tool for estimating long-term effects of climate change and agricultural management (such as application of manures, returning plant residues to the soil, crop rotations, conservation tillage etc.). The RothC model validation in the cold semi-arid agricultural lands of the region, shows the ability of model to properly simulate the pattern of organic carbon changes. Also, simulation of soil organic carbon changes under the climate changes conditions indicates an increase in cumulative CO2 emissions and decrease in soil organic carbon pools of the study area. The methodology can be applied to other regional estimations, provided that the relevant data are available. The predictions allowed to identify the land management potential to carbon sequestration. Such information demonstrate a beneficial tool for evaluation of past land management effects on soil organic carbon trends and also estimation of future climate change effects on soil organic carbon stocks and CO2 emissions.

Soil organic carbon is a key element in determining soil quality, health, and fertility. Due to the complexity of the structure and relationships of soil organic carbon pools, the use of models is beneficial in identifying the reaction of these pools to the change in ecosystem conditions. So, by using the RothC model, the effect of global warming and climate change on the amount of soil organic carbon pool of the agricultural ecosystem of southeastern of Mashhad was investigated. Therefore, the model was calibrated and validated using data measured in 2020 and available long-term data. Comparing the measured values of soil organic carbon and the simulated values by the model, the coefficient of determination (R2) was 0.89. Root means square error (RMSE): 3.45, mean difference (MD): 1.84, mean absolute error (MAE): 2.79, and model efficiency (EF) was 0.73, demonstrating the validity and suitability of the model. The modeling of the future climate changes of Mashhad showed a decrease in rainfall and an increase in temperature and evaporation, leading the amount of total soil organic carbon (TOC) would decrease by 1.13% compared to the current conditions. Considering the decomposition rate constant of the model's four active carbon pools, humus exhibited the slowest decomposition rate of 0.96%. At the same time, decomposable plant materials (DPM), resistant plant materials (RPM), and microbial biomass (BIO) were decreased by 1.18%, 2.21%, and 2.10%, respectively, compared to the current climate condition. Moreover, over time, the decomposition rate decreased due to the decay of active organic matter pools that are easily decomposed.

A national project was carried out in 10 provinces in order to compare the fertilization method of farmers with the integrated method of soil fertility recommendation for canola plants. The experiment included 54 farms in the provinces of Golestan, Mazandaran, Khuzestan (Dezful), Khuzestan (Ahvaz), Ardabil (Maghan), East Azerbaijan, Isfahan, Bushehr, Khorasan Razavi, Kermanshah, Fars and Varamin regions. These areas consisted of a wide range of canola cultivation regions, climatic conditions and a different range of soil characteristics. In a part of selected fields, fertilization was recommended based on the soil test (optimal use of fertilizers) and in the other half of the fields, application was according to the traditional method of the farmer in the region. At the end of the growth stage, the seed yield of treatments was compared in each province and in each region. According to the results, phosphorus had the highest percentage of deficiency in the soil of the fields, and among the micro nutrients, the highest deficiency was related to the zinc element. On average, the increase in grain yield of first year was 600 kg ha-1 or 24%, compared to the conventional treatment. In the second year, the increase in grain yield was 784 kg ha-1 or 27.4%. For the average of two years, the amount of 692 kg ha-1 or 25.7% increase in grain yield was obtained. The benefit to cost ratio of different regions depended on the characteristics of the soil and the amount of used fertilizers. The highest value of this ratio was obtained from the region of Neishabur (26.52) and Darab (22.31. The lowest was obtained from Bushehr (0.28). The results showed that the consumption of fertilizers with considering the time, quantity, source and method of application will improve the yield of canola in all regions.

Soil organic carbon is one of the most important basis for assessing soil quality, health, and fertility. Therefore, accurate, rapid, and inexpensive measurement of soil organic carbon is essential to determine efficient management and conservation practices, which might increase carbon sequestration or preservation in soils. Soil carbon measurement methods are generally classified into two categories: ex-situ and new in situ methods. The first category includes traditional field-collected samples and chemical laboratory degradation of soil organic matter using wet and dry combustion methods. These methods are often expensive, time-consuming, and environmentally destructive. In many cases, common sampling and laboratory errors have resulted in these approaches to be less precise. In situ methods, including infrared reflectance spectroscopy, laser-induced breakdown spectroscopy, inelastic neutron scattering, and remote sensing, are considered more efficient, especially in large-scale national studies as utilizing such methods may increase the speed and accuracy of measuring and significantly reduce the cost of conventional field sampling and laboratory analysis. National quality monitoring of Iranian agricultural soils showed that in more than 60% of lands, soil organic carbon content is less than one percent and further studies have confirmed the declining trend of organic carbon storages in most parts of the country. On one hand, more than 80% of Iran's land area is located in an arid and semi-arid climate whose soils are low in organic carbon content, and on the other hand, the salinity and predominance of calcareous soils in vast areas of Iran have become controversial issues, which have made the attempts to find the correct method of measuring the organic carbon of these soils more complicated. Although the use of an automated carbon analyzer is considered as the standard method for comparing the ex-situ soil carbon measurements, the incomplete conversion of carbonate to CO2 under the combustion conditions in highly calcareous soils might happens which is problematic. Therefore, the use of wet combustion methods is more popular because it’s not affected by the presence of carbonates and the direct calculation of organic carbon. All soil organic carbon measurement methods have strengths and weaknesses, so its choice depends on the characteristics of the studied soils, the purpose of the project as well as the financial considerations, and the allocated budget. Considering the lack of comprehensive studies in this field, the present study aims to help to gain a better understanding of the results of the Walkley Black method, as the basic method of measuring soil organic carbon in Iran, and to assist with developing a standard and robust protocol for measuring the organic carbon of saline and calcareous soils of Iran, and finally to comparatively evaluate the soil carbon measurement methods by outlining their strengths and weaknesses.

Soil quality can be considered as a comprehensive index for sustainable land management assessment. Studying the most important soil physical properties and combining them as an index of soil physical quality (SPQI) could be used as an appropriate criteria for evaluating and monitoring soil physical changes. In this regard, this study was conducted to determine the most important soil physical properties and calculate the SPQI of medium to coarse-textured soils of Khorasan-Razavi province.
Torogh Agricultural and Natural Resources Research and Education Station of Khorasan-Razavi province is located in south-east of Mashhad city (59° 37' 33"-59° 39' 10" E, 36° 12' 31"-36° 13' 56" N). Soil texture variability in this research station is one of its outstanding features. The soil textures are classified into loam, silt loam, silty clay loam, clay loam, and sandy loam. More than 90% of agricultural soils in Khorasan-Razavi province are classified in these five texture classes. Using the available data, 30 points with different soil textures and OC contents were selected. The soil samples were collected from 0-30 cm soil depth at each point. Intact soil cores (5 cm diameter by 5.3 cm length) were used for sandbox measurements, and disturbed soil samples were used to determine other properties. Required laboratory analysis and field measurements were conducted using standard methods. In this research, 35 soil physical properties as total data set (TDS) including: soil moisture release curve (SMRC) parameters, particle size distribution and five size classes of sand particles, soil bulk and particle density, dry aggregates mean weight diameter (MWD) and stability index (SI), S-index, soil porosity and air capacity, location and shape parameters of soil pore size distribution (SPSD) curves, relative field capacity (RFC), plant available water measured in matric pressure heads of 100 and 330 hPa for the field capacity (PAW100 and PAW330), least limiting water range measured in matric pressure heads of 100 and 330 hPa for the field capacity (LLWR100 and LLWR330), integral water capacity (IWC) and integral energy (EI) of different soil water ranges were measured and calculated for 30 soil samples. The most important soil physical properties were selected using principal component analysis (PCA) method by JMP (9.02) software. Selected physical properties as minimum data set (MDS) were weighted and scored using PCA results and scoring functions, respectively. In this study, three types of linear scoring functions were used. The soil physical quality index (SPQI) was calculated by two scoring and two weighting methods for each soil sample and the differences between these four SPQIs were tested by sensitivity index.
Principal component analysis results showed that among 35 soil physical properties (TDS) which were studied at this research, six properties of mean pore diameter (dmean), PAW100, total porosity (PORT), EI LLWR330, SI and PAW330 accounted for about 90% of the variance between soil samples. Weight of the selected properties (MDS) was calculated by the ratio of variation in the data set explained by the PC that contributed the selected property to the total percentage of variation explained by all PCs with eigenvalue ˃ 1. In this research, the parameters of PAW100, total porosity (PORT), SI and PAW330 were scored using scoring function of more is better, EI LLWR330 was scored using scoring function of less is better and dmean was scored using scoring function of optimum by two scoring methods with score ranges of 0.1-1 and 0-1. Considering unweighted and weighted MDS and two ranges of scores, four SPQIs were calculated for each soil sample. The results showed that SPQIs which were calculated by the MDS derived from PCA method and scoring weighted MDS at the range of 0-1, had the highest sensitivity index and could represent the differences between the studied soil samples better than other SPQIs. By this method, maximum and minimum SPQI values for the studied soils were 0.82 and 0.12, respectively. SPQI is a relative comparison criterion to quantify the soil physical quality which could be applied only for the studied soils with specific characteristics.
The results of this research showed that minimum data set (MDS) explained about 90% of the variance between soil samples. Combining MDS into a numerical value called soil physical quality index (SPQI) could be used as a physical comparison criterion for the studied soils. From the SPQI based on the MDS indicator method, soil quality was evaluated quantitatively. Soil samples with grade I, II, III, and IV accounted for 10%, 36.7%, 30%, and 23.3% of the soil samples, respectively.

Nitrogen use efficiency (NUE) is a critically important indicator for judging the wise use of N fertilizer in crops. It can be affected by management factors or soil properties. This study was conducted to estimate NUE in wheat based on large scale fields (20 wheat farms) trials and determining the role of soil factors, in Khorasan Razavi province. Two levels of N as urea (0 and conventional) were applied in each of the fields. In the fields, grain yield (conventional) was divided into low yield population (LYP, GY< 5 Mg ha-1) and high yield population (HYP, GY ≥ 5 Mg ha-1) categories. Then, apparent recovery efficiency (ARE), physiological efficiency (PE), agronomic efficiency (AE) and Partial Factor Productivity (PFP) were calculated for N in both yield populations. The results showed that nitrogen efficiency indices in both yield populations were much lower than optimum level. However, AE (8.2 kg/kg), ARE (25%), and PFP (46 kg /kg) in the HYP were more than in the LYP, but there was no difference between PE in both yield populations. There was a significant correlation between AE and soil organic carbon (r = -0.97, P <0.01,   also ARE with soil organic carbon (r = -0.82, P<0.05) and soil clay content (r = 0.68, P <0.05) for LYP in contrast to the HYP). While PFP exhibited positive correlation with soil organic carbon (r = 0.86, P <0.01) and negative correlation with soil electrical conductivity (r = -0.77, P <0.05) for LYP, this relationship was weaker with soil organic carbon in HYP. None of soil factors had a significant effect on PE. These results show that soil factors such as organic carbon, salinity, and clay content of soil had a decisive role in AE, ARE, and PFP in the LYP. In contrast, soil factors had no effect on nitrogen efficiency indices in the HYP (with the exception of the effect of soil organic carbon on PFP).