Harold M. van Es

Farmer-oriented assessment of soil quality using field, laboratory, and VNIR spectroscopy methods

Soil quality and health are terms describing similar concepts, but the latter appeals to farmers and crop consultants as part of a holistic approach to soil management. We regard soil health as the integration and optimization of the physical, biological and chemical aspects of soils for improved productivity in an economic and sustainable manner. This paper describes the process used for the selection of soil quality/health indicators that comprise the new Cornell Soil Health Test. Over 1,500 samples collected from controlled research experiments and commercial farms were initially analyzed for 39 potential soil quality indicators. Four physical and four biological indicators were selected based on sensitivity to management, relevance to functional soil processes, ease and cost of sampling, and cost of analysis. Seven chemical indicators were also selected as they are part of the standard soil nutrient test. Soil health test reports were developed to allow for an overall assessment, as well as the identification of specific soil constraints. The new soil health test is being offered on a for-fee basis starting in 2007. In addition, visible near infrared reflectance spectroscopy was evaluated as a possible tool for low-cost soil health assessment. From preliminary analyses, the methodology shows promise for some but not all of the soil quality indicators. In conclusion, an inexpensive soil health test was developed for integrative assessment of the physical, biological, and chemical aspects of soils, thereby facilitating better soil management.

EVALUATION OF LABORATORY-MEASURED SOIL PROPERTIES AS INDICATORS OF SOIL PHYSICAL QUALITY

Routine soil analyses provide an approach for assessment and monitoring of soil quality and targeted implementation of management practices, but suitable indicators are mostly undefined. We used three long-term experiments on several soil types where maize (Zea mays L.) was grown under different tillage (no till and plow till), rotation (continuous maize and maize after grass), and harvesting (silage and grain) methods to identify suitable indicators for evaluating soil physical quality. Disturbed and undisturbed soil samples were collected, and laboratory-based analyses were performed for water-stable aggregation, saturated hydraulic conductivity, several pore size parameters, penetration resistance at &PSgr; = −10 MPa, and bulk density. Sensitivity to management, expense of measurement, measurement consistency, and relevance to critical physical soil processes were used as criteria to evaluate indicator suitability. Indicators varied significantly seasonally and by soil type, and several showed significant differences and trends between management treatments. Small water-stable aggregates (0.25-2 mm) showed the most consistent and significant treatment differences. Bulk density, available water capacity, and air-filled pores at field capacity (PO > 30) were also related to treatment effects and had low variability. Penetration resistance and effective porosity (PO > 0.2) were not sensitive to management practices, whereas aeration pores and saturated hydraulic conductivity were too variable to use as indicators. Several indicators measured on undisturbed cores may be predicted from those measured from disturbed samples using pedotransfer functions. Small water-stable aggregates (0.25-2 mm), available water capacity, bulk density, and PO > 30 appear most promising as indicators for routine evaluation and monitoring of soil physical quality.

Evaluation of the PNM model for simulating drain flow nitrate-N concentration under manure-fertilized maize

Mathematical models may be used to develop management strategies that optimize the use of nutrients from complex sources such as manure in agriculture. The Precision Nitrogen Management (PNM) model is based on the LEACHN model and a maize N uptake/growth and yield model and focuses on developing more precise N management recommendations. The PNM model was evaluated for simulating drain flow nitrate-nitrogen (NO3-N) in a 3-yr study involving different times of liquid manure application on two soil textural extremes, a clay loam and a loamy sand under maize (Zea mays, L.) production. The model was calibrated for major N transformation rate constants including mineralization, nitrification and denitrification, and its performance was tested using two different calibration scenarios with increasing levels of generalization: (i) separate sets of rate constants for each individual soil type and (ii) a single set of rate constants for both soil types. When calibrated for each manure application treatment for each soil type, the model provided good simulations of monthly and seasonal drain flow NO3-N concentrations. The correlation coefficient (r) and Willmott’s index of agreement (d) ranged from 0.63 to 0.96 and 0.72 to 0.92, respectively. The calibrated model performed reasonably well when rate constant values averaged over manure application treatment for each soil type were used, with r and d values between 0.54 and 0.97, and 0.70 and 0.94, respectively, and greater accuracy for the clay loam soil. When rate constant values were averaged over manure application treatments and soil types, model performance was reasonably accurate for the fall time manure application on the clay loam (r and d of 0.60 and 0.91 and 0.72 and 0.92, respectively) and satisfactory for the spring time on the clay loam and the fall and spring times for the loamy sand soil (r and d between 0.56 and 0.90 and 0.58 and 0.84, respectively). The use of the model for predicting N dynamics under manure-fertilized maize cropping appears promising.

Evaluation of temporal, spatial, and tillage-induced variability for parameterization of soil infiltration

Model simulation of water and chemical transport requires information on soil hydraulic properties. Recently, independent parameterization methods have been developed to characterize soil type, tillage, temporal and spatial effects of soils. This study determines the relative magnitude of tillage-induced, temporal (yearly and seasonal), and spatial (within fields and between rows) variability in a combined analysis of soil infiltration in an agricultural field and evaluates the appropriateness of various parameterization scenarios. Infiltration measurements were obtained in the row and interrow position under plow-tilled and ridge-tilled corn (Zea mays L.) in four replicates on multiple dates in a wetter (1990) and dryer year (1991). Measurements exhibited significant temporal variability within a growing season, especially in a dry year under plow till when soil cracking resulted in higher infiltration. Position with respect to the row was the most significant source of variability under ridge till, but not under plow till. Row and interrow differences in a ridge-tilled soil are the result of dense soil and lack of disturbance in the interrow. Yearly variations and field variability were relatively low. Differences between tillage practices were primarily expressed in variable susceptibility to spatial and temporal variation. Adequate parameterization of soil infiltration on agricultural fields requires recognition of various sources of variability under different tillage management systems, weather and climatic conditions, and soil types. High intrinsic variability of soil infiltration must be accounted for through increased sampling (e.g. duplicate measurements) and the use of stochastic methods.

Losses of ammonia and nitrate from agriculture and their effect on nitrogen recovery in the European Union and the United States between 1900 and 2050

Historical trends and levels of nitrogen (N) budgets and emissions to air and water in the European Union and the United States are markedly different. Agro-environmental policy approaches also differ, with emphasis on voluntary or incentive-based schemes in the United States versus a more regulatory approach in the European Union. This paper explores the implications of these differences for attaining long-term policy targets for air and water quality. Nutrient surplus problems were more severe in the European Union than in the United States during the 1970s and 1980s. The EU Nitrates and National Emission Ceilings directives contributed to decreases in fertilizer use, N surplus, and ammonia (NH) emissions, whereas in the United States they stabilized, although NH emissions are still increasing. These differences were analyzed using statistical data for 1900-2005 and the global IMAGE model. IMAGE could reproduce NH emissions and soil N surpluses at different scales (European Union and United States, country and state) and N loads in the Rhine and Mississippi. The regulation-driven changes during the past 25 yr in the European Union have reduced public concerns and have brought agricultural N loads to the aquatic environment closer to US levels. Despite differences in agro-environmental policies and agricultural structure (more N-fixing soybean and more spatially separated feed and livestock production in the United States than in the European Union), current N use efficiency in US and EU crop production is similar. IMAGE projections for the IAASTD-baseline scenario indicate that N loading to the environment in 2050 will be similar to current levels. In the United States, environmental N loads will remain substantially smaller than in the European Union, whereas agricultural production in 2050 in the United States will increase by 30% relative to 2005, as compared with an increase of 8% in the European Union. However, in the United States, even rigorous mitigation with maximum recycling of manure N and a 25% reduction in fertilizer use will not achieve the policy target to halve the N export to the Gulf of Mexico.

The Use of Hyperspectral Visible and Near Infrared Reflectance Spectroscopy for the Characterization of Salt-Affected Soils in the Harran Plain, Turkey

The quality of lands may be degraded by the accumulation of salts in soils, which is typically measured as soil Electrical Conductivity (ECe). High-salinity soils developed in low elevation spots in the Harran Plain after the initiation of intensive irrigation and crop production on clayey soils under high evaporation. This study evaluated the feasibility of using hyperspectral Visible and Near Infrared Reflectance Spectroscopy (VNIRRS) as a potentially more cost-effective approach for the characterization of soil salinity. 150 locations were taken at 0–15 and 15–30 cm depths from an area of 1000 ha with salinity levels ranging from none to very high. Sieved soils were measured for ECe using saturation paste and also scanned by VNIRRS in both air dried and oven dry states. For spectral preprocessing, raw reflectance spectra were averaged over 10 nm and a continuum removal (CR) method was applied. Calibration models between spectra and ECe were based on Multiple Adaptive Regression Splines (MARS), Partial Least Square Regression (PLSR), and Classification and Regression Trees (CART, for groupings). The VNIRRS data were also combined with topographical parameters from digital elevation models to improve estimations. Results showed that the estimation quality of ECe varied depending on approaches used, with the best results using continuum removed spectra of oven dried samples using MARS after separating samples containing high amounts of gypsum (R2 = 0.86, RPD = 2.70). Topographical variables with VNIRRS data improved estimations up to 12%. CART analysis showed that soils could be categorized as saline and non-saline based on soil reflectance with 65% accuracy.

The Challenge of Generating Spatially Balanced Scientific Experiment Designs

The development of the theory and construction of combinatorial designs originated with the work of Euler on Latin squares. A Latin square on n symbols is an n × n matrix (n is the order of the Latin square), in which each symbol occurs precisely once in each row and in each column. Several interesting research questions posed by Euler with respect to Latin squares, namely regarding orthogonality properties, were only solved in 1959 [3]. Many other questions concerning Latin squares constructions still remain open today.

Soil Test, Aerial Image and Yield Data as Inputs for Site-specific Fertility and Hybrid Management Under Maize

Several potential sources of information exist to support precision management of crop inputs. This study evaluated soil test data, bare-soil remote sensing imagery and yield monitor information for their potential contributions to precision management of maize (Zea mays L.). Data were collected from five farmer-managed fields in Central New York in 1999, 2000, and 2001. Geostatistical techniques were used to analyze the spatial structure of soil fertility (pH, P, K, NO3 and organic matter content) and yield variables (yield, hybrid response and N fertilization response), while remote sensing imagery was processed using principal component analysis. Geographic information system (GIS) spatial data processing and correlation analyses were used to evaluate relationships in the data. Organic matter content, pH, P, and K were highly consistent over time and showed high to moderate levels of spatial autocorrelation, suggesting that grid soil sampling at 2.5–5.5 ha scale may be used as a basis for defining fertility management zones. Soil nitrate levels were strongly influenced by seasonal weather conditions and showed low potential for site-specific N management. Aerial image data were correlated to soil organic matter content and in some cases to yield, mainly through the effect of drainage patterns. Aerial image data were not well correlated with soil fertility indicators, and therefore were not useful for defining fertility management zones. Yield response to hybrid selection and nitrogen fertilization rates were highly variable among years, and showed little justification for site-specific management. In conclusion, we recommend grid-based management of lime, P, and K, but no justification existed within our limited study area for site-specific N or hybrid management.

Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach

Large temporal and spatial variability in soil nitrogen (N) availability leads many farmers across the United States to over-apply N fertilizers in maize ( L.) production environments, often resulting in large environmental N losses. Static Stanford-type N recommendation tools are typically promoted in the United States, but new dynamic model-based decision tools allow for highly adaptive N recommendations that account for specific production environments and conditions. This study compares the Corn N Calculator (CNC), a static N recommendation tool for New York, to Adapt-N, a dynamic simulation tool that combines soil, crop, and management information with real-time weather data to estimate optimum N application rates for maize. The efficiency of the two tools in predicting the Economically Optimum N Rate (EONR) is compared using field data from 14 multiple N-rate trials conducted in New York during the years 2011 through 2015. The CNC tool was used with both realistic grower-estimated potential yields and those extracted from the CNC default database, which were found to be unrealistically low when compared with field data. By accounting for weather and site-specific conditions, the Adapt-N tool was found to increase the farmer profits and significantly improve the prediction of the EONR (RMSE = 34 kg ha). Furthermore, using a dynamic instead of a static approach led to reduced N application rates, which in turn resulted in substantially lower simulated environmental N losses. This study shows that better N management through a dynamic decision tool such as Adapt-N can help reduce environmental impacts while sustaining farm economic viability.

Influence of Residue Management and Tillage Systems on Carbon Sequestration and Nitrogen, Phosphorus, and Potassium Dynamics of Soil and Plant and Wheat Production in Semi-arid Region

Reducing the tillage and application of mulch are important strategies for soil and water conservation and sustainability of agricultural systems. Soil can be a source or sink for carbon (C) depending on management strategies and plays a major role in the global C cycle. These interacting practices can alter nutrient movement and availability to the crops, reduce water loss, slow down organic-matter (OM) decomposition, and thus enhance C sequestration. A 2-year field study was conducted to quantify the tillage and mulching effect on soil organic C (SOC), OM, nitrogen (N), phosphorus (P), and potassium (K) at two depths (i.e., 0–15 and 15–30 cm deep) in the soil profile and N, P, and K concentrations (g kg−1) in plant shoots at harvest on a Typic Calciargids in wheat–maize rotation. The four tillage systems used were zero tillage (ZT), minimum tillage (MT), conventional tillage (CT), and deep tillage (DT), and four mulch rates [control, 2 (M2), 4 (M4), and 6 (M6) Mg ha−1 year−1 wheat (Triticum aestivum L.) straw] were applied in combination with each tillage system, keeping recommended rates of fertilizers. There was a linear positive response of mulch application on SOC for both years, but it was more pronounced during the second year. Greater values were found in ZT and the lowest in CT at all depths, although greater SOC content was found in upper layers than in deeper ones. Greater shoot N, P, and K concentrations were found in MT, CT, and DT, whereas the lowest concentration was found in ZT. Mulch application has no effect on N, P, and K concentrations in shoots. The soil N concentration was not affected by tillage and mulch, yet greater soil N content was found at 0–15 cm than 15–30 cm deep. There was significant effect of tillage on soil P and K during one year as greater P and K concentrations were found under MT, CT, and DT compared to ZT. More N, P, K, and OM concentrations were found at 0–15 cm deep than at 15–30 cm deep during the whole study period. Mulch effect was significant on K, and significantly greater amounts were found at greater levels of mulch application. The increases in the soil OM were 34.5, 35.75, and 24% at 0–8, 8–16, and 16–24 cm deep respectively from the first year to the second year. Tillage effect on soil organic-matter content was not significant. Tillage increased grain production for both years. For the first year, 22.9 and 27% greater yields were found in CT and DT, whereas in the second year yields were 10.6, 17.9, and 57% greater, respectively, in MT, CT, and DT as compared to ZT. Grain production was increased at a result of mulch application by 12.9, 20.3, and 10.6% during the first year and 11.45, 23.74, and 10.9% during the second year as compared to control (i.e., without mulch). Results show the importance of mulch application and crop residue retention. Both can increase the SOC content and water-holding capacity, which will result in improved production and soil physical health over long and continuous use of mulch.