The Value of Global Soil Information to the International Plant Nutrition Institute GSP/e-SOTER workshop March 20, 2012, Rome, Italy Terry Roberts, PhD President, IPNI Better Crops, Better Environment through Science
IPNI is a not-for-profit, scientific organization supported by leading fertilizer manufacturers and industry associations Our mission is to develop and promote scientific information about the responsible management of plant nutrition Better Crops, Better Environment through Science
IPNI 30 scientists working 12 Program Areas Eastern Europe and Central Asia Mexico & Central America North America Middle East China Northern Latin America Brazil Africa South Asia SE Asia Latin America Southern Cone Australia/New Zealand
Multiple issues/benefits of soils Degraded soils, soil protection, sustainable soil management impact food security, water resources, biodiversity, carbon sequestration Of most concern to IPNI are those related to soil-plant relationships soil fertility
Soil-plant relationships Soil physical, chemical, and biological characteristics support or limit productivity Soil characteristics in relation to parent material affect soil nutrient supply and other factors impacting crop production Accurate information about the distribution and variability of soil properties, at the proper scale is needed to optimize inputs
IPNI uses global soil information to facilitate the management of plant nutrients To make better general and site-specific nutrient management decisions To identify areas of poor or excessive soil fertility To better understand and manage soil variability To link soil information to decision support tools
Applied soil fertility soil testing, calibration and fertilizer recommendations
Median soil test K levels in 2010.
Percent of samples testing below critical levels for K for major crops in 2010.
Relationship between soil test K and relative soybean yield and soil test K and K 2 O rate (Arkansas) What happens when soil testing and calibration trials are not available?
Supply of nutrients from the soil can estimated using omission plot techniques. Small plots where each of the nutrients being evaluated is omitted, while all the other nutrients are adequately supplied Use the agronomic efficiency (AE) from omission plots to determine fertilizer rates in similar soils AE= Y-Y 0 /F
Example: calculating fertilizer rates from omission plots in winter wheat from India Treatment Yield, kg/ha 1. Ample rates of N, P, and K 5,556 2. N omitted; ample rates of P and K 1,667 N rate= 150 kg/ha AE = (5,556 1,667)/150 = 26 kg of grain/kg of N If the target yield was 4,500 kg/ha: Calculated N rate = (4,500 1,667)/26 = 109 kg/ha IPNI unpublished data, 2011
Site-specific nutrient management (SSNM) Set of nutrient management principles used to make field-specific decisions on N, P, and K management tailored to a specific field or growing environment. It aims to: account for indigenous nutrient sources, including crop residues and manures apply fertilizer at optimum rates and at critical growth stages to meet the deficit between nutrient need and indigenous supply Originally developed for rice in Asia by IRRI and now successfully adapted for maize and wheat
Generic principles of SSNM for calculating fertilizer requirements
Challenge with SSNM Mainly a research tool little adoption by farmers beyond on-farm trials. Farmers rely on extension for nutrient management guidance Extension agents lack confidence in using the methodology and many perceive SSNM as too complicated Decision support tools make the use of SSNM easy e.g. IRRI s Nutrient Manager for Rice Source: http://webapps.irri.org/nm/nmtutorial/
Decision support tools help advisers develop fertilizer recommendations for a specific field or growing environment IPNI has developed simple software based on the principles of SSNM to help advisers develop fertilizer recommendations for a specific field or growing environment Source: http://seap.ipni.net/articles/seap0059-en
Nutrient Expert follows principles of the QUEFTS model in using the relationship between the uptake of nutrients at harvest and grain yield to develop NPK requirements http://seap.ipni.net/articles/seap0059-en
Nutrient Expert for Hybrid Maize: SSNM Rates In the absence of data from nutrient omission plots, NE estimates attainable yield and yield responses to N, P, and K based on climate and soil properties, i.e. soil fertility indicators
NE decision rules for use of proxy information to estimate SSNM parameters SSNM parameter Attainable yield Yield response Proxy information Characteristics of the growing environment (water availability, risk of flood/drought, soil depth, problem soils) Maximum attainable yield (Ymax) based on experts experience or crop modelling Farmers actual yield Soil fertility indicators: soil texture, soil color/om content, historical use of organics, soil test for P/K (if any; not required) Nutrient balance (P and K) from previous crop (affected by nutrient inputs, crop residue management, irrigation water for K)
NE decision rule: estimate attainable yield 1. Determine the maximum attainable yield (Ymax) for the region, province, or domain what can be attained with BMPs, considering climate and soil constraints from local experts experience or crop modeling of yield potential 2. Estimate attainable yield (Ya) for a location depending on the growing environment and the current yield of the farmer (Y) Growing environment: low-risk, medium-risk, and high-risk depending on presence or lack of risks (i.e. drought, flooding, problem soils, etc)
NE decision rule: estimate yield response to fertilizer Assessment of soil fertility class Soil fertility class low medium high Characteristics sandy soil regardless of soil color, or clayey or loamy and reddish/yellowish clayey or loamy and grayish/brownish clayey or loamy and very dark soil with high OM and high fertility
NE decision rule: estimate yield response to fertilizer Observations and assumptions The nutrient-limited yield (i.e. Y0N, Y0P, Y0K) is a fraction of the attainable yield (Ya). For a specific field or location, nutrient-limited yield follows the same trend as Ya, which depends on the climate. Under the same climatic condition, the indigenous nutrient supply (soil fertility) will determine the nutrient-limited yield. The indigenous nutrient supply (INS, IPS, IKS) will determine the ratio of nutrient-limited yield to attainable yield (i.e. Y0N/Ya, Y0P/Ya, Y0K/Ya).
NE decision rule: estimate yield response to fertilizer N response based on INS class NE determines the indigenous N supply class (low, medium, high) from soil characteristics (texture, soil color and/or organic matter content) For a given attainable yield (Ya) and soil fertility class, NE estimates N-limited yield (Y0N) from Y0N/Ya. It assumes that: the median represents soils with average fertility or indigenous N supply (INS) the 25 th percentile represents low INS the 75 th percentile represents high INS N response = Ya * N response factor N response factor = 1 Y0N/Ya (for the INS level) Grain yield without fertilizer N (t/ha) 14 12 10 8 6 4 2 0 China S. Asia 25 th median 75 th L 0 2 4 6 8 10 12 14 Grain yield with NPK (t/ha) Source: IPNI data (unpublished) H M
Spatially variable soil fertility in North Vietnam and its implications for fertilizer needs Soil survey on more than 100,000 ha of degraded soil with intensive rice and maize cultivation in North Vietnam revealed potential large-scale nutrient stresses Area of small farms (0.3 ha) with large field-to-field variability in terms of crops, cropping practices, fertilizer use, and soil fertility status Permanent variability in soil properties in relation to parent material affecting soil nutrient supply and factors relevant to crop production Source: Witt et al. 2007
Objective of survey: Assess spatial variability of soil properties that (i) affect general soil fertility for rice and maize and (ii) soil indigenous supply of nutrients Red River Delta in Vietnam has about 132,000 ha of degraded soils of light or gray color - characterized as low fertility because of parent material and nutrient losses from leaching and intensive cropping Base samples on 1 x 1 km grid (100 ha) with additional transects giving spatial support of 100 x 100 m (1 ha) to model short-distance spatial variability Location of degraded soils (blue) and study sites (red)
Survey results Correlation analysis indicated weak relationships between easily measured soil parameters (e.g. elevation and texture) and those related to nutrient status like organic matter or exchangeable bases mapped soil parameters individually Soil ph in degraded soil of North Vietnam. Interpolation by kriging.
Spatially variable soil fertility in North Vietnam and its implications for fertilizer needs More systematic research on spatial variability is needed in small-scale rice, wheat, and maize in Asia is needed to improve SSNM recommendations. Reliance on old soil maps to help delineation of borderlines for fertilizer recommendations are problematic, because they were not developed for agronomic purposes.
Would global soil information facilitate the transfer nutrient management information from one location in the world to another? Example: IPNI s SE Asia program has successfully developed a system of sustainable agronomic BMPs at commercial oil palm plantations in degraded land environments in South Asia that have increased yields by 25% We are developing an Alliance for Ecological Oil Palm Intensification to try an adapt BMPs developed in Southeast Asia to elsewhere in the world.
Global soil information would aid the transfer of nutrient management information from one location in the world to another. Homologue Evaluation of Environments (climate) Oil Palm in SE-Asia This data is based on climate only, and would benefit from a soils overlay.
Global soil information often outdated and inadequate Better linkage of global soil information to decision support tools Need to extrapolate soil information to: yield gap analysis and target yields: assess nutrient limited yields and develop target from soil information Integrate risk from soil degradation, soil acidity, salinity, nutrient depletion, excessive nutrient loading, etc. Incorporate spatial soil information s impact on nutrients and nutrient supply
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