Jawoo Koo

Can agriculture support climate change adaptation, greenhouse gas mitigation and rural livelihoods? insights from Kenya

Changes in the agriculture sector are essential to mitigate and adapt to climate change, meet growing food demands, and improve the livelihoods of poor smallholder producers. What agricultural strategies are needed to meet these challenges? To what extent are there synergies among these strategies? This paper examines these issues for smallholder producers in Kenya across several agroecological zones. Several practices emerge as triple wins, supporting climate adaptation, greenhouse gas mitigation, and profitability goals. In particular, integrated soil fertility management and improved livestock feeding are shown to provide multiple benefits across all agroecological zones examined. Triple wins of other agricultural practices are limited to specific agroecological zones. Irrigation and soil and water conservation, for example, are essential for adaptation, mitigation, and profitability in arid areas. The results suggest that agricultural investments targeted toward these triple-win strategies will have the greatest payoff in terms of increased resilience of farm and pastoralist households and global climate change mitigation. To reap the benefits of triple-win strategies will require that policymakers, researchers, and practitioners move away from isolated approaches focused on either adaptation or mitigation or rural income generation toward a more holistic assessment of joint strategies as well as their tradeoffs and synergies.

Reanalysis of a global soil database for crop and environmental modeling

There is an increased need for detailed soil information that can be used for applications of crop and environmental modeling. The goal of this project was to conduct a reanalysis of the ISRIC-WISE 1.1 Soil Profile Dataset. As part of the procedures, the soil reanalysis database was fitted to the standard formats of the International Consortium for Agricultural Systems Application (ICASA). Thus, the soil reanalysis database tailors dynamic crop models such as the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). During the reanalysis, the physical and chemical parameters of the soil profiles were revised and estimated, where necessary and possible, using pre-established ranges given by the literature and correlations among other more stable variable. To evaluate each of the 3404 reanalyzed soil profiles, the CSM-CERES-Maize model was run for a standard crop management scenario using both the original and the new improved soil databases. Nine hundred seventy-eight soil profiles were considered to be not useful during the reanalysis due to missing values for one or more critical variables and were, therefore, not considered for quality control procedures. A pre-diagnostic for only nitrogen and soil organic carbon in the original dataset showed 70% and 5% of missing values respectively. A sensitivity analysis based on crop simulations comparing the original and the reanalyzed soil databases, showed that 1294 soil profiles yielded different results due to improvement of either the original data or improved conversion procedures. The details and considerations for detecting missing and erroneous values and for estimating soil variable values are presented in this paper for further use. The final soil reanalysis global database contains 3404 soil profiles and is available at https://harvestchoice.wufoo.com/forms/download-wisol.

Global Cost of Land Degradation

Land degradation—defined by the Millennium Ecosystem Assessment report as the long-term loss of ecosystems services—is a global problem, negatively affecting the livelihoods and food security of billions of people. Intensifying efforts, mobilizing more investments and strengthening the policy commitment for addressing land degradation at the global level needs to be supported by a careful evaluation of the costs and benefits of action versus costs of inaction against land degradation. Consistent with the definition of land degradation, we adopt the Total Economic Value (TEV) approach to determine the costs of land degradation and use remote sensing data and global statistical databases in our analysis. The results show that the annual costs of land degradation due to land use and land cover change (LUCC) are about US

Potential impact of climate change on cereal crop yield in West Africa

231 billion per year or about 0.41 % of the global GDP of US

Estimating soil carbon levels using an ensemble Kalman filter

56.49 trillion in 2007. Contrary to past global land degradation assessment studies, land degradation is severe in both tropical and temperate countries. However, the losses from LUCC are especially high in Sub-Saharan Africa, which accounts for 26 % of the total global costs of land degradation due to LUCC. However, the local tangible losses (mainly provisioning services) account only for 46 % of the total cost of land degradation and the rest of the cost is due to the losses of ecosystem services (ES) accruable largely to beneficiaries other than the local land users. These external ES losses include carbon sequestration, biodiversity, genetic information and cultural services. This implies that the global community bears the largest cost of land degradation, which suggests that efforts to address land degradation should be done bearing in mind that the global community, as a whole, incurs larger losses than the local communities experiencing land degradation. The cost of soil fertility mining due to using land degrading management practices on maize, rice and wheat is estimated to be about US

A SIMPLE SOIL ORGANIC‐MATTER MODEL FOR BIOMASS DATA ASSIMILATION IN COMMUNITY‐LEVEL CARBON CONTRACTS

15 billion per year or 0.07 % of the global GDP. Though these results are based on a crop simulation approach that underestimates the impact of land degradation and covers only three crops, they reveal the high cost of land degradation for the production of the major food crops of the world. Our simulations also show that returns to investment in action against land degradation are twice larger than the cost of inaction in the first six years alone. Moreover, when one takes a 30-year planning horizon, the returns are five dollars per each dollar invested in action against land degradation. The opportunity cost accounts for the largest share of the cost of action against land degradation. This explains why land users, often basing their decisions in very short-time horizons, could degrade their lands even when they are aware of bigger longer-term losses that are incurred in the process.

Estimating Soil Carbon in Agricultural Systems Using Ensemble Kalman Filter and DSSAT-CENTURY

Resilience of crops to climate change is extremely critical for global food security in coming decades. Decrease in productivity of certain crops as a consequence of changing climate has already been observed. In West Africa, a region extremely vulnerable to climate change, various studies predicted significant reduction in productivity of the major crops because of future warming and shift in precipitation patterns. However, most studies either follow statistical approaches or involve only specific sites. Here, using a process-based crop model at a regional scale, we project the future changes in cereal crop yields as a result of climate change for West African countries in the absence of agricultural intensification for climate adaptation. Without adaptation, the long-term mean of crop yield is projected to decrease in most of the countries (despite some projected increase of precipitation) by the middle of the century, while the inter-annual variability of yield increases significantly. This increase of yield variability is attributed to an increase of inter-annual variability of growing season temperature and/or precipitation in future climate scenarios. The lower mean yield and larger year-to-year variation together make the regional food security extremely volatile. For a comprehensive understanding of climate change impact on crop yield, the distribution of temperature and precipitation over specific growth stages, in addition to growing season averages, should be accounted for. Although uncertainties are rife in calibrating and running a process-based crop model at regional scale, the present study offers insight into potential vulnerabilities of the agricultural system in specific countries or West Africa as a whole because of regional climate change.

RiceAtlas, a spatial database of global rice calendars and production

Soils have been proposed as carbon storage sinks to help reduce atmospheric carbon dioxide levels and global warming. Benefits could accrue to farmers, due to beneficial effects of soil organic carbon on productivity, and to society by managing land to increase soil carbon. Measurements are needed to determine if practices aimed at increasing carbon levels are effective and to quantify amounts of carbon stored for verification purposes. However, measurements are expensive and have high errors relative to annual changes in soil carbon. In this article, we develop an Ensemble Kalman filter (EnKF) approach that combines measurements with predictions from a simple model, taking into account errors in measurements, model parameters, and model predictions. The EnKF was used to estimate soil carbon at annual time steps and to estimate an uncertain soil carbon decomposition rate parameter. A sensitivity analysis was conducted to evaluate the effects on EnKF estimates of uncertainties in measurements, model predictions, and the decomposition rate parameter. The EnKF estimates of soil carbon were compared with true values that were generated using a Monte Carlo method. Results showed that EnKF estimates of soil carbon levels and annual changes of soil carbon were more accurate than measurements alone for all combinations of conditions studied. The root mean square error of estimation was reduced from around 700 kg/ha based on measurements alone to about 225 kg/ha using the EnKF procedure. The unknown soil carbon decomposition parameter converged to its true value after about seven years. This EnKF method can be modified to incorporate more comprehensive models of cropping systems and soil carbon, to incorporate spatial variability, and to assimilate remote sensing inputs. It is simple to implement and has considerable promise for practical use in soil carbon sequestration projects.

Perspectives on Climate Effects on Agriculture: The International Efforts of AgMIP in Sub-Saharan Africa

Soil carbon (C) sequestration has been proposed as a transitional win-win strategy to help replenish organic-matter content in depleted agricultural soils and counter increases in atmospheric greenhouse gases. Data assimilation and remote sensing can reduce uncertainty in sequestered C mass estimates, but simple soil organic carbon (SOC) models are required to make operational predictions of tradeable amounts over large, heterogenous areas. Our study compared the performance of RothC26.3 and a reduced compartmental model on an 11-year fertilizer trial in subhumid West Africa. Root mean square error (RMSE) differences of 0.05 Mg C/ha between models on total SOC predictions suggest that for contractual purposes, SOC dynamics can be simulated by a two-pool structure with labile and stable components. Faster (seasonal) and slower (semicentennial and beyond) rates can be approximated by constants as instantaneous and infinite decay. In these systems, simulations indicate that cereal residue incorporation holds most potential for mitigation of transient C loss associated with recent land conversion to agriculture.

Combining Model Estimates and Measurements Through an Ensemble Kalman Filter to Estimate Carbon Sequestration

Among various ways to sequester CO2 from the atmosphere, increasing soil carbon is an option that could also lead to increased agricultural productivity, especially in developing countries. In order to accept this option as a mechanism for reducing atmospheric CO2 levels, a reliable soil carbon accounting system needs to be developed. Soil carbon can be estimated from direct measurements, but measurements often have higher variability than annual soil carbon changes due to the uncertain nature of in-situ sampling methods and laboratory analysis techniques. To reduce such variability in soil carbon estimates, the estimation accuracy of an approach that assimilates measurements with simulated outputs from a complex crop model was studied. The DSSAT-CENTURY model and the ensemble Kalman filtering (EnKF) method were used to test if this approach can provide more reliable estimates of SOC over time than measurements alone. The EnKF was developed to estimate soil carbon, crop biomass, and a model parameter that specifies the rate of soil organic matter decomposition. A base-case simulation study was set up with a continuous maize farming system in Ghana for 20 years. Based on an identical twin test that used model-generated truth and measurement datasets, the EnKF estimates of soil carbon showed superior estimation accuracy to measurements with less uncertainty. In time, the uncertainty in soil carbon estimates based on annual sampling was reduced by about 60% using the EnKF method. The method was not effective in estimating the model carbon decomposition rate parameter, mainly because of the low correlations of that parameter with either soil carbon or crop biomass. However, even when uncertainties in measurements and model predictions were relatively high, estimates of soil carbon converged to the true value, although uncertainties in those estimates varied with measurement and model uncertainties. We conclude that the use of the EnKF method with the complex DSSAT-CENTURY model would provide more reliable soil monitoring system, but more research is needed to compare complex versus simple models in this approach.