Site-Specific Nutrient Management: Implementation guidance for policymakers and investors

Site-Specific Nutrient Management (SSNM) provides guidance relevant to the context of farmers' fields. SSNM maintains or enhances crop yields, while providing savings for farmers through more efficient fertilizer use. By minimizing fertilizer overuse, greenhouse gas emissions can be reduced, in some cases up to 50%. SSNM optimizes the supply of soil nutrients over space and time to match crop requirements. SSNM increases crop productivity and improves efficiency of fertilizer use. SSNM mitigates greenhouse gases from agriculture in areas with high nitrogen fertilizer use. Incentives for adoption of SSNM depend strongly on fertilizer prices

Framework for rapid country-level analysis of AFOLU mitigation options

Mitigation in the agricultural sector is critical to meeting the 2 ̊C target set by the Paris Agreement. Recent analysis indicates that land-based mitigation can potentially contribute about 30% of the reduction is needed to reach the 2030 target. However, action to reduce emissions from the agricultural sector has lagged behind other sectors. Action and investment in agriculture have been constrained by a lack of policy-relevant and science-based methods estimating GHG emissions and mitigation potential that contribute to decision making. In this paper, we present a framework for a rapid country-level scientific assessment of emissions and mitigation potential from the agricultural, forestry and other land-use (AFOLU) sector. The framework sets targets for AFOLU mitigation based on local agro- environmental conditions, mitigation options best fitted for those conditions and stakeholder input. It relies on the use of simple models or tools to estimate emissions at the farm gate using a mix of Tier 1, Tier 2 and simple Tier 3 methods under baseline, business-as-usual (BAU) and mitigation scenarios. The mitigation potential of low-emissions agriculture options is determined relative to a baseline or BAU scenario. The framework also enables examining the likely level of implementation of low-emission options. This includes assessing the cost and additional benefits of applying the identified low- emission options across different jurisdictions of interest. The feasibility of these options, assessment of institutional capacity for scaling and identification of barriers and risks of adoption to identify priorities are also determined. This information is used by stakeholders and experts to develop a road map for implementation. Rapid assessment of national mitigation potentials can help countries to assess their Nationally Determined Contributions’ (NDC) targets and prioritize mitigation options for achieving the targets and monitor progress towards their achievement. Spatially explicit information helps countries plan implementation at subnational levels

Sixty years of irrigated wheat yield increase in the Yaqui Valley of Mexico: Past drivers, prospects and sustainability

Continued global wheat yield increase (about 1.3% p.a. for 2000–2019) remains an essential condition for greater world food security. Relevant to this challenge is the rise in average farm yield (FY) of irrigated spring wheat in the Yaqui Valley of northwest Mexico from 2 to 7 t/ha between 1960 and 2019. Since the early 1950s the region has been the prime target of wheat research by the International Maize and Wheat Improvement Centre (CIMMYT) and its predecessors, research still having significant impact on wheat in the developing world, a grouping that today delivers more than half the world's wheat. FY increase was investigated in detail by dividing the interval into three 20-year periods, correcting FY for the strong influence of inter-annual variation in January to March minimum temperature (Tmin J-M, warming lowering yield around 7%/°C) and measuring the remaining linear increase in FY (Fischer et al., 2022). Total yield increase, corrected for Tmin J-M and CO2 rise, relative to average yield in each period, was 4.17%, 0.47%, and 1.59% p.a. for 1960–79, 1980–99, and 2000–19, respectively. The breeding component, estimated by the increase in the Varietal Yield Index in farmers’ fields, rose at 0.97%, 0.49%, and 0.71% p.a., respectively. The remaining yield change (3.16, −0.02% and 0.87% p.a., respectively) comprised the net effect of improved crop management (agronomic progress) plus that of off-farm changes, together here called agronomy+. Major changes in agronomy included: a large increase in fertiliser N use, benefitting early on from a large positive variety × N interaction; in the second period a switch to planting on raised beds and a decline in rotational diversity; and in the final period, consolidation of operational crop units and probably more skilful and timely management. Off-farm developments saw strong government financial support in the first period, but in the second period breakdown of the traditional small holder land system and withdrawal of government support. The last period saw better prices and improved access to technical advice. Breeding progress is expected to continue in the Yaqui Valley but at a slowly diminishing rate (currently 0.66% p.a.), while progress from new agronomy appears limited. Although FY gaps are small, some gap closing remains possible, and 1.2% p.a. FY progress is estimated for the next 20 years in the absence of new technologies. World wheat food security without area increase will increasingly depend on developing countries where yield gaps are generally wider and gap closing prospects better. Biophysical sustainability of the Yaqui Valley wheat system is moderately good but N management and diversity can be improved

Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier

Sen2-Agri is a software system that was developed to facilitate the use of multi-temporal satellite data for crop classification with a random forest (RF) classifier in an operational setting. It automatically ingests and processes Sentinel-2 and LandSat 8 images. Our goal was to provide practitioners with recommendations for the best sample size and composition. The study area was located in the Yaqui Valley in Mexico. Using polygons of more than 6000 labeled crop fields, we prepared data sets for training, in which the nine crops had an equal or proportional representation, called Equal or Ratio, respectively. Increasing the size of the training set improved the overall accuracy (OA). Gains became marginal once the total number of fields approximated 500 or 40 to 45 fields per crop type. Equal achieved slightly higher OAs than Ratio for a given number of fields. However, recall and F-scores of the individual crops tended to be higher for Ratio than for Equal. The high number of wheat fields in the Ratio scenarios, ranging from 275 to 2128, produced a more accurate classification of wheat than the maximal 80 fields of Equal. This resulted in a higher recall for wheat in the Ratio than in the Equal scenarios, which in turn limited the errors of commission of the non-wheat crops. Thus, a proportional representation of the crops in the training data is preferable and yields better accuracies, even for the minority crops

Nitrogen rich strips

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Climate change and food security in the developing world: potential of maize and wheat research to expand options for adaptation and mitigation

Maize and wheat are two of the most important food crops worldwide. Together with rice, they provide 30% of the food calories to 4.5 billion people in almost 100 developing countries. Predictions suggest that climate change will reduce maize production globally by 3 to 10% by 2050 and wheat production in developing countries by 29 to 34%. This will coincide with a substantial increase in demand for maize and wheat due to rising populations. Maize and wheat research has a crucial role to play in enhancing adaptation to and mitigation of climate change while also enhancing food security. Crop varieties with increased tolerance to heat and drought stress and resistance to pests and diseases are critical for managing current climatic variability and for adaptation to progressive climate change. Furthermore, sustainable agronomic and resource management practices, such as conservation agriculture and improved nitrogen management can contribute to climate change mitigation. There is also a need for better policies and investments in infrastructure to facilitate technology adoption and adaptation. These include investments in irrigation, roads, storage facilities and improved access to markets. There is also a need for policy innovations for stabilizing prices, diversifying incomes, increasing farmer access to improved seeds and finance, and providing safety nets to enhance farmers' livelihood security. This review paper details the potential impacts of climate change on food security, and the key role of improved technologies and policy and institutional innovations for climate change adaptation and mitigation. The focus is on maize and wheat in sub-Saharan Africa and South Asia

Rapid analysis of country-level mitigation potential from agriculture, forestry and other land uses in Mexico

- Total mitigation potential from the AFOLU sector was the highest in Chiapas (~13 Mt CO2eq) followed by Campeche (~ 8 Mt CO2eq). - 11 states (i.e. Oaxaca, Quintana Roo, Yucatan, Jalisco, Sonora, Veracruz, Durango, Chihuahua, Puebla, Michoacán and Guerrero) had a total AFOLU mitigation potential between 2.5 to 6.5 Mt CO2eq, other states had AFOLU mitigation potentials of less than 2 Mt CO2eq. - Crop mitigation potential was the highest in Veracruz, Jalisco and Michoacán; it was intermediate (between 0.4 to 0.6 Mt CO2eq) in the states of Chiapas, Sinaloa, Guanajuato, Mexico and Guerrero. Other states had crop mitigation potential less than 0.4 Mt CO2eq. - Livestock mitigation potential was the highest in Jalisco and Sonora and intermediate (between 0.4 to 0.8 Mt CO2eq) in the states of Puebla, Veracruz, Guanajuato, and Yucatan. Other states had livestock mitigation potentials of less than 0.3 Mt CO2eq. - The state-wide and total magnitude of mitigation was the highest from the FOLU sector. Per unit abatement, cost was also the highest in this sector. - If properly implemented, mitigation potentials on cropland can be realized with net benefits, compared to livestock and FOLU options, which involve net costs

Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data

Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation. Results: We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500–690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, − 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction. Conclusions: The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative

Quantification of economically feasible mitigation potential from agriculture, forestry and other land uses in Mexico

Countries often lack methods for rapidly, but robustly determining greenhouse gas (GHG) mitigation actions and their impacts comprehensively in the land use sector to support commitments to the Paris Agreement. We present rapid assessment methods based on easily available spatial data and adoption costs for mitigation related to crops, livestock and forestry to identify priority locations and actions. Applying the methods for the case of Mexico, we found a national mitigation potential of 87.88 million tons (Mt) CO2eq yr−1, comprising 7.91, 7.66 and 72.31 Mt CO2eq yr−1 from crops, livestock and forestry/agro-forestry, respectively. At the state level, mitigation potentials were highest in Chiapas (13 Mt CO2eq) followed by Campeche (8 Mt CO2eq). Eleven states had a land use mitigation potential between 2.5 to 6.5 Mt CO2eq, while other states had mitigation potentials of less than 2 Mt CO2eq. Mitigation options for crops and livestock could reduce 60% and 6% of the respective emissions. Mitigation options for forestry could reduce emissions by half. If properly implemented, mitigation potentials on cropland can be realized with net benefits, compared to livestock and forestry options, which involve net costs. The method supports science-based priority setting of mitigation actions by location and subsector and should help inform future policy and implementation of countries’ nationally determined contributions

Genetic mitigation strategies to tackle agricultural GHG emissions: The case for biological nitrification inhibition technology

Accelerated soil-nitrifier activity and rapid nitrification are the cause of declining nitrogen-use efficiency (NUE) and enhanced nitrous oxide (N2O) emissions from farming. Biological nitrification inhibition (BNI) is the ability of certain plant roots to suppress soil-nitrifier activity through production and release of nitrification inhibitors. The power of phytochemicals with BNI-function needs to be harnessed to control soil-nitrifier activity and improve nitrogen-cycling in agricultural systems. Transformative biological technologies designed for genetic mitigation are needed so that BNIenabled crop-livestock and cropping systems can rein in soil-nitrifier activity to help reduce greenhouse gas (GHG) emissions and globally make farming nitrogen efficient and less harmful to environment. This will reinforce the adaptation or mitigation impact of other climate-smart agriculture technologies